enterprise devops

How Does Your DevOps Measurement of Success Stack Up?

CloudBees teamed up with IDG to conduct a DevOps measurement survey to investigate how well organizations think they are doing with their DevOps journey. We spoke to 100 respondents consisting of IT executives, IT operations, engineering and shared services who are all involved with DevOps adoption in their organizations. These organizations represent a cross-section of small, medium and large businesses. The majority of our survey takers work in the technology, manufacturing, and financial services vertical industries throughout North America.

Our initial findings include:

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A DevOps on AWS Enterprise Transformation

In helping enterprises transform their engineering organizations and apply DevOps practices on AWS, we are often introduced to all types of legacy systems and processes.

Let’s imagine an enterprise that might have hundreds of brownfield applications and services and they want to move some of these applications to AWS. Let’s also assume they don’t see AWS as another data center with APIs but more as a platform on which to transform their applications and services. That said, they have an existing group of engineers who are excited but not yet ready for the shift of moving all of their applications to leverage a Serverless paradigm — at least for application development. They want to ensure there are tight controls and auditing on all changes to the software systems including infrastructure, application code, configuration, and data.

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Why You Need a DevOps Consultant

Efficiency is one of the fundamental pillars upon which any successful business is built. We’re all familiar with the old adage that “Time is money,” but many companies don’t appreciate just how deep this simple observation runs.

Amongst businesses who do prioritize the efficiency of their operations, there is an ongoing need to devise better workflows and to identify where other inefficiencies lie.

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Where Are You on Your Mainframe DevOps Journey?

Mainframe-powered firms are all at unique points in their mainframe DevOps journeys. We know this through the DevOps Transition Workshops we conduct with customers; the DevOps data we collect from customers leveraging Compuware zAdviser; and the insight we glean from industry thought leaders, analysts and partners.

Whether that means your journey hasn’t begun or you’re using CI/CD pipelines, we want to help you learn and continue to leverage the power of mainframe DevOps. We’ve made it easy to discover where you are and where you’re going on your mainframe DevOps journey, with five stages that offer supporting resources to help you move forward.

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Mining the Ground Truth of Enterprise Toolchains

To learn more about what works and what doesn’t in large-scale DevOps and agile deployments, we need data. The problem is, that data is notoriously difficult to get ahold of because much of it lies hidden across numerous private repositories.

Efforts such as The State of DevOps reports have helped us gain some understanding by using survey data to answer questions about practices such as the frequency of deployments in a team or organization. However, survey data has its limitations, as Nicole Forsgren and I described in "DevOps Metrics," which we wrote to clarify the trade-offs of system and survey data collection. 1 Today, our understanding of DevOps practices is based largely on this survey data and on anecdotal evidence. Is there a way to expand our view of DevOps to include studies of system data of DevOps at work?

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The Future of DevOps

To understand the current and future state of DevOps, we spoke to 40 IT executives from 37 organizations. We asked them, "What’s the future of DevOps from your perspective, where do the greatest opportunities lie?" Here’s what they said:


  • Security is huge. The more we automate the more we can automate problems. How to integrate security into DevSecOps. More connected = more exposure. What happens when next wave of tools comes out. What is going to be the driving tools after containers?
  • DevSecOps – security and privacy. Don’t need to know the details. Know the kind of information that is private. Common security things and privacy things you need to worry about. As a group working with educators here’s what you need to teach on security and privacy in addition technical. Human aspect cannot be ignored. More teaching on the socio part in addition to the technical side. See more collaboration. There will be a huge backlash with more disclosures coming out. 
  • Next two to five years merging operational stuff. Public cloud is solving a lot of issues. Opportunity to get more discipline about security. Finding a way to make that easier. 
  • As DevOps grows as the standard way of working across dependent IT teams, along with its subsequent rapid increase in global adaptation, one can expect more focus on DevOps in the fields of security, IoT and cloud computing. These are relatively nascent streams of DevOps right now, but the spread of DevOps into various uncharted fields of software could mean widespread implementation into various fields of technology and operations in the future.


  • DevOps with cloud-native and microservices allows you to revolutionize the app lifecycle – testing and production are integrated, and you see problems before you go live thanks to testing and troubleshooting. Analytics across the DevOps pipeline and runtime you can do things in a much better way agility with insight and control. Apply ML to determine risk. Bandwidth use, performance, see problems able to see code and configuration changes. Connecting the two together is a big thing. Can shorten the lifecycle with higher quality. Insight and control with security. 
  • More people are taking the leap from DevOps to data ops. As streaming architectures become more popular people will want and need more data ops and data logistics in their organizations because of ML adoption. Maturation of DevOps with the data becomes more pervasive. Simplifying the toolsets to pick up GPUs and run ML is becoming more pervasive and less scary. Use cases deliver high value. This is creating the launching pad for practical applications of big data in business. 
  • I see a lot of opportunities related to artificial intelligence (AI) and machine learning (ML) as it relates to DevOps. Anticipating how changes I make to the application affect the overall user experience. What do I need to test? What is the impact of a change? There is a lot of opportunities to help companies decide what to do next with their product as well as verify the impact of those changes, so they can confidently release high-quality software targeted at their end users’ needs. For example, with AI and ML, I can analyze hundreds of thousands of test results on my platform and determine those changes to a particular portion of the code base result in, on average, a 20% failure rate. The actions I can then take care to ensure that, whenever changes occur in this part of the code, we test much more thoroughly. An even better approach would be to refactor this portion of the code base. AI and ML can allow organizations to make better decisions faster by providing unique analysis correlating, code, test results, user behavior, and production quality and performance. This type of capability can have a dramatic effect on increasing the velocity and quality of DevOps pipelines. 
  • AI integration. To get good data you have to be able to repeat the process over and over again without human intervention. Repeatable and quantifiable processes. DevOps is about scaling out the same test over and over again. With AI it’s automated and the data reports automatically. AI development with DevOps. 
  • AI/ML data-driven understanding patterns are working and automating the findings of the patterns, remediation, going into a self-driving, self-healing. DevOps has to be built into that. This is my business environment, how do I build an app to reduce friction. Infrastructure needs to be set up based on the business the developer and the user.


  • Automated cloud-based testing. Security is integrated into DevOps upfront for DevSecOps. 
  • For big companies, which is who we focus on, renew your thinking, your processes, your architecture, and your applications. All of these make up your legacy—not just the applications. All need to be modernized. By renewing your thinking, I mean, embrace that you are turning into an IT company no matter what your business is. You’re no longer just a bank or an airline. Your software is central to the services you provide your customers. Since you’re now an IT company, you need to think about how to improve your IT processes so that they’re more streamlined, efficient, and producing value. Automation of application development and provisioning is a big part of efficiency and turning out better software. Also, look to reduce your number of applications, and use principles of modern software development to rewrite them. 80% of your code is old, never used, and is full of bugs. You need to modernize it to increase quality. 
  • DevOps is one area where they are watching the market and talking to start-ups. Testing, deploy management, process, security can all benefit from automation. 
  • 1) Opportunity to pursue what DevOps is supposed to mean. Pursue automation. Opportunity to mingle changes in automation with the cultural change to maximize flow, feedback, and continuous improvement throughout the process so problems are solved in the process. 2) Think about security more heavily. 3) Making work fulfilling and rewarding. 
  • We’ve seen more standardization and common tooling. DevOps is becoming more precise and scientific practice. More reference cases. Learning and tooling will spread to the community. More understood as a practice, more standardize and more precise and scientific. Opportunity to do more with merging DevOps with security. Huge potential with automation and CI/CD with DevSecOps and the value of automation in the security area.
  • 100% automation of CI/CD, unit testing, QA. More tools and expertise get better the more quickly you can run with your plans. Fully automated to continuously release software, features and bug fixes.


  • People on a DevOps team and looking at that as a quality of life factor when looking for a job. This will result in the coolest innovation for the companies. Focus on attracting the right talent and breaking down the silos.
  • Platforms to enable collaboration and automation. Once present in an organization enables greater success with predictability. Rather than failing fast all the time prevent failure by learning from your failures so you are failing less frequently. Tools, process, metrics, reporting take advantage of learning for continuous improvement. 10,000 releases per day at Amazon. Financial services want to reduce app dev time from 300 days to 120. Ability to articulate the value to get continued resource support. Gartner report when starting on DevOps, commit for three years. After one year, it’s worse than when started. You need to break through the wall to see the benefits. Accrue analytics necessary to learn and improve.
  • People continuing to break down silos. People are getting smarter about bringing everyone to the table. As people bring about new projects they will think about building and deploying before thinking about rearchitecting. Growing pragmatism. Ask where we get to with intelligent build automation before we go cloud or microservice.
  • Infrastructure as code. A set of tools will be used for deployment. Ultimately it will be cloud providers handling deployment effectively removing the operation side.
  • If we expand the scope of thinking a focus on user stories it’s not a question of technology it’s a question of low-level infrastructure and a full spectrum of application delivery – observability, PaaS, alerting and dashboarding. Beyond operations to entire IT organization.
  • Exploring extensively. Right now it begins when writing the code ends. Writing the code is not part of DevOps thinking. We’re changing that since code generators are becoming popular. In integration generating the code is part of the DevOps pipeline. We’re exploring extending the DevOps pipeline to generating the code as well. The industry will collapse into two or three main product ecosystems – probably a dominant in every vertical a SaaS best practice.
  • The network. Need to envision to support the application for better time to service. Hasn’t changed much in 15 years. The network has not been designed to react to the needs of the application and the change in deployment. From the operators’ perspective, there’s not much focus on day two operations and repeatable processes and the operation state of the environment at the moment.  Tooling is happening but its early in its infancy.
  • For DevOps to work, developers must be willing to expand their expertise and responsibilities. DevOps relies on a “many hands make light work” ethos which requires everyone to handle a piece of the development, operations, and security pie. This shouldn’t be viewed as a burden. DevOps is an opportunity for developers. DevOps enables companies to clearly define roles and responsibilities for developers which gives them autonomy and responsibility for the development of their project.
  • Today there are a lot of independent Lego blocks. K8s independent of cloud, CI is independent of CD, security, all are important and all are tiny dots. Will see a more holistic, integrated POV, from doing it yourself to integrated and validated as part of the flow. Simplification with alliances, acquisitions to make it easier.
  • We think the greatest opportunities lie in implementing and scaling DevOps in large enterprises. Enterprise IT is so diverse across companies and industries, so turning these horses into unicorns will have an incredible impact on the world. 
  • 1) In the future, DevOps will be popular in the Enterprise. Dev and Ops teams are increasingly moving to rapid development and reliable, high-performance services delivery. 2) Software enterprises will push towards a microservices infrastructure. Microservices allow organizations to architect an enterprise solution, independently, over a set of smaller services.
  • The biggest opportunity for DevOps is to drive into tech stacks and organizations that think that a move to DevOps requires a complete re-architecture of their application or adoption of replacement technologies. While those sorts of changes may be excellent opportunities to introduce culture change as well, teams running existing business-critical apps in “monolithic” architectures can take advantage of DevOps as well, if they choose the right tools.
  • I think containers continue to provide interesting opportunities as the tooling around them improves. Depending on how they’re used, they can confer a lot of benefits or a lot of downsides within a system. There are a lot of untapped or just-scratched ideas on how to use them to convey significant benefits, though. For a few examples of what I mean: using containers to sandbox applications for security and resource reasons. It seems basic on its face, but we mostly see containers used for stateless (and some stateful) server-side applications that are otherwise operated in traditional manners within those containers. The container is treated more like a packaging system than a runtime. What if you generated an application container per user or per user session? It provides a limitless array of opportunities for improving system security, user security, and adding additional analytics around user behavior or preventing resource contention between users of your application. As the tooling improves, containers become even lower-cost to deploy. I think there’s many yet to be tapped opportunities in how containers could be used by both technically-focused and traditional industry verticals to improve their business.
  • Platform-as-a-service is definitely a growing field. In the long-run, an application developer should be able to simply define a couple of entry points in their application package and that should be sufficient for the application to be deployed. I think this is very much aligned with what we have been doing in developing an abstraction between Applications and Technology Infrastructure. Another example is our Data Science Platform as a Service, where the DevOps of the platform itself is fully encapsulated away from the application developers who use this platform to develop machine learning powered applications.
  • Kubernetes (K8s) and Docker seem worthwhile to learn about for any DevOps team. Another one is scaling of machine learning. Not everyone is doing this and it’s new so there’s a lot to learn there. We’ve been diving into what it’s like to scale TensorFlow serving and I think that this is going to be a big part of DevOps in the future.
  • Containerization opens up a lot of possibilities in terms of revamped architecture. The surrounding architecture is isolated from everything else. This provides deployment mobility and flexibility in terms of what libraries are in use and what resources they need which is exciting. Another one is dealing with machine learning and IoT data. There’s a massive influx of data from devices and so we have to think about how we store it in a usable format and also the global distribution of it. People need to be able to get data from a variety of locations which brings politics into the mix. There are rules in development from countries all over the world that make determinations about how data is stored. It’s becoming kind of tricky to have international customers because data has to be stored differently in various physical locations. Finally, the move towards the SRE model. Operators will no longer be a separate team but work on each project with developers as they go. Part of this will be because of containers because you’ll be working together to build at the development phase. Containers couple the development process with the deployment process.
  • In today’s DevOps environments, many technology professionals have mastered operating containerized workloads at scale, and leveraging containers in production. DevOps teams that have mastered containers, and have begun deploying microservices, are swiftly moving into the future of DevOps. The future of DevOps will include increased deployment of microservices and implementation of a service mesh architecture to manage and monitor said microservices. Moving forward, tech pros will increasingly need to develop a good understanding of the breadth/depth of service mesh necessary for their environment and choose the right tool to fit their needs. The future of DevOps also lies in focusing on serverless computing and determining the use cases where serverless computing is appropriate for certain distributed environment and use cases. With this acquired skill, DevOps teams will be able to lead their organizations to strategic decision-making and ultimately into the age of digital transformation.
  • The future of DevOps is bright. I believe there are great opportunities to deliver purpose-built pipelines that would connect to common repositories and perform a set of necessary steps to reliably deliver working applications to known environments. All cloud-based of course.
  • Containers will continue to grow prevalence and influence within the modern technology landscape. DevOps has always been about automation and this technology allows for companies to more easily build and deploy infrastructure. More companies will adopt containers in an effort to improve automation capabilities and velocity. Organizations will also continue to seek out technologies that integrate into frameworks like K8s as work to further improve automation and velocity.

Here’s who shared their insights with us:

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DevOps Concerns

To understand the current and future state of DevOps, we spoke to 40 IT executives from 37 organizations. We asked them, "Do you have any concerns regarding the current state of DevOps?" Here’s what they said:

Organization Change

  • Don’t over-engineer. Ideal DevOps situation is a company building with as little friction as possible in the best fashion. Copy the culture not what’s been deployed. 
  • As I mentioned earlier, a lot of tools are being spun as DevOps, but if those tools don’t focus on building the culture—on bringing people closer together—and on increasing value creation, then they’re not really doing DevOps. 
  • We still have too many organizations not focused on systems thinking. People are hyper-focused on automation when they don’t have a great culture. Automation helps companies fail faster. Be value-stream minded. Plan backward. Most bottlenecks are at the very end. Plan backward to get to the bottleneck more quickly. Focus on reliability versus resilience. 
  • Not sure. See where it goes in a year or two. Getting too far away from culture drivers and getting too lost in the technology. Success is breaking down silos between dev and operations. 
  • I think there are many concerns and challenges with the current state of DevOps.  The hype and expectations are high.  Implementing and scaling DevOps in large enterprises is difficult.  It’s a transformational experience, not a project.  Many companies will jump on the DevOps bandwagon without understanding the primary business objectives, and they will also not be willing to make the cultural changes necessary to create an organization that is focused on continuous improvement.  They will meet failure with regression and revert back to the old way of doing business which will put their business at a disadvantage.


  • There are so many tools in the DevOps space that all claim to solve DevOps challenges. It is becoming a very crowded space and many vendors are claiming to have the silver bullet to solve DevOps. My concern is for companies that are just starting to test the DevOps waters. It can be very confusing and overwhelming, given the broad array of choices and methodologies. 
  • One concern I have is that as we continue to use more layers of abstraction on top of the underlying computer system to simplify building automated and complex distributed systems, that the next generation of engineers may lack the fundamental system knowledge necessary to truly understand what their applications are doing. I see things potentially headed this way with the emphasis on fully abstracting compute through concepts like Functions as a Service, which, while a powerful tool, could potentially lead to engineers who are unaware of the realities happening behind the scenes. A person is only capable of holding so many layers of abstraction in their mind at once and still working at that top layer. Building a robust, high-performance, scalable, and secure system relies on fundamental systems understanding. It creates a catch-22 for the next generation of DevOps engineers.


  • Not enough practitioners, more expensive to hire, not taught in school. As more people adopt it becomes a more expensive labor proposition. It affects what a developer is.
  • How do we move this faster especially on the people side? This is a giant transition for the industry and a different way of getting the software to the market. Significant skillset needs. How do we get people to evolve their skillsets?
  • It seems like there are fewer and fewer people in the field capable of doing the job. This may have to do with the fact that people don’t want to be ‘on call’.


  • People are still not buying into full testing with a repeatable baseline. Still a few cutting corners. Do it right the first time or you’ll have to do it again. If you get failures that’s good data, you can fix it before too late. Data tells you whether it’s good or bad. Don’t have fear of getting a bad result. Security tends to be overlooked. Multi and hybrid-cloud need to be paying more attention to security.
  • It’s still early in maturity. Part of the problem is people promising silver bullets. Gartner hype cycle – multi-year journey, organizational change, here’s the value you can realize. Set reasonable expectation. Don’t leave a bad taste in people’s mouth. It’s a three to seven-year process, not six months. It took 12 years to go to VMware, how long do you think it will take to move to microservices? 
  • The level of maturity is growing but some things are still unknown with regards to tooling and deployment environments. The notion is that people who have done DevOps to-date were very forward thinking. The next batch will do DevOps because its fashionable or trendy but they don’t do it well – they will be less successful. 
  • DevOps leads people to just think dev and ops. DevSecOps is really agile with a customer feedback loop. Every company and vendor talks about DevOps in a different way. We need greater clarity and simplicity. We’re getting better but we still have different interpretations.
  • What do you do with legacy systems? How do you adapt and make sure they do not slow you down? Each organization has to deal with their legacy systems and software.
  • Need an exit plan for the technology we cannot get spare parts for in two years. Different architectures, processes, teams you need coordinated releases and deployments. Need to coordinate to release a new mobile app. Freak out shows in many ways. IT delayed set up of Chef prototype environment for two years. Need to be smart and transparent.
  • DevOps will evolve as businesses adapt and discover its greatest benefits based on their own needs. One area that we see DevOps helping tremendously is in reducing technical debt. Many development teams find themselves having to update a horribly outdated project or struggling to keep applications up to date and clean. Often, engineering finds itself at odds with business stakeholders or trying to squeeze major refactors into feature development. Paying down tech debt is never a bad thing if it helps a company grow. Companies with minimal technical debt are nimble and more competitive. DevOps is an important triage step towards paying down that tech debt. 
  • Converting (or creating a new one) the application to DevOps is only part of the challenge. One of my major issues in DevOps is databases. Databases are stateful and cannot be treated the same way as stateless applications. We cannot spin down and spin up and scale down and scale up databases easily as data has gravity and requires special caring and feeding from the team. This makes it hard for them to be adopted in a DevOps environment.
  • We have embraced a culture of diversity and team-driven innovation, which has led to great success with our products. Many of these achievements can be attributed to the fact that we enable different teams to leverage different tools in building their applications. However, this heterogeneity also brings two challenges: 1) Different levels of SDLC maturity – While some teams are pushing the envelope and using the most up-to-date DevOps solutions, there are still teams across the organization that need more help to modernize their developer workflow. 2) Change is hard – Long-standing organizational structures, culture and dynamics, as well as code pride, are often barriers to cross-team collaboration and InnerSourcing. Another challenge we have is how to set up best practices for newer DevOps tools within the organization. For example, the way Docker images are typically reused across the industry represents a risk, because it’s often hard to reproduce the sequence of steps needed to build an image. This is especially true if you’re using up-to-date versions of the depended-upon images. As people are updating their workflow to leverage tools like Docker, we need to develop a better Continuous Integration/Continuous Development (CI/CD) practice for Docker images – one that will likely require some form of mechanism to keep track of coherent sets of versions across images that are already tested.
  • Yes, as DevOps adoption increases, there is always an over reliance on tools. This tool will fix the problem! Or, that tool didn’t work, and we need to use this tool. Teams must always remember that tools are not culture. I’d hate to see DevOps viewed as a set of practices that may or may not work for a company. DevOps, when implemented well, works every time
  • 1) As with any other technology, things will evolve with time and more research. To mention one concern in particular, it seems that DevOps is typically limited to the developers and operations teams. By integrating DevOps solutions with third-party software, the DevOps scope could be extended to include other teams in the IT department. 2) DevOps should also be able to support more complex applications to better facilitate technological adaptation. Many organizations have large, complex application ecosystems, a lot of which fall outside the scope of DevOps. Support for such complex applications will allow businesses to overcome the barriers of traditional application life cycle management and adopt DevOps more willingly. Also, with increased awareness, more IT Ops teams will see the business value in DevOps and consider implementing DevOps for delivering maximum efficiency. Compared to how quickly enterprises adapt to DevOps changes, having SMBs get completely on board with DevOps seems like a challenge.

Here’s who shared their insights with us:

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Common DevOps Fails

To understand the current and future state of DevOps, we spoke to 40 IT executives from 37 organizations. We asked them, "What are the most common DevOps fails?" Here’s what they said was typically missing:

Fail Fast Mentality

  • People who are successful are people who can talk about failure in the right way. Talk about what you are learning from the data. Apply a method to identify the failure. Fail to recognize that part of DevOps is to divide the team into eight to 12 members with developers dedicated to one project. You need everyone on the same team – if you fail, you fail together. Start with boot camps with all the developers. Short camps about how to manage people, measure success, assign work. Focus on tools and automation and don’t think about what you are trying to accomplish, what value trying to bring and then map KPIs to the value proposition.
  • People and technical side. If the customer starts with “fail fast and iterate” it’s pretty easy. Honest and healthy retrospective versus coming from a blame model. Need a blame-free environment to successfully implement. On the technical side, we’re seeing fewer failures. The fails typically are functional. You get drift in environments to find that a quarter of incidents are the result of a divergence in staging and production environments. Automate the creation of staging and production.
  • In companies with a culture of learning from failure, you see people who are not afraid of failing. You see a production issue where some part of the end of the value stream was forgotten because someone didn’t document a firewall rule. It’s important to document everything. People are starting to understand the need to document the failure and talk about it. Being transparent builds trust.
  • A culture where slow means safe. You can go fast and be at least as safe. Misguided performers are conservative. They go slow so won’t screw up. When you have problems it takes longer to recover. Learn from your mistakes. If it hurts, do it more often, smaller batches, learn. Very few times when you need to go slow to prevent failure – a rocket launch or a heart surgeon. You have to fail a thousand times to get the rocket launched or be successful with heart surgery. Apprenticeship is a good thing. Recognize you can go slow and sort of be safe but when something comes up you have to act fast. We had a financial services company shut down for a weekend to address the struts vulnerability. You can automate everything.
  • At Jenkins World had people Tweet their “DevOops” stories. Very technical. Doesn’t talk about specific applications. Remove dash R – remove everything. Still focused on practitioners making a specific mistake in their work. Would like to see the bigger picture. Templates and patterns. Find people with the courage to tell you what didn’t work. The first wave will be process oriented. Want a process where you can create. Gene Kim unicorn – only hear about the perfect company. See what happens in the real world. Talk about challenges.

Executive Support

  • 1) Having a lack of senior support. It pays to find someone in a leadership position who is excited by this. 2) The idea of being afraid to fail. Embrace failure and learn.
  • 1) Regression to ITIL. As we gone down the path of DevOps adopt infrastructure as code, can cascade to being devasting to a large swath of infrastructure. How to use tools with leverage safely. As we get into frontier problems, challenge organizations to think about problems differently. Policy as code approach – codify policy so you can automate enforcement. Get away from manual processes as a default. 2) Not having executive alignment. Change agents try to forge a path without executive being aware or supportive. So much of what you’re trying to do is organizational change. Need to force teams to play nicely.
  • The most common failure is not getting executive management on board early. Only the management team can align the resources within the organization to drive the level of transformation that is required. Management also has an end-to-end view of the value chain and is best positioned to bring all the resources of the organization to bear on a common plan. If management is not on board you either have to help them understand why they should be or ask yourself, “Am I working at the right company?”


  • Applies to any DevOps transformation. Many customers excited, purchase a lot of product, time to renew but people aren’t using. Couldn’t get sufficiently organized to use. These processes are like a recipe and cannot be driven by one team. This is a culture and process change. Pushed more with customer onboarding having an executive sponsor to resolve tension. Every representative of the lifecycle is at the table. Need to bring everyone to the table or one group will feel offended they weren’t part of the process and end up being the bump in the road. Multiple constituents need to build consensus across groups and processes.
  • One of the biggest issues is when processes are not aligned across teams. DevOps is a great buzzword, but it will not work unless all teams are bought in on an agreed upon process. In many cases, executive level alignment is required to get everyone on the same page. Another common failure is thinking tools alone are the answer. Often, companies buy the hot new tool on the market and think it will solve their problems or magically get them to DevOps. Tools are only as powerful as how they are used and implemented. Companies must first start with defining the people and processes involved, as well as the desired end state goal. Once they do, and have organizational agreement, tools can then be selected and implemented to automate the DevOps process as designed. Start with the process, then pick the right tools.
  • Deploying the infrastructure without the culture. Developers and operations need to be on the same page. It’s more about the culture. When you wind up having a small team who run DevOps, becomes a big risk or bottleneck.
  • There are a lot of human challenges with too much isolation between the teams. You don’t know how what you’re doing affects someone else. Need known knowns. When teams interact it’s like a firefight. An interrupt-based approach is not good for either side – DevOps or data science. That interaction can be a source of conflict. It’s about making the data available to both. Build into the company process enabling access to data in the pipeline. Understand there are things that multiple people need. As an executive the need to manage technical debt. I do I identify my greatest technical debt; how can I understand the impact and how it works – processes and risk.
  • DevOps programs often fail because the company’s culture discourages collaboration. This is something we often see from our customers with legacy development methodologies are often rooted in isolationism. Development, operations, and security teams are cloistered and rarely know what the other groups are doing. The teams have completely different incentive structures and often have competing goals. Development teams need to hit goals which causes them to write code first with little thought spared given to security. Security teams bring the process to a halt because they are worried about vulnerabilities that can be exploited by cybercriminals. Operations teams are resistant to change, making it difficult to introduce new technologies like Kubernetes and containers. Organizations can solve this problem by taking DevOps implementations slowly. You need to crawl before you walk and walk before you run. DevOps is a cultural transformation that can be met with resistance by people afraid of the unknown. Businesses should be patient with their staff and refine their DevOps program based on their own experience. DevOps is a long-term goal, so patience is required.
  • In many cases, DevOps teams still segregate the concerns of “Dev” and “Ops”, while the best teams build culture and process that integrates the infrastructure and app code management as part of the same system design. Not only does this shared responsibility drive developers to improve the reliability of their deployment systems (and app code!) it drives empathy between team members. If you or your teammate might be the person that will have to address an infrastructure issue late at night caused by an app code change, you’re more likely to write better tests, patch sub-par monitoring, or devise ways to scale in an automated fashion.
  • Not sharing the same goals and only focusing on process automation. The importance of DevOps starts with developing a common goal across functional units. For an organization to achieve full potential, it’s much better to look at the whole dev life cycle and find opportunities for encapsulation and abstraction. On the implementation level, the other most common fail we’ve seen is the inability to create automated tests that are representative of what issues may actually happen.
  • Some tech pros assume DevOps is simply merging development with operations and then agreeing on new agile processes to bind teams together. In reality, DevOps requires much more than merging two disparate teams—it requires collaboration before development and after production, to ensure teams find ways to use their tools in new ways that connect, rather than firewall them from one another. Successful DevOps teams align individual people and teams with their strengths but invest in technology that connects them. For example, operations teams are focused on monitoring dashboards throughout the application lifecycle, while dev teams help ensure metrics, logs, and tracing are baked into applications from the very beginning. By collaborating at every stage, both teams find ways to use their tools to help one another.


  • We hear from people doing DevOps for 18 months and yet to release anything. In the wake of doing quite a bit of automation find a couple of areas of low hanging fruit a lot of time or waste and try to automate – manual build or test. DevOps is about automation. Lack of automation is the number one cause of failure.
  • Unable to move massive amounts of data.
  • Cutting corners. Not going through all of the security checks and deploying products on admin or privilege level where you shouldn’t need to. May accidentally require a privilege level to use the app. Don’t find out about security vulnerabilities and until it’s too late.
  • Insecure apps unless doing DevSecOps. Quality of coding and runtime integration will mean less because of programming insecure applications, loss of trust and decline on the stock exchange.
  • Customers are not tolerant of failure. Deploy to production and retrieving is not acceptable in banking for compliance issues and lose the trust of customers. Banks don’t fail fast. They’re willing to embrace the pipeline and velocity up to deploy quickly. When it comes to deployment want to test a couple of times and deploy every day versus a couple of times per year. Test more before deploying. Velocity to getting to test environment is pretty good but wait in test for more of a planned release.
  • Taking on too much. Everyone is trying to transform. People are used to the traditional way of planning. If can adopt DevOps now developing and employing more rapidly. Other business units doing annual planning need to adopt DevOps. Apply DevOps notion across the entire value chain – how to collect money, work with partners, manage budgets, pricing. Global companies need localization – need to look at end-to-end. Where do I get started? Apply lean principles – remove anything that’s not adding value to the customer. Where is my biggest bottleneck? How long to make and implement the change – testing, automation, decision making between two teams.
  • If you want a recipe for failure, give your developers too much power, and don’t give them any responsibility for what happens in operations. Developers will just keep throwing code over the wall, which will lead to a lot of frustration in ops and a widening of the productivity gap. If you want to succeed at DevOps, don’t push bad quality forward. Focus on “first time right,” instead of on fixing problems later. Also, pay attention to the whole release-to-production pipeline, not just continuous integration. If you don’t, you might gain some speed early on, but you’ll soon hit a wall. Finally, automate in ops. If you automate a lot in dev but not in ops, development will speed up dramatically, but ops will fall behind. Dev may be tripling their output, but ops will be stuck doing most things manually, and again, you’ll widen the productivity gap between the two.
  • Over-engineering. Fall in love with the process and forget to bring value. Need someone from business in the loop. Separate silos created hard to create a joint team across the organization. Fail to see the most value. Rectify by involving people from the business. What organizational structure does the DevOps team report to? Each organization needs to determine what’s the best fit. Visit other companies like yours and see how they built their DevOps team. Failure is not getting the value you expected up front. Going from monthly to weekly to daily to multiple releases per day.
  • People tend to choose a technology because its hip and col and try to solve the problem versus thinking about the problem to solve and picking the best technology to solve. Don’t blindly pick the technology that works for others and apply to your own situation. Pick the tools and technology based on the problem you are trying to solve. Don’t’ force a technology on the problem. Not being clear about responsibilities. If responsibilities are not well-defined, then culture change doesn’t take effect and people don’t take ownership. Information must be shared and made transparent.
  • The two most common problems are misunderstanding the type of change that is required to begin a DevOps journey and not taking the time to create an environment where teams can invest the time necessary to make changes. For the first problem, DevOps does not simply mean buying a tool. It is much more about changing the way teams work with each other, how they view the value each other provides and how they measure and experience success together. Early in coaching engagements, we work with clients to ensure that stakeholders are aware of and understand the steps involved in a successful DevOps adoption. For the second problem, it’s important to note that change takes time and can be difficult. Because of this, it is critical that an organization’s leaders shield new teams from the traditional organizational pressures to deliver, which the company is moving away from. There is often a lack of organizational support in long-term adoption, which usually results in teams that don’t feel they have enough time to change the way they work or learn something new because deadlines are too tight.
  • Two major things come to mind. 1) The first is not having rigorous and automated testing for your infrastructure automation code. 2) The second is not ensuring the auditability of operations engineer actions. In many DevOps organizations, there exists a cultural structure that believes that operators are intended to create automation to enforce quality and auditability on the work of other software development teams — but somehow these same standards do not apply to the operations team as well. Having automation built into your source control system can help to ensure that untested code is never allowed to be merged and forces engineers across the company to ensure tests are written. By the same token, audit processes should be held in authority outside your operations team and the operations team should deploy auditing systems that include monitoring and tracking of their actions as well. Auditing should be done by a security or compliance function, rather than the operations function, within your overall organization.

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How DevOps Has Changed

To understand the current and future state of DevOps, we spoke to 40 IT executives from 37 organizations. We asked them, "How has DevOps changed since you began using the methodology?" Here’s what they said:


  • DevOps used to be only for Silicon Valley, now it’s on everyone’s mind. How can it help our organization? It’s a mentality and methodology to fit your organization. How do we get started with the framework and the momentum? 
  • Most notable is greater awareness. Instead of how different from Jenkins now aware of other tools, understand where and where the different tools in the system apply. Fluency and understanding has improved dramatically. People are understanding the importance of including the database in the process. Maturity as people figured out to get business requirements, write app code and move faster. Realize there’s more to the story of full-stack DevOps. The entire stack needs to be automated in order to be successful in speed and quality. 
  • More acceptance as people are aware of the benefits and more availability of tooling and knowledge. AWS has added a lot of services over time. People are building tools and products with increased adoption of DevOps – more easily available and easier to adopt. 
  • Still in infancy. Start-up SMB is using DevOps. Global 2000 is very spotty because ITIL processes are ingrained. Congealing around super developer and getting rid of ops isn’t going to happen, more clarity about the agility of application teams and enablement. Formally a buzzword, now clients looking at adopting DevOps. 
  • A big change in the past year with maturity of tools, platforms, and thinking. People are embracing and implementing. People understand the benefits. Large organizations are embracing DevOps and new ways of thinking. May lag with legacy systems but seeing a desire to bring legacy processes into DevOps. 
  • Four to five years ago DevOps was a mythical thing from Silicon Valley with Netflix, Facebook, and Google. Over the last few years more companies getting on stage and talking about how they’ve transformed from within the organization and individuals taking on more responsibilities and automating more. How to share more openly with others. Measurement and sharing is growing. Finance and telco organizations have their innovation labs to attract new people with a different mindset to change the way and the behavior of the traditional employees. Teams are figuring out how the company goes through digital transformation and how to build software and fit within the current organization and processes. They teach the rest of the organization how to do things differently for the future. 
  • Broader adoption. Infrastructure as code is getting more attention. Culture adoption was the first wave and is now a constant. Now rolling into more tools with software and virtual hardware with infrastructure as code automation. Using automation in CI/CD for fresh environments and blue-green releases.
  • Better understanding. Through conferences, articles, podcast the market is much more well informed about what you can accomplish though there may be some differences in understanding what every aspect means. People accept the concepts and want to know how to implement.

Public Cloud

  • The emergence of cloud-native gave us the push. We’ll always need DevOps. We needed to up our game when we began delivering code for running services. The entire team working on a well-understood process. Why we automate everything through Jenkins and adopting containers, how to instrument the cloud. We don’t want to take chances. Experiment with code to see if we’re going in the right direction. Eight different environments before you get into production. Now fewer environments.  DevOps simplifies the entire process. 
  • 1) Change in attitude the rise of Kubernetes (K8s) in the public cloud. When the cloud begins to run K8s for you that’s a game changer. Having someone else run it reduces the difficulty. Can deploy large amounts of software on a platform that’s already set up. Because some tasks are automated there’s less to do. The reality is when these changes happen there will be job loss, but so far having it become easier creates more workloads. Tools are changing for those moving to K8s. Large cloud properties Intuit has gone all-in on K8s. Hiring a lot of people. 
  • Tools becoming more sophisticated and the cloud environment. Used to be on-prem and nothing more. Now cloud and hybrid cloud makes it easy to spin up and spin down. Ultimately the cost of doing has gone down and the benefits have gone up. 
  • Leading edge people are pushing the frontier with the public cloud and more API access and automation. Leaders continue to blaze a path forward. Most folks are nowhere near that but their recognition they need to do this to keep up with their customer-facing applications. Feel fear and urgency across the board. 
  • The DevOps terms that started back in 2007 has only now being kicked into production for the majority of organizations outside the big websites. So the strategy is moving from early adoption to mass deployment only now.
  • The greatest change in the last few years is not necessarily the concepts of DevOps, but how widespread attempts at adoption are and which parties are spearheading adoption. 

Containers and Kubernetes

  • It’s a philosophy or thought process but a tactic alignment of DevOps and containers. It’s much easier to do virtualization of code and automation containers make it easier for developers to do work in an environment that looks like production. Containers enable a shift-left attitude. Dev community is hangry for K8s – it’s the standard for automation. K8s is the baseline and developers are jumping on the platform which is much more agile.
  • Seen increased adoption. Also, see people coalescing around K8s for managing containers. Containerization is an evolution we’ve seen, and it creates a mindset where I have a configuration that I apply to my test environment and then apply to my production environment. Now build container in non-production and then move to production. VMs and containers, not or.
  • More mature. More adoption in industry verticals with more conservative companies. What changed is the container movement. Docker and container orchestration more digestible for users in small and large companies. Ability to easily package software component to move in a CI/CD pipeline. Docker is not that incredible but the standard format of the images and the ability to put the image where ever you want makes DevOps and automation more feasible. K8s is a mini-cloud. These created an acceleration.
  • The biggest change to DevOps has been the recent movement to containers. It seems like everyone is rolling out containers within the cloud environment. They’re the new hotness. The problem is many organizations aren’t using them strategically. Containers do many things well including acting as a cost-efficient artifact that makes development quicker. However, many businesses have found diminishing returns if they roll out too many containers.
  • At the start, DevOps was primarily used as an operational philosophy. This philosophy was mostly associated with the Agile methodology for development and integrating development and operations functions within the same teams and queues. Treating infrastructure as code grew out of this. With the advent of cloud architectures — and now containers — it has become the default way that modern organizations approach working with infrastructure. Now DevOps is less focused on the basic requirements around infrastructure as code and more focused on how to use these concepts to provide continuous integration and delivery pipelines and ensure auditable automation is a component of every function of product delivery. This allows DevOps teams to assist with organizational cost optimization and compliance goals.
  • Containerization has been a big change. We’ve also almost moved from Puppet to Kubernetes!


  • We’re an early adopter of DevOps enabling the fast scaling of the business. Make sure tools, processes, and standard operating procedures are well understood. We support seeing operation metrics out of the box. Handle logistics of data movement and staying resilient across data centers.
  • Since we began using DevOps, the biggest change has been the explosion of tools and infrastructure options in the market for automating the entire DevOps process. Years ago, DevOps was merely a concept of bridging communications and processes between Dev and Ops. Today, DevOps is seen more as a continuous pipeline from planning to production. To optimize the pipeline, and automate all steps in the process, a massive proliferation of tools has emerged. For deployments, it was first Puppet/Chef/Ansible and now Docker is taking over. Everything is done in the cloud. Local labs (physical and even Virtual) are a thing of the past. Cloud/SaaS has been a huge enabler of optimized DevOps pipelines.
  • Rushing from Dev into Ops and releasing three a day versus three a month. This is causing SQL ingestion to get worse. 45% have information leakage, up 8% versus previous years. Adapting more DevOps and getting more vulnerabilities. 60% of detected vulnerabilities has never been fixed. 139 days to fix a vulnerability. 40 to 60% of enterprises don’t use any security testing.
  • Partly greater mature adoption. How to CD software, eliminate friction, collaboration. Getting faster. The application was not changing. Seeing more non-deterministic applications. The application may change based on the data from sensors. How to test and address that sort of change. Infrastructure applying DevOps focus on CD apply AI/ML for testing and determining patterns. Can put inert features until feedback is provided. Analyze the data. Shifting from deterministic to non-deterministic application DevOps becoming more intertwined.
  • Within DevOps more automation which is natural. Tools for automation more sophisticated and creating more value. DevOps went beyond R&D and more of a cultural issue. Evolves into a culture issue later in the adoption. This is a function of seeing where it can help them and fit in their processes.
  • The overall principle of “infrastructure-as-code,” and of reducing the variability in how application developers configure their applications, has stayed pretty much the same. Where we’ve seen major changes has been on the development SDLC side, where we’ve adopted new technologies and tools, such as Jenkins, Docker, Kubernetes and more.
  • My experience goes back to before people were even calling it ‘DevOps’, so I’ve seen a lot of changes. The biggest ones have been the creation of various frameworks like RightScale, Puppet, etc. and the switch from doing things manually to the more automated processes we see now.
  • Since we began, automation has become the way to do most of DevOps. Many manual processes like testing, configuration and deployment have become automated with various tools. Also, with the advent of the cloud, DevOps automation has become cloud-centric now.

Here’s who shared their insights with us:

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DevOps Enterprise Summit Las Vegas 2018 — The Best Yet?

Its already over again — the annual get-together of the brightest DevOps minds (well, the brightest who could make it to Vegas). And in this instance, I want to make sure that what happens in Vegas does not stay in Vegas by sharing my highlights with all of you. It was a great event with a slightly higher focus on operations than last time.

The four trends that I picked up on:

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Keys to DevOps Success

To understand the current and future state of DevOps, we spoke to 40 IT executives from 37 organizations. We asked them, "What do you consider to be the most important elements of a successful DevOps implementation?" Here’s what they said:

Culture of Collaboration

  • It’s not just about CD and automation. Collaboration between developers and executives in middle and upper management about managing and working with employees with CI/CD and automation, organizing into teams of eight to 10 working from concept to delivery. How you think about and plan it with the minimum viable product in mind. Experiment, measure and prove hypotheses. Have moon shots and chunk work into iterations. 
  • Automate everything: Builds, deployments, testing, reporting, everything. Ensure alignment across teams: Dev, IT / Ops, QA, Product. DevOps, by definition, bridges across development all the way through to operations. Go fast but maintain control. It’s not the wild west; teams need to make fast decisions and move quickly, but everything needs to align to the strategy.
  • For many companies, when they talk about DevOps, they primarily focus on developers and on the technical side. They tend to forget about the cultural and people part, as well as the operations part. But DevOps transformations really require bringing developers and operations together and connecting them, not only from a technology point of view but from a collaborative standpoint. They need to work together to figure out how to do things like push code to production or fully integrate their tools. They also need to develop a mutual understanding of what each side does and to feel mutually responsible for each other. 
  • Start with culture first and envision how the team interact to achieve the goals. Then you need to articulate the goals. No combination of tools will make you successful with DevOps. Definitions and understanding are all over the map. DevOps should not be a designated team lest you are setting up another silo. It’s a philosophy or movement rather than a methodology. Philosophical way of thinking about building better software faster. 
  • Have the workflow and ownership well understood before using tools. Plan out process and workflow. Know who owns what part of the infrastructure. Have autonomy to make decisions within their teams. Make sure boundaries are well defined. 
  • It’s a matter of people more than technology. The real benefit starts from the ability to make the team more agile and break teams into smaller units to be more nimble and productive. The same way microservices starts with people. Adopt the right organizational changes that can be supported by the right technologies. 
  • The most important element of a DevOps implementation is transparency. The DevOps philosophy is founded on the assumption that coordinated collaboration is the best way for businesses to innovate and grow. DevOps programs enable businesses to have realistic expectations and align incentives between the operations, development, and security teams. In an effective DevOps program, everyone in the group understands their role within the project which paves the way to a smooth rollout. 
  • Start with CI and then move to CD. Legacy has been a big focus to help those enterprises through a bigger transition. CI is just one step. Doing all tests manually, you realize the need to automate to be able to scale. Parallelize tests so you can run concurrently. Container and headless tech enable you to run high value faster and move further left. Legacy still doing 90% manual tests. How to think about automating. Do you need to run all of your tests? Change management requires organizational changes. Need to determine what good looks like. A lot is change management and you need support from the top. 1) Right Culture and Mindset: Successful DevOps need to be followed in the entire organization right from the executive leadership down to everybody in the Dev and IT departments. You can’t just do DevOps in a silo without Product, Marketing, Engineering, Finance, Sales and Exec Teams being affected by it. 2) Right Tools: Leverage DevOps tools: Source control management:  Jenkins, bitBucket, etc.; Database Automation:  Datical, DBMaestro, etc.; Continuous Integration (CI): Jenkins, Travis CI, etc.; Configuration: Chef, Ansible, Puppet, etc.; Deployment: Terraform, AWS CodeDeploy, etc.; Containers: Docker, Kubernetes, etc.; Release Orchestration: CA CD Director, OpenMake; Cloud: AWS, Google Cloud, Azure, etc.; AIOps: Splunk, Sumo Logic, etc.; Analytics: New Relic, Dynatrace, etc.; Monitoring: Nagios, Zenoss, etc.; Security: Checkmarx SAST, Signal Sciences, etc.; Collaboration: Jira, Trello, Slack, etc. 3) Right Database: Move to database technologies that scale out (and not scaleup) since you never know when demand will go up and you can’t build for some future state. 
  • 1) Culture and buy-in are key when launching DevOps, as are tools and processes that ensure your team is able to maintain consistency and reliability from the first lines of code to scaling an app. From a cultural perspective, teams need to embrace new ways of working – think truly rapid iteration and smaller releases rather than ‘big bangs.’ Developers must also shed harmful habits that seem like best practices “because we’ve always done it that way.” One example: holding on to the idea of “never deploy on Friday” that prevents teams from fully embracing a model that allows for safe releases at any time, even the most critical ones. Another example: keeping humans in the loop. Intuitively it seems like a good idea to have that last step before a release is sent into the wild controlled by a person at the keyboard, but if there’s one thing humans are good at, it’s introducing variability and making mistakes! Trust the “robots,” and ensure you can automate from end to end. 2)  Provide tools to app dev teams practicing this sort of DevOps. Code and infrastructure config are managed from end to end with the same tool – Git – and “robots” take care of turning straightforward config files into complex container-based infrastructure. To help teams focus on what matters (their app!) and move faster, we create perfect clones of production for every branch nearly instantly. 
  • It may sound cliché, but culture is the cornerstone of success. Culture drives collaboration and sustainable practices. It’s not easy. Collaboration can be hard work, but you see results over time, and people seem both happier and more productive in their work. 
  • The primary element of a successful DevOps strategy is breaking down the walls separating individual teams within IT and encouraging active collaboration across teams. At the end of the day, the most desirable outcome is to deliver business value to users. Today, this can be better facilitated with cross-team collaboration between developers, testers, etc. This way, teams will be empowered to continuously develop, test and improve with real-time feedback. Traditional techniques are still largely relevant in various phases of production and deployment. However, the need to automate and upgrade with evolving technological developments has to be realized wherever possible.

Real-Time Feedback

  • Outcomes with a feedback loop from internal and external customers for developers to get faster feedback on features and bugs. Looking for gaps and bottlenecks. DevOps Dojo concept aligns a space where people can become immersed in the DevOps culture. Establish an enterprise toolchain and allow people to use and flex outside as necessary. 
  • Improving the value stream starts with visualizing the value stream. Begin by creating a visual baseline of the value stream including process time, wait time, percent complete, and accuracy with efficiency KPIs. Capture work-in-progress – how much is there and how does it affect what you’re doing. Ensure the work you are doing as part of the delivery pipeline is tied back to planned work and business value. A pie chart that shows number of commits to source control, changes show what’s delivering business value and what isn’t. In order to measure the effectiveness of what you are doing, you need to match the change to what you are doing to planned and unplanned work. DevOps in the delivery pipeline were a black box. Provide more players KPIs and information to non-technical players so know how well they are performing.

Mutually-Agreed Upon Metrics

  • Release something. Start small and get some quick wins. You need collaboration and visibility into the entire toolchain even while using multiple different tools. Without that, you’re creating silos more quickly than when doing manually (most common fail). Doing automation without fixing the process or culture and respecting the need to have visibility across the value stream and CI/CD pipeline. Understand the KPIs and metrics to know if you’re getting better. Another fallout from Jenkins World is pipeline visibility and management. No one is watching everything embedded in scripts. How to integrate a DevOps pipeline with a legacy pipeline. If you can’t measure you cannot manage and continuously improve. Look at the delivery pipeline as a system. Four challenges of scale: 1) visibility of the portfolio, 2) fragmented management, impedance management between the old world and new world, looking at the delivery pipeline; 3) continuous improvement needs to be measured and valued. Getting to the metrics of the delivery. Be able to answer whether or not you are doing better than before. How are you solving problems – not giving up, getting stuck, and slowing down? Looking at other methodologies like value stream management, tools to measure application delivery, visibility into the process. 
  • Quantify quality. Put DevOps for a repeatable process and can track metrics. Measure and ensure meeting KPIs. Put in procedures and process to ensure every test is repeatable and scalable. Take a baseline and deploy over and over again. Get a baseline and anything about that is your metrics coming in. Need data to qualify a particular. 
  • Continuous Integration (CI) and Continuous Delivery (CD) are often components of DevOps implementations, but they are not one in the same. DevOps is much more about culture and process than tools. It’s easy to think a DevOps tool or solution is a quick path to the promised land when, in fact, it’s much more about how individuals on a team, and teams themselves, work together with a focus on quickly, efficiently and securely delivering business value. In order to have a successful DevOps adoption, companies must make sure team members represent the appropriate functions and are moving toward the same goal. This starts with effective communication and common incentives. Development and operations teams already have many common goals like security and customer satisfaction, but in the past, they were often motivated by different concerns; development teams focused on software quality and on-time delivery, whereas operations teams centered on system stability and uptime. Aligning these mindsets is extremely important. If these cross-functional teams have more business value-focused ways of measuring their performance like feature lead time, mean time to recovery and deployment frequency, then they’ll have something closer to a common definition of success. When that common definition exists, teams will start to experience success together, helping to remove some of these barriers to adoption. 
  • 1) Share the same goal: The key to successful DevOps is to change the way different functional units work together. Therefore, teams should have a shared goal instead of having siloed or even contradictory measurements for different units. 2) Make things simple: Automation is one way to simplify the whole process, but we often can do much better by abstracting away complexity when necessary. For example, we ensure applications are decoupled from the baseline operating system configuration, so those can be changed or updated without a corresponding lock-step change in the applications that are deployed on those clusters. Decoupling infrastructure changes from application changes has allowed us to work towards reducing heterogeneity in our data center, which is a key aspect of their overall maintainability. Similarly, we focused on the introduction of Infrastructure-as-a-Service for commonly needed platforms, such as Kafka, Kubernetes, Spark, Solr, and Hadoop. This allows us to create teams with expertise in those particular technologies to deploy them at scale. This way, the application teams simply need to go through a self-service provisioning interface to use them. This approach allows us to benefit from a centralized team for those technologies, while the self-service approach ensures we don’t create artificial barriers to the adoption of those technologies. 3) Measure your success: This element is critical to close the loop from setting up the shared goal. To make this possible, we have built out a comprehensive metrics and monitoring platform. This has enabled us to get much greater insight into the performance and behavior of our services. It also provides developers with an easy way to configure monitoring at the application level, which has given our teams more confidence to deploy software quickly. Beyond system metrics, we also include measurement of business goals, such as time-to-market and the end-user experience. 
  • It’s important to make sure you include the assumption of the ephemeral nature of cloud computing into your implementation. All resources are temporary and expendable. Implementation has to be a process for keeping those resources available even if pieces of them go away. Instead of going in and installing everything we need on hardware, we need to set up a process and automate it so we can recreate what we’re working on quickly and easily. Another important element is to think about emergency prevention, rather than focusing on an emergency response which is what most organizations tend to do. We’ve thought a lot about choosing the right tools and it’s important for us to do research in that area. Choosing the right tools for server management and log handling is a big deal. Being involved in infrastructure design with regard to how our application scale-up is also a big part of what makes DevOps run smoothly. 
  • Successful DevOps implementations must start with culture change to build a collaborative culture across Operations and Development that aligns goals and priorities. When operations teams are solely focused on 100% service levels, and Development teams are driving to innovate faster, there can be considerable friction and mistrust. Being able to integrate objectives into a shared purpose is key to capitalizing on the benefits that a DevOps process can offer. For example, a goal that includes a shift in focus from simply “running infrastructure” and prioritizing “uptime,” to ensuring true application performance and an end-user experience that exceeds expectations, combines the competing goals of uptime and innovation. When an IT team is aligned on these two factors, they naturally collaborate, consider performance metrics differently, and find new ways to work together seamlessly. For successful DevOps integration, which includes transitioning to metrics of resiliency, recoverability, and change velocity, Operations and Development teams must agree on a common set of performance measurement and reporting approaches, and fully accept that old up-down, percent-utilization obsessions must yield to measuring the end-to-end experience of end users. Skills to manage and understand in-browser metrics, API performance, and metrics derived from events, alongside learning to trace transactions instead of “watching the stack,” are all key to cooperative DevOps service delivery.


  • Look at the surrounding technologies Pytorch or Tensor Flow. When you look at containerizing and look at versioning people understand they can version with Git and it works. With 50 GB data in deep learning. It’s big to be able to go back to the data and see what you run. Can’t make multiple copies of 50 GB databases. Copying between NAS and server you have to wait. More core copy to GPU and push back to NAS or SAN and lose lineage. Execute a snapshot and collocate GPUs can snapshot and keep a version including your Git repository. Don’t have to copy everything everywhere. Customers with 10 GB ethernet need more local data. No one has infinite bandwidth. Deep learning requires more than a TB of data. It will take time for the process to complete. It’s even slower as more people start using big data. We can optimize in either direction – GPUs local or not. Boxes only with GPU as edge nodes. Given the circumstances and size of storage, you can guarantee data at your location. Co-locate enough data with the job. Less contention for network bandwidth. Need flexibility in these solutions. If have to rearchitect you will feel even more pain.
  • Database change in many organizations is treated differently than the rest of the application state. Take a step back and review database code changes instigated by application developers, performance tuning, maintenance is separate when looking at end-user revenue-generating applications. The developer will take the first pass at a change and after that, the visibility is lost. Sent to the DBA over email for ticketing, ServiceNow or JIRA, goes through modification when and where deployed can change. Can reliably guarantee what developer wrote is not being deployed. Treat database code as all other code. Treat database code as application code. Recognize the need of operators is different than the needs of developers. A lot of database tooling hasn’t evolved from the old school DBA. Tools have been built around the operations side of the business. Going from glacial change to agile app shops. Database code changes as part of that. You need a DBA with the right intelligence to make it easier for developers. Talk about core principles and treating DB code as app code.
  • Focus on the performance and analytics of applications. How to guarantee performance and metrics? Simplicity – don’t over-engineer things. Let the application determine what you need. More components result in more opportunities for failure.
  • Any software company needs to understand how operations work so software is appealing to the IT professionals and be able to partner with the tools and cloud. Understand where you are today, where you want to be in the near future and be practical about how to get there. Most important is to get there.
  • Turn vertical silos into horizontal. Organize people by the process designed by the tools you are using. What are the processes to focus on automation and the pipeline? DevOps is not a silver bullet.
  • One of the biggest pain points for DevOps and integration is there are a lot of proprietary black boxes with a specific dedicated process for integration assets versus software it means your pipeline is broken. It slows you down and makes it impossible to integrate. You need a DevOps toolset to accommodate the main process.
  • Speed and agility with control. Quality, secure, always available. Educate and get people to understand DevOps helps them get there. Meet requirements of quality expectations — speed  with control.
  • The most important element is automation. Automate as much as you can to get the value from the methodology as quickly you can.
  • DevOps is a cultural and process change. Implement aspects of what role we believe monitoring plays in DevOps and the cloud – monitoring as a service wherever you deploy. Performance as a service – pulling out metrics to create the unbreakable pipeline. Bring toward self-healing for auto-mitigating actions. Depending on where customers are we help implement the next steps to self-healing and no ops or autonomous ops. Determine what stage they are and where we can help get faster-automated feedback to the right people.
  • The fundamentals remain just be more aware and open to dialogue. There used to be more pockets of information, now more at a global level. Leadership from the top down need to embrace DevOps. It will require some rewiring. You should not shock the team. Create a culture of discovery and fail fast. Failure should not mean punishment. There is no way to discover a process if you’re not going to fail. It’s about failing fast. 
  • The cultural shift to an organization that focuses on learning, experiments and continuous improvement has to be the most important element for sustained success.  A successful DevOps implementation is never finished. Its ongoing, constantly improving the “system” by developing capabilities that support the success of the business.
  • Good automation, definitely. If it’s in an existing environment, it’s important for your engineers to really understand all of the processes that occur regularly so they can be automated in a custom fashion. In new environments, it’s often better to look at what best practices exist in the marketplace and if there’s existing (often free) automation you can use to implement those best practices.
  • The notion of DevOps is tied very closely to several other ideas, as such DevOps is something that can be embraced piecemeal as each of those related ideas come together. Containerization of code, new microservice-based application architectures, modern and automated application delivery processes, changing organizational structures – they are all part of a major, industry-wide transformation around the way applications are delivered. To be successful using DevOps to transform application delivery, enterprises must recognize that this is a significant change, but not one that needs to be implemented all at once. They must understand the big picture and have a clear vision for how they can bring these different elements of cloud-native together in a rational way. Taking the journey one step at a time, starting small and expanding from points of local success, enterprises can slowly build the expertise they need to embrace DevOps on a large scale. An enterprise might start by containerizing legacy code to streamline dev/test and improve portability. Then they might leverage Kubernetes to automate deployment and ongoing operation of that containerized code. Eventually, they might start to experiment with a simple microservices-based application, creating a small DevOps team for each microservice and giving each team end-to-end responsibility for its microservice through its entire lifecycle. As competencies build and rewards of the approach become clear, the enterprise may begin to take on larger projects, building microservice-based applications, transitioning from functionally siloed organizations toward smaller DevOps teams, and extending agile processes across the complete application lifecycle. The traditional application can also be modernized along the way, by adding new features as microservices using DevOps team and processes. The full transformation will take time, but significant gains can be realized at each step along the way, ensuring that each phase of the journey is worthwhile in its own right. Ultimately, enterprises that embrace application delivery transformation, including DevOps and these many interrelated components, will become more agile and more innovative while improving efficiency and the bottom line.
  • Moving to DevOps can be a huge undertaking, especially for organizations with a lot of legacy applications and technology. Many companies fail to reap the many benefits because making a big move at once requires substantial changes to culture, processes, and tools. I recommend IT teams make smaller, more strategic moves to ensure successful implementations. Start implementing DevOps with skunkworks teams for each new project, a new application for instance, instead of trying to force the modern development process for older applications.

Here’s who shared their insights with us:

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DevOps Enterprise Summit 2018, Day One Recap: The ”Project to Product” Movement

In his opening remarks to the Las Vegas iteration of DevOps Enterprise Summit 2018, Gene Kim set the tone for Day One by providing an alternative definition of DevOps via Jonathan Smart (formerly of Barclays): “Better Value Faster, Safer, Happier.” At the London show in June, Jonathan expanded on that by extolling the pressing need to go beyond Agile and DevOps to think more end-to-end about how we build products.

DevOps legend Gene Kim kicks off proceedings with aplomb.

Four months later, amid the glowing neon lights of Sin City, that key message has evolved and intensified. Traditional organizations are moving away from a project approach towards a product-centric model, and focusing on the flow of work and business value across their value streams. That sentiment was echoed across the show floor and the stage as the DevOps and wider IT community considered how to take DevOps and enterprise software delivery to the next level. How do they transform their business in the Age of Digital Disruption? How does IT deliver more value to the business?

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Do You Really Need Enterprise Release Management in a DevOps World?

In the enterprise, application delivery has always been challenging. Enabling teams across all lines of business to converge for production is no mean feat – particularly when those teams are so disparate they might as well be separate companies. But what is the unified goal that binds these teams together? Increasingly, it appears to be the directive to "go faster" and ultimately to deliver software more quickly.

A proven methodology to achieving greater speed at scale is DevOps. If you can make Dev and Ops work better while breaking up delivery into smaller chunks, then you’re good to go. An integral part of the road to improvement is technology-driven; organizations need to adopt agile technologies in order to increase operational efficiency. However, strong communication and collaboration are also key necessities to make the concept a reality.

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Why Is DevOps Becoming Mainstream in Software-Powered Organizations?

Early DevOps practitioners have shown DevOps to be more than just a cultural aspect or a set of tools – they have confirmed it to be a crucial success factor and a competency well worth developing in today’s environment of rapid evolution, technological advancement, and huge customer or employee expectations. The demand for DevOps in organizations is high and need of the hour, but it is not something that can be adopted on to the average team just like that. When this happens, the current organizational undercurrents will weaken the effectiveness of such a program. Rather, the development, operations, and overarching management processes must be redesigned anew and from the scratch. DevOps can be profoundly disruptive to a business, it has an enduring and strong impact on organizational success. After all, IT is the core of almost any business and the effectiveness and agility gained there will have a notable impact on the readiness and coordination of the organization as a whole.

The term DevOps has entered into our general language and has gathered much attention and focus these days.

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How DevOps Is Transforming the World of Software Development

DevOps, as we all know, it is the word on the lips of every software enthusiast around the world. DevOps has come a long way and is taking center stage in the world of software development.

What Exactly Is DevOps?

DevOps is a cultural methodology in which professionals are groomed to adapt to a new and emergent environment in software-powered organizations. Teams are trained to follow a specific set of rules, steps, and tools to achieve success in DevOps. When it comes to DevOps adoption, having the right attitude, employing the proper tools, and learning the stages of the DevOps cycle are imperative.

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Questions to Ask to Accelerate Your Digital Transformation

The journey to digitally transforming your company is a long one, with plenty of roadblocks and dead ends along the way. However, there’s a way to speed up the journey — ask the right questions before you set out.

MIT Researcher Stephanie Woerner offers advice on the right questions to ask in an interview on the Knowledge@Wharton website, "Six Questions That Can Help Guide Digital Transformation." Woerner and co-researcher MIT Peter Weill wrote the book, "What’s Your Digital Business Model? Six Questions to Help You Build the Next-Generation Enterprise," based on several years of study at the MIT Center for Information Systems Research.

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How DevOps Can Boost Efficiency in a Business Budget

IT budgets are getting increasingly difficult to secure. Individual IT teams are trying to grab a piece of the pie. However, at the same time, more and more pressure is on the IT department to improve efficiency and performance without raising the cost.

This has led to the development of DevOps, a shift from siloed IT teams to consistent and collaborative teamwork in IT groups. This has bought a change in the way IT teams function and operate and has supported the improvement of cloud hosting. Let’s review some ways in which this transformation can help in meeting enterprise goals and objectives on a constricted budget.

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Dos and Don’ts of DevOps: How Not to Bite Off More Than You Can Chew

Many businesses are hell-bent on doing some kind of transformation. While DevOps is definitely not as ubiquitous as digital or agile transformation (17% of software developers have fully embraced DevOps in 2018), the hype is real. If you aren’t doing DevOps in your IT organization, you’re not taking advantage of such benefits as continuous software delivery, faster problem resolution, more productive teams, improved communication and collaboration, and so on. At least the talk goes like that.

While there’s no denying that DevOps is good for basically any company, many IT organizations bite off more than they can chew when kicking off their DevOps transformation. Some of them try to do everything at once, pushing their teams into disruption and dissatisfaction. Others simply cherrypick certain practices that they like and end up with dysfunctional “pockets” of DevOps.” Of course, this results in misaligned pipelines, broken down processes, and burned-out employees. And there are many other reasons for DevOps failure.

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The Need to Change Our Mental Models — A Core Idea Behind DevOps for the Modern Enterprise

I always loved this quote: "Nothing is more dangerous than using yesterday’s logic for today’s problems," which shows you that you just cannot afford to get lazy and do the same thing again and again. This causes larger problems when you scale it up. Gary Hamel summarizes the problem our organizations face as follows: "Right now, your company has 21st-century Internet-enabled business processes, Mid 20th-century management processes all built atop 19th-century management principles."

One of the main reasons for me to write "DevOps for the Modern Enterprise" was to help address this mismatch between the work we need to do, creative IT based problem solving, and the management mindset many managers still have, that of IT being managed just like manufacturing.

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Getting Started With Ansible Tower

Today’s scaling industries aim to provide large productivity gains, but they have to deal with a wide variety of automation challenges, which are overcome by tools such as Ansible. Let’s start with what Ansible Tower is.

What Is Ansible Tower?

Ansible Tower is Ansible at an enterprise level. It is a web-based solution for managing your organization with a very easy user interface that provides a dashboard with state summaries of all the hosts, allows quick deployments, and monitors all configurations. Tower allows you to share SSH credentials without exposing them, log all jobs, manage inventories graphically, and sync them with a wide variety of cloud providers.

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2018 Accelerate: State of DevOps Report Identifies Elite Performers

Every business will be a software business. Software is the backbone of an organization and dictates its ability to innovate, improve and react to changes in the market. That puts a big burden on the company’s IT organization to deliver high-quality software quickly and frequently.

Continuous delivery (CD) performance matters not only for IT organizations but also a company’s ability to be competitive. Indeed, the DevOps Research and Assessment (DORA) released its Accelerate: State of DevOps 2018 report which details the correlation between software delivery and organizational performance.

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Starting the DevOps Journey [Video]

In this webinar, Steve Jones, a Microsoft MVP, welcomes Bob Walker, Solution Architect at Octopus Deploy, to discuss how taking a DevOps approach paves the way for staying ahead in business.

What’s covered:

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How DevOps and the Cloud Are Perfect for Business Success

Most organizations understand that to increase competitiveness in this rapidly changing world, it is essential to obtain digital transformation. DevOps, as well as cloud computing, are oft-touted as the crucial ways for companies to achieve their needed transformation. The association between these two is confusing as cloud computing is about technology — its tools and services — whereas DevOps are about the processes and their improvement. Not mutually exclusive, it’s crucial to understand how DevOps and cloud work together, helping businesses achieve their transformational goals.

Agility is the core component of this relationship and DevOps behind the agile methods provides the automation. Traditional platforms need weeks or months of planning for the necessary software and hardware. The automated provisioning with virtualization of these resources can’t quickly be done on demand.

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Self-Assessment: Are You Already Doing SAP DevOps?

DevOps is a key element of many enterprise IT strategies today as digital transformation drives the need for greater efficiency and higher speed. However, teams responsible for Enterprise Resource Planning (ERP) software like SAP sometimes feel like they’re not surfing the same wave.

Do you think that DevOps isn’t for you? That it’s not relevant to SAP? Perhaps you understand the value and are trying to achieve a culture of DevOps for SAP, but find yourself unsure as to whether you’re succeeding? Maybe you’re even reading this thinking "What is DevOps?" You might want to check out a previous blog I wrote for more on this and about laying the foundation for DevOps success.

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It’s Time for Leadership to Own DevOps

DevOps has become more than a trend—it’s a survival imperative for the enterprise. In today’s digital economy, software innovation drives business innovation.  The faster developers can deliver on the next wave of software innovation, the faster the business can deliver customer value, bring new revenue streams online, and respond to market events. DevOps practices across the enterprise can deliver business results at the speed and quality customers expect.  

Many IT organizations start their DevOps journey implementing automation and tools only to quickly face hurdles when trying to scale DevOps practices across the organization.  Their journey starts to take a detour as they struggle with organizational boundaries, unwieldy system-wide processes, and cultural resistance to change. It’s common to blame the people and teams that are not getting on board, but to quote Edward Deming, “People work in the system, management creates the system.”

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Rolling Out Nexus in a Large Organization

With over 80,000 employees, 150 million customers a year, and 800 aircraft with service to 57 countries, there is no denying the size and reach of Delta Air Lines. And, with a 94-year history, you can imagine, they have lots of legacy technology.

This creates challenges, of course, but times are changing, and Chris Bolton, an application developer, and Jasmine James, a development tools engineer, teamed up at the 2018 Nexus Users’ Conference to discuss how they rolled out Nexus at Delta.

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DevOps Technician Training: Think Requirements, Not Solutions

This is the eleventh in a series of blogs about enterprise DevOps. The series is co-authored by Nigel Willie, DevOps practitioner, and Sacha Labourey, CEO, CloudBees.

By nature, technicians are problem solvers. Many companies assess an individual’s ability to solve problems as a precursor to an employment offer.

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Continuous Discussions (#c9d9) Podcast, Episode 90: Gene Kim and DOES’18 Speakers [Video]

This morning on our Continuous Discussions (#c9d9) podcast, we had a great discussion with a panel of DevOps Enterprise Summit Las Vegas 2018 speakers and programming committee members to discuss a major theme top of mind for many at the fifth annual USA conference.

This year, "Next Generation Ops and Infrastructure Practices" gets moved under a larger magnifying glass. Targeted specifically at Ops leadership, and the beginning stages of a greater focus on a multi-year roadmap to properly define the problem space, we continued the discussion from DOES18 London here with some of the top minds in DevOps.

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Why DevOps Requires Buy-In From Everyone

In DevOps, culture is as important as the process and tools. That’s why buy-in, from everyone, including the CEO, is essential for success. Even organizations that clearly recognize the business value of adopting a DevOps approach may face a variety of potential stumbling blocks. One of the most prevalent challenges is inherent in the divergence of what developers and operations professionals prioritize and value most-and the traditional development practices that amplify those differences.

Development is concerned with speed and agility while operations is focused on quality and stability. As a result, there are often organizational barriers between the development team creating new software and the operations team responsible for pushing changes to production. Some of those barriers are by design, even though they often bog down processes and create tension between the teams. Most organizations tend to be risk-averse. They don’t want to risk compromising quality or security for the sake of speed, so legacy software development processes and tools are designed to reduce risk and ensure quality, not for speed and agility.

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Transformational Leadership and DevOps

The following is an excerpt from a presentation by Dr. Steve Mayner from Scaled Agile, titled “Transformational Leadership and DevOps.”

You can watch the video of the presentation, which was originally delivered at the 2017 DevOps Enterprise Summit in San Francisco.

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DevOps at Nike: There Is No Finish Line

The following is an excerpt from a presentation by Ron Forrester and Scott Boecker from Nike, titled “DevOps at Nike: There is No Finish Line.

nike-does-us-2017You can watch the video of the presentation, which was originally delivered at the 2017 DevOps Enterprise Summit in San Francisco.

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Augmenting the Org for DevOps

The following is an excerpt from a presentation by Stephanie Gillespie and John Rzeszotarski from KeyBank, titled “Augmenting the Org for DevOps.”

You can watch the video of the presentation, which was originally delivered at the 2017 DevOps Enterprise Summit in San Francisco.

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The Five Key ”-tions” of Enterprise Scale Continuous Delivery

For a large organization, implementing and successfully adopting continuous delivery (CD) within an app system is a challenging, even daunting, task. The idea of overhauling a preexisting way of working can seem like taking a leap into the dark, which often causes executives to balk at the idea. Yet, the jump is not actually that large. The technical issues are well understood and there are plenty of tools available to get a basic, functional infrastructure ‘working’ with a bit of effort.

However, ‘working’ is not enough for modern enterprises. Large organizations are concerned with higher level capabilities and operational approaches that reflect the value of the actual scale at which they exist. These companies want to maximize and exploit the advantages of their size. Enterprises that cannot take advantage of their scale will struggle as competitors and market disruptors alike seek to alter the status quo.

How does the enterprise attain and deliver on the promise of CD at scale? To answer that question, I’d like to look at an interrelated list of “-tions”:


There is no escaping the fact that large enterprises are concerned with who does what. Old-school hierarchies usually think of that in terms of pushing “authority” to do something “downward.” Indeed, this is the way most people think of the word ‘delegation’. In organizations attempting to adopt enterprise scale continuous delivery, there is also the challenge of horizontal delegation-from one team, or silo, to another. Continuous delivery only works when long communication, decision-making, or approval-granting chains are minimized or eliminated. This can be a challenge for enterprises that are used to very bureaucratic approaches to IT. Delegating control to those closest to the work is critical if the organization is to achieve agility.


Collaboration is closely related to delegation. It is an important enabler of trust, which enhances the willingness to delegate. It also incorporates key communications channels and behaviors that let people learn, share knowledge, and generally improve the organization’s ability to execute rapidly. Maintaining a rapid execution cadence is a key goal of CD and ultimately a reason enterprises are interested in it as a practice.

Large organizations, given their disparate teams, technologies, and even geographies, must deliberately invest time and effort into fostering these interactions. Merely saying people are ’empowered’ is not enough-goals and incentives must be aligned within and, crucially, across organizations in the same value streams. Otherwise, team-centric interests can interfere with delivering new functionality.


Automation is the capability that makes enterprise scale continuous delivery possible. It is the backbone of a modern software factory.

Automation serves as a source of great efficiency for repetitive tasks, executing them in a consistent, fast and reliable fashion. This shifts staff time from repetitive, low-value tasks to creative efforts that bring higher business value resulting in greater productivity for the business.

Successful automation relies on clear, cross-functional understanding of the overall process and its value increases the more broadly it is used. Good collaboration is crucial to achieving the understanding required to establish and extend automation within processes-particularly those that cross team boundaries.


Being a large enterprise enables you to leverage scale and achieve higher levels of efficiency. This is achieved via shared resources and reducing duplication of effort, which can be a major management challenge for enterprises because, as we have discussed, CD pushes a lot of activities to teams. This creates pressure in the enterprise as CD practices can lead to teams duplicating facilities that other teams have created.

However, the knee-jerk reaction to deduplicate efforts may actually yield a worse outcome for the enterprise as a whole. There is mounting research that the traditional pursuit of efficiency will generally yield worse business outcomes when it comes to delivering software. That said, too much duplication will also yield negative outcomes-remember that even a high-speed tech titan like Google enforces a single code repository standard because that is how it manages its intellectual property.

Striking the correct balance requires good automation. By eliminating repetitive tasks, it can empower teams to use a central resource in a self-service manner. Knowing what is critical to maintain centrally, such as intellectual property or compliance, is a key part of intelligent delegation.


The first four “-tions” are all related to each other and there is a delicate balance required to successfully adopt CD. The fifth “-tion,” Instrumentation, sits at the heart of the CD practice and enables all participants in the enterprise to contribute to its success. Instrumentation provides the trust, transparency, and communication required to maintain and improve the CD practice in a sustainable way that benefits a large enterprise.

The 5 “-tions” outlined above serve as a framework that can help focus conversation and effort around enterprise scale CD. It can be a tricky balancing act, but the research published in books such as Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations, by Nicole Forsgren, Jez Humble and Gene Kim, reflects that companies that successfully adopt modern practices such as CD will substantially outperform their rivals.

Enterprises that want the benefits of DevOps practices such as CD must act deliberately. Early successes from individual teams or “grassroots” efforts are good and provide real data that such approaches are feasible. However, without a coherent framework such as these 5 “-tions,” scaling the early successes across the numerous and diverse teams within an enterprise will be impossible.

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Hot Shot 007 – Decent DevOps Defined [Podcast]

Hello there, once again, and welcome to another hot shot. My name is Peter Pilgrim, Platform engineer and DevOps specialist, and Java Champion.

What is a definition of decent DevOps? Here is a simple explanation. It is a combination of software development skills with that of a system administrator.

If you know how to program and very well, then you are similar to me, then your approach to DevOps is closer to that of a tactical and top flight software engineer.

You most likely have been programming application software almost all of your life. You can write decent quality and productive programs, impressive ones to boot. You possess creative skills, you already know plenty of programming languages from experiences. You might be efficient in high-level programming on the JVM such as the mother of them all, Java. You already might have an alternative language under your belt, such as Groovy, Scala and even Clojure. If it is not the JVM, then you have experience of native programming such as C / C++ from way back in the day. In the modern terms, you might have developed using GO or RUST. If you have no JVM experience, then you might have come into this from the Microsoft community with C#, Dot Net and if you are truly hipster, you might have Ruby experience.

(These notes are a little bit more off the cuff than usual — my apologies)

System administration is to ensure that all related computer systems and services keep working, available for use by the business (a system can be a website, it can be a persistent store, LDAP and a suite of core applications).

A system administrator, or sysadmin, is a person responsible to maintain and operate a computer system or network for a business. The role of a system administrator can vary a lot from one organization to another. For example, inside one organization, such as a forward-looking financial institution, you might be looking about AWS and PaaS solution. In another organization, such as a utility energy business, you may only have to deal with a traditional RDBMS and Java application servers.

In the old school way of thinking, classic System administrators are usually charged with installing, supporting, and maintaining servers or other computer systems, and planning for and responding to service outages and other problems. In the new school of thinking, especially with the cloud computing, system admins are heavily involved in automation features, monitoring and eventually site reliability.

System administrators share programming duties with their classic software engineering folk. Practically, every system administrator knows how to program. They know how to test code (whether test-driven or not). Other duties may include scripting or light programming, project management for systems-related projects, supervising or training computer operators, and being the equivalent of a handyman for computer problems beyond the knowledge of technical support staff.

I didn’t have an operational background, but throughout my career, I had to dig under the hood of the computer, to install databases, install and understand operating systems, such as Linux, various distributions such as SuSE, Red Hat and Ubuntu. I even stuck the CD ROM into machines into install Solaris. I learned the first thing about continuous integration by configuring Jenkins, web server management, application servers, database management, bug trackers, setting up fundamental security for users and learning to program using scripting languages. (*Skipped* ad-libbed at the beginning).

So modern DevOps encompasses both software engineering and system administration ideas. These are reasons why people and businesses are into DevOps:

  • Reduce the communication gap between engineering and administration.
  • Avoid Unicorn businesses with silos
  • Become proactive rather than reactive (especially if one adds Security to DevOps)
  • Improve code quality
  • Generate production code with very little downtime
  • Share knowledge and competency between individual

Notice that I say very little about productivity in the above bullet items. This is because, DevOps if organized and executed with the correct procedure, attitude and organizational freedom ought to provide better visibility, transparency, enhanced feedback and human teamwork. The road to DevOps can be mudded severely through corporate misunderstanding, cultural organization, and other economic factors. DevOps is easier to “install” in a new young company, division, or department. It is far harder to provide the function is organizations with legacy technology dependencies and waterfall software development process and methodology.

From the business point of view, they are already might understand two concepts: SLO and SLI.

SLO – service level objective – this specifies the SMART goals that define business application reliability. These objective define the minimum requirement for business operations to function in order to do the productive work. It might be building the next fleet of cars in a manufacturing or it could be how many foreign exchange payments will be processed in the month of May, next year. These are achievable goals and not vague and so the idea that I want my website up and running for 24/7/365 is simply not good enough for an SLI.

SLI – service level indicator – this specifies the ways technical, business related to how we can measure through metrics that we are reaching the SLO. These are definite, clear and concise, periodic and reliability measurements that demonstrate how the business application is hitting its set goals. So this means that we have indicators from various analytical tools, prefer dynamic over static, that humans can digest in real time. I am glossing a bit over automatic scaling of cloud runtime instances, but there is no point is having auto-scaling if you can understand the measurement data that you are reading. If you cannot intercept, don’t do it.

This is, for me, the high-level view of what is Developer Operations. It is about building application build pipelines, providing and securely manage in-house and third-party artifact or assembly dependencies, managing persistence datastores, short term and long term (like data laike), it is about providing functionality to monitor applications, analyse the data going in and out of the applications, lifecycle and payload and of course the best is saved for last, the biggest and important one, security protecting user’s data GPDR, and commmercial data.

DevOps and platform engineering are essentially about building an automated pipeline that allows implementation of business logic and ideas to be coded and then deployed to production. Some people will want to throw the C word “continuous” in that last sentence, however, I recommend strongly that you crawl before you walk, you walk before you run.

I think that I will stop here. Bye for now.

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Executive Insights on Automated Testing

This article is featured in the new DZone Guide to Automated Testing: Your End-to-end Ecosystem. Get your free copy for more insightful articles, industry statistics, and more! 

1. The keys to automated testing are to save time and improve quality while realizing the goals and objectives of the organization. Value drivers are quality and velocity, and they need to be prioritized first. 70% of the time, clients are looking for both and it can take multiple meetings to decide what to prioritize. Automation helps customers with a quality assurance process that runs in minutes and hours versus days and weeks, all while removing errors.

The organization must understand its goals. Planning is important. Many organizations are facing the traditional issue of “we’re too busy to improve, we don’t have time to think about test automation, but we know we are going to fail if we don’t have it in place soon.” They need to understand the end goal of doing this kind of testing.

There are four key factors of success: 1) level of automation (must be 90%+ for CI/CD to succeed); 2) maximize coverage of all flows, all datasets, quality of app and UX; 3) creating an efficient feedback loop with data; and 4) adopting an integrated approach and platform for testing across all devices. Organizations need to be able to do end-to-end DevOps testing throughout the SDLC. Testing needs to shift left at conceptualization.

2. The most significant change to automated testing in the past year has been increased adoption. This is attributed to the maturation of Agile methodologies and adoption of the DevOps culture. Agile-driven teams are no longer separating automation from development. Organizations are realizing as complexity increases, automation is the only way to keep up with all of the changes. Also, the continued evolution of tools and frameworks is easier to work with. Enablement tools integrate automatically, so tests can be built quickly.

3. There are more than a dozen benefits to automated testing being seen in six key industries. Automated testing is invaluable for: 1) saving time by running tests automatically 24/7; 2) reporting with daily insights and accurate feedback; 3) consistency and accuracy; 4) saving money; 5) reducing resources (i.e. manual testers); and 6) achieving full coverage. Manual testing can achieve 6% code coverage, while well-written automated tests can achieve 100% coverage. Automated testing is helping organizations achieve continuous integration (CI)/continuous delivery (CD) and is helping legacy enterprises make the digital transformation with microservices.

The industries most frequently mentioned as benefitting from automated testing are: 1) automotive, especially automated cars; 2) healthcare – pharmaceuticals, medical devices, and patient monitoring; 3) telecommunications; 4) financial services– brokerage and algorithmic trading; 5) e-commerce; and 6) the Federal Government.

4. The ROI of automated testing is three-fold: time saved, fewer defects, and greater customer satisfaction and retention. Organizations can see 20 to 40x cycle time improvements by spreading the work across different machines. Going from weeks to days and days to minutes can yield 20x savings. Conservatively speaking, if you find a defect, it takes 10 times longer to fix in production than earlier in the SDLC. Catching errors earlier and more accurately saves a million dollars per year in developers not having to look for errors. For us, automated testing has had a direct correlation with customer satisfaction. The product is simply running better, and the customers are happier.

5. The most common issues affecting the implementation of automated testing is the corporate culture used to doing manual testing. Where manual processes have been used, people need to be retrained and turned into programmers — but management doesn’t want to ruffle feathers. Companies need to shift left and developers need to learn to write tests. Old style testers are not adapting to, let alone embracing, automated testing and AI. We need one comprehensive, automated, visible delivery process to share the feedback from the different tools.

6. The future of automated testing is the use of AI/ML to improve the process on several fronts. Testing will be designed for AI/ML to build predictable models and patterns. Testing will be a natural as writing code, which will be done by machines. AI/ML will be part of the solution as teams generate more data which will make the models smarter. With AI/ML, testing will be faster, more thorough, and will result in self-healing tests and self-remediating code. You will be able to use AI/ML to run every test imaginable in the least amount of time to ensure your code is always vulnerability and defect-free.

ML will also improve automated security testing, as securityteams are able to leverage historical vulnerability data to trainML models to automate the vulnerability verification process,thereby providing developers accurate vulnerability data in nearreal-time.

7. The three primary skills a developer will need to ensure their code and applications perform well with automated testing are: 1) test scripting skills; 2) understanding the use case of the application; and, 3) move left, beginning testing earlier in the SDLC.

Hone test scripting skills. Write small, simple tests. Recognize the different types of tests you will need to run at different points in the SDLC. Have the ability to write your own unit and regression tests. Know how automated tests are going to be written. Learn to write a model type of code from mentors and previous products.

Think about the use cases first and what the purpose of the code is. Understand users, domains, who they are, and what problem they’re trying to address. Have an overarching view of the application, what it’s doing, and how that impacts APIs and services.

Get rid of simple, recurring problems by architecting the system to be tested from the beginning. Leverage the testing methodology from the planning phase, and build richer code earlier in the SDLC.

Here’s who we interviewed:

This article is featured in the new DZone Guide to Automated Testing: Your End-to-end Ecosystem. Get your free copy for more insightful articles, industry statistics, and more! 

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Context Switches in Software Engineering

The following is an excerpt from a presentation by Chris Hill, Software Development Manager at Jaguar Land Rover, titled “Context Switches in Software Engineering.”

You can watch the video of the presentation, which was originally delivered at the 2017 DevOps Enterprise Summit in San Francisco.

A Bit of Background

  • At Jaguar Land Rover, we have about 42,000 employees.
  • We make about 22 billion pounds a year in revenue. (That’s about 30 million dollars.)
  • We’ve got about 135 software groups across 5000 software personnel, internal and external.

I currently head up systems engineering for the current generation of infotainment. If you were to sit in one of our Jaguar Land Rover vehicles that were made this year or last or the last two years, you would use my current product.

Today, I’m going to share about context switches in software engineering, which is probably one of my favorite topics, specifically I want to share:

  • The context of switching in infotainment and at Jaguar Land Rover
  • Some penalties that were associated with the different types of context switches
  • An analysis of the interactions of the software activities between software specialties

Let’s begin.

I like to visualize the software development life cycle in terms of a factory.

You have your inputs.

You have your planning, where your project managers would typically schedule and do your prioritization.

You’ve got your backlog where your change requests and your features come in.

Then you have flow control, whether not to release WIP or release work into your factory.

You are probably very familiar with these stations, requirements, design, develop, validate, deploy and ops. If you can envision all of the software activities that are done, all of the specialties.

10 Years Ago, I Joined a Software Startup

Like many software startups, they were just starting their software product. I happened to be right out of college and I was the only software personnel that they hired. Unfortunately, that means I was in every part of the software development lifecycle. One benefit of operating this way is that I could operate with full autonomy.

That meant I was the only one who could interact with the entire factory, and one thing I didn’t have was the ability to plan on what I worked on.

I may come in at the beginning of the day and think that today was going to be an ops day. I may take an hour for customer needs and wants, and do requirements authoring. When I may have remembered that I’m about 75% of the way done with a bug fix, and realize that that’s a higher priority and switch to that. I may have some code that wasn’t published from last week that I know is ready for release, (this was back in the days of manual deployment,) and so I actually need to do some deploy work. If I’m really unlucky, since I’m terrible at design, I’ll get asked to do some HMI design work, maybe within the next hour.

Unfortunately, every day was a different station for me, and I was the bottleneck at every point of the factory.

Fast forward to JLR, infotainment specifically.

We’ve got a lot more people, and these people could either just contribute at their own station, they could be proficient enough to review other people’s work, they could potentially be proficient enough at going to another station, but typically more people will allow you to scale.

Enter the Idea of Context Switch

Imagine we’re all CPU’s. If we’re working on a current set of instructions, and another higher priority set of instructions comes into the queue, we need to save the current state of our instructions, address the higher priority set of instructions, finish it, take it off the stack, resume the state of the lower priority and finish executing that.

Humans do the same thing.

If I’m sitting at the development station or I’m working against the development station, and I’ve been asked to work on task number two even though I’m in the middle of task number one, if it’s the same project I’m going to consider it a lower penalty.

If you look on the bright side, I’ve got a “Barometer of Penalty.”

The next stage up in penalties is if I ask you to work on a different project.

Which happens to be the same station, happens to be the same type of work but it’s a different project. Now, I need to save the state of my previous task and previous project and ramp up on the next one to finish it if I’m told that it’s a higher priority and that I need to be working on right away. That’s a little bit higher on the penalty scale.

The next one is design, or a different station. If I ask you to do a different station or a completely different work type but I keep you on the same project — I’m hoping you’ll be a little more familiar because you know the project, but this is a completely different type of work so it’s a higher penalty on my barometer.

The last one is if you switch stations, which is your task type, project, toolset, maybe the computer that you’re using, maybe an operating system that you’re familiar with, you could even be asked to go to a separate building, etc. There are many other variables.

If you’re completely changing your environment and your variables, this is very taxing on the human mind. I’m sure we’ve all dealt with this at one point in time or another, but in terms of a CPU, they just have to worry about addition. In terms of a human, you have to worry about all these other variables. It’s almost like asking a CPU to cook me breakfast tomorrow. It has no idea how to do something like that, but at the same time it’s higher priority and I need you to address it right away.

Questions Based on Those Findings on Our Penalties

Should we eliminate all higher penalty context switches?

The answer is, it depends.

We found that if you can actually sustain the capacity and remain efficient on the different specialties, then you can actually avoid these higher penalty context switches with capacity in those specializations.

My favorite question: should we train cross-functional teams or train cross-functional people?

The difference between those is somebody who could work and be proficient at multiple stations, or a team that is capable of sharing empathy, that each one of them can be specialized at their own station.

Which one is more of a worthwhile investment?

Are some stations and roles easier to switch to than others? Do some roles understand other roles better than others?

Here in infotainment, these are the specialties or the roles that contribute in our software factory.

You’ll probably recognize the majority of these because they match typically in the industry.

value contribution areas within our factory

First up, Flow control station: Idea to WIP.

I went around and I asked my coworkers and I asked other people in the software industry the question that’s defining those arrows. I call those arrows empathy and proficiency arrows.

Out of all the product owners that you know, on average could they step in and be proficient at being a customer for that product?

Out of all of the project managers you know, on average could they step in and be proficient at being a customer?

Now I know that’s a complicated question. However, the green arrow symbolizes a yes, the red arrow symbolizes a no. We found that the relationship, in this case, is a highly empathetic relationship towards the customer.

These are the primary actors that exist specifically within this flow control station, we’re trying to determine whether or not WIP should be released into our factory.

I’m not saying these specialties can’t do each other’s jobs, I’m just saying on average these are the results. Typically what happens in this particular station are what I call micro-consulting engagements, and that’s where we’re actually interrupting all of those other specialties to determine whether or not we should release WIP. All of those interruptions are all contexts which are on their own as well.

What’s interesting is if I’m sitting at my desk and I’m absolutely crushing out code, and I’ve got my headphones on, completely in the zone, and somebody walks over to my desk and interrupts me, they’re automatically assuming that what they have to talk about is of higher priority than what I’m currently working on.

I don’t think that’s fair. In fact, I think they should rank whatever they’re going to talk about.

In the CPU’s case, all they have to worry about is addition and this queue line. I kid you not, I’ve actually had a queue line at my desk full of people who were going to interrupt me.

Typically that prioritization is something of the equivalent, had the CEO been in the queue line I would imagine I’d treat it like a CPU treats a real-time threat: you can come to the front, what do you have to say?

The next station is the requirements station.

The same relationship exists. One interesting thing I’ve found is that customers on average aren’t good at writing requirements, specifications or even exactly what they want.

They’re very good at talking about needs, talking about wants, talking about vision, but typically when it comes to handing over to a developer, most of the time I’ve found it’s not enough.

They have the same sort of micro-consulting engagements that the previous station did, again all interruptions, to ensure that the requirements being written are not going to be impeded further on downstream.

The next design station is “How should we build this?”

This is an interesting one.

This is design, and design to me can be in two different categories. Design is super overloaded. It could be architecture, it could be HMI design.

And what you see here is a lot of red arrows. Basically, I asked my coworkers again: out of all the architects you know, on average would they be proficient at being an HMI designer? The answer was no. The reverse relationship exists, as well as the same thing exists within the customer.

What this actually can show is there are some automatic friction points that exist between these specialties. This could also show you that we could spend some time to make them a little bit frictionless, or maybe we could spend the time developing a relationship that doesn’t have to do with the product or the project, but with the people in general.

Typically there are validation engagements that happen, which are also interruptions. For example, one of the UX designers has a trail-off based off of how much effort they plan to put on a product. When they’re finished with their wire framer, or with the majority of iterations, they are putting remaining capacity for these interruptions. They’re adding it into their workflow, which I thought was pretty smart.

The same consulting engagements exist further on downstream.

In the develop station, if we ask ourselves the same question: out of all the developers you know, on average could they fulfill a QA engineer role and be proficient at it?

While a lot of people in these specialties don’t necessarily want to be specialized in one of these areas however they could. We get double greens here across all three of these.

This is one thing that contributes to the value of DevOps — all three of these specialties understand each other’s risk, understand where each other’s coming from, understand what they could do to help the other person complete their task.

Validation engagements exist. We’ve migrated from design or theoretical, and now we’re at implementation. Most of these engagements are “Hey, I went to build the thing you told me to build or the thing that you wrote out, and it’s not going to work for me. It’s definitely not working out.”

How We Exploit the Double Green

All of our build machine infrastructure is all done in Docker containers, it’s all identified within Packer.

Each one of our developers who are contributing towards a product, if they have some sort of library or some sort of component they need the build machine to have, can go right to a Packer recipe, create on a branch completely in isolation, make their new set of infrastructure, point their product to build with that new set of infrastructure ALL without bothering anyone else in the entire workflow or disrupting anyone else.

Here, the ops has enabled self-service for the developer to completely work on their own, test whatever they need to do. “I’ve got this new version of Python I need to put on the build machines.” “Okay, there’s the Packer repository, go ahead and do it.” We have CI on that Packer repository. We get an automatic deployment to a Docker registry. That Docker registry is pointed to by the product.

Another way we exploited double green arrow is we have automated test feedback, with CICD pipelines. We can put test cases into an automated pipeline so that developers can get the results back quicker.

Validation and deploy stations are the same type of relationship. However, your primary actor is typically the QA engineer. There are validation engagements that also exist when you’re in the QA phase.

Sometimes the validation engagement could be, “Should we ship this or not?” or “Should we disable this maybe in production before we actually let it out?” One thing that’s unique about developing for an embedded device is we can actually put it into a production representative vehicle without officially saying that we’ve released things. It’s very difficult for us to compare to the web world because in the web world we can release everything out to millions of customers at scale very quickly. For us, we contribute toward an embedded device or an OS that runs on an embedded device, and there’s a point in time at which we bless that for a release.

One way that we exploit specifically for validation and deploy stations is virtualize and simulate the production environments so that we don’t have to use hardware. One of the challenges with hardware is it typically doesn’t scale, or by the time you’ve scaled it for what your team demands it’s already outdated.

Here’s the ops station. The only surprise here for me is actually the architect. Most of the average architects we’ve found could be proficient at an ops role. Now, that’s not necessarily whether they want to be, but they could be.

Here are the lessons that we’ve actually learned:

  • We found that if context switches are inevitable, we should factor them into capacity. This was actually really hard for me to swallow, but if it’s unplanned work and it happens so frequently, it’s now become planned work.
  • The capacity at which each of your stations are staffed at depend on the project maturity. You may find out at the beginning of a project that you have significantly more architects doing these requirements are at those stations than you do on stations further downstream.
  • We found that some roles are in a perpetual state of interruption. It’s always some sort of higher priority that you must be working on, but it never actually ends. This is a very challenging problem for us to solve when we have a due date that we need to deliver vehicles to customers with.
  • We found that empathy increases if a close team member has an impediment that they could fix. If the person right next to you is struggling because of something that you could actually take care of yourself, they’re more likely to take care of that problem when they’re next to each other, and when they’re a cross-functional team. We typically found that teams that are cross-functional are more fruitful when they’re all in the same location or they at least all bond with each other on a regular basis.
  • Using the same or closely-coupled toolset will create less friction in the more expensive switches. This means if I’m going to a different task that’s a different product, or if I’m going to a different task that’s at a different station, if it is in the same toolset and I’m very comfortable, then it’s easier for me to do that context switch. This is where tools like Tasktop are extremely helpful because you can replicate an entire ALM tool in another ALM tool so that nobody has to go out of their comfort zone. This can help throughput.
  • If one of the other stations or context switches that you’re doing has a significant number of manual tasks, it ends up becoming very draining and adds more friction to whether or not you should switch to it.
  • A culture of mentoring and training typically increases throughput. From a brain surgeon perspective, I’m pretty sure after an entire 8 years, 12 years of education, they don’t just walk in and start doing surgery on brains. They probably watched a few. I think what’s interesting is I find it’s very unhealthy if a department doesn’t factor in training or mentoring into their capacity. The only way you’re going to train the next generation that will run your company is if you actually focus on training as it is important as everything else.

Attend the DevOps Enterprise Summit, Las Vegas: October 22-24, 2018

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DevOps: Who Does What (Part 1)

The following is an excerpt from a presentation by Cornelia Davis, Senior Director for Technology at Pivotal, titled “DevOps: Who Does What.”

You can watch the video of the presentation, which was originally delivered at the 2017 DevOps Enterprise Summit in London.

Throughout the years, I’ve had the great opportunity of working with very, very large enterprises across all verticals. My background is as a technologist, I’m a computer scientist, and initially, I spent a lot of time talking tech at the whiteboard. But then I realized that there was so much more that needed to change, which is why I’m sharing now about the organizational changes that can support our technology needs.

My First Question to You Is, Is This Your Reality Today?

We have different business silos across the organization and different individuals that are coming from those silos. When we have a new idea for a product, we kick off a project and individuals go into the project to do some work.

The first individuals from the first silos come in, and they generate their artifact. Then what do they do? They throw it over the wall to the next step. If you look at this slide below you’ll notice once they’re done they leave the project.

If for some reason we have to go backwards, we have to figure out how to get them back into the project. And, so it goes through each silo. We all recognize that this is a slow and challenging process. If it only moved linearly, it might be okay. But we all know that it goes this process goes backwards, and forwards, even circular!

But that’s not even the biggest problem of these things.

The biggest problem is that each one of these organizations are incentivized differently.

My favorite examples are App, Dev, and QA — so let’s look at these.

Application Development is almost always incentivized by ‘Did you release the features that you promised on time, and ideally on budget?’ And, if you released the features on time you get a ‘Way to go, you achieved your goals.’
Then it moves over to QA.

What is QA incentivized on? Well, they’re responsible for quality. So they are generally incentivized by the number of bugs that they have found and fixed.

Now, let’s look at these things in combination. What happens when the application development process starts to fall a little bit behind? Developers start working late into the evenings. They work on weekends, they start working very unsustainable hours, and what happens? Quality suffers, but they hit their features on time.

Well, when they throw that over the wall to QA, what’s gonna happen now?

QA is going to find more bugs. Way to go! So we’ve got locally optimized metrics that do not create a globally optimized solution. That’s a big problem.

Well, the answer is really simple…

The Answer Is Balanced Teams!

What we’re going to do is we’re going to center things around a product and the product team is incentivized to deliver value to a customer, to deliver value to some constituency.

For example if I’m in an e-commerce scenario:
I have a product team that is really about the best experience around showing product images, recommendations, soliciting reviews, or it could be some back office product that is enabling your suppliers. These are all the different product teams.

There’s been a lot of research, and a lot of discussion, and a lot of proof points that product teams are really the way to go.

But what if we don’t have product teams? What if we have different roles within the SDLC, how do you create product teams of these different disciplines to come together into a product?

We’re Going to Try and Put Things Through the Sorting Hat

If you have been living in a cave for the last 10 years, and you don’t know what the sorting hat is, this comes from Harry Potter. When new students come to Hogwarts School of Witchcraft and Wizardry on their first day, each one of them places the hat on their head and they get sorted into one of four houses, and that’s the house that they live in for the next seven years.

So we’re going to take those roles and we’re going to sort them into houses. But the question then is, what are the houses that we’re going to sort into? So let’s take a little bit of a tangential ride over to the side and think about a couple of houses. (I’m going to end up with four houses in the end, but I want to start with two.)

The left part of this slide you’ve all seen for the last several years.

That is where we were maybe 15 years ago. IT was responsible for the entire stack from the hardware all the way up through the application.

Then VM Ware came along and virtualized infrastructure.

Then a whole host of people made infrastructure as a service available. Amazon web services of course being kind of the behemoth of that.

That made it so that we could just get machines, EC2 machines for example, and then we could stand up everything that we needed on those machines. Getting machines was easy.

Then in the last five years or so, we’ve taken that abstraction up another level and we’ve created application platforms where we have individuals who can be building applications, and the only thing that they need to worry about is their application code.

What’s important about that application platform is that it generates a new set of abstractions. Those abstractions are at a higher level. They are fundamentally the application, or maybe some services there in support of that application, and it allows us to not do things like implement security by creating firewall rules that machine boundaries, but instead allows us to implement security at the application boundary.

This new abstraction is one of the key things that’s happened in platforms over the last five years. It’s given us something really interesting and really important. It’s allowed us to define two different teams. And it’s defined a contract between those teams that allows these teams to operate autonomously.

When we hear about all of the different goals of an enterprise, they all talk about needing to bring software solutions to market more quickly, and more frequently. So agility, and autonomy, and teams is incredibly important. We’re always looking for those boundaries where we can create more autonomy.

Now the Application Team…

The team that’s going to create the next mobile app or the next web app or even some analytics app for example, can focus on building that application, and they don’t need to worry about even the middleware that sits below it.

They’re responsible for creating the artifact. They’re also responsible for configuring the production environment, deploying to production. They are doing Dev and Ops. It’s not necessarily the same person, but it is the same team. They’re deploying to production, they’re monitoring. When they notice that they need more capacity, they’re scaling so that they can achieve better performance. They deploy new versions when they need to.

It’s entirely up to them.

Now There’s Another Product Team, and That Is the Platform Team

So that’s the team that’s providing the platform, and notice that they’re doing exactly the same things.

They are deploying the platform, they’re configuring it, they are monitoring it, they are upgrading it when they need more capacity, or upgrading it to the next version. They’re doing the same things but they have their own products that they’re working on. So the product orientation is really key.

This separation gives us the first two houses that were going to sort into. The APP team, and the platform team.

Now let’s take all of these roles that come from traditional organizations and start sorting them. And, so here’s our two houses, the APP team and the platform team.

We’re going to do this piece by piece and I’ll explain the steps as we go along.

The first ones that we’re going to do is we’re going to start with the purple bubble there. Before I sort them, notice that this Middleware and App Dev team is actually taking care of both the Middleware and the application development.

In retrospect, having worked in this new world for the last five years, I find this kind of counterintuitive because why would somebody who’s creating an application i.e., using the middleware be in the same group as the middleware itself? To a large extent, it’s because in the past middleware required a great deal of expertise. You had to know a lot about the middleware to be able to effectively program against it. That’s something that we’re trying to move away from. We’re having more agile middleware platforms and so on.

Notice what happens here.

We’ve got middleware and we’ve got App Dev, and we break those apart. We put the middleware engineers inside of the platform team. They’re part of that team providing the capabilities that the APP team can then use. Then we take kind of a full stack application development team and put them up in the APP team. We’ve got front end, and we’ve got back end. All of those individuals are there.

That one’s pretty straightforward.

The next one that’s also pretty straightforward is we’re going to pull some of the folks out of the infrastructure team, the folks responsible for building out the servers and the networks.

You might have noticed I put virtualized infrastructure and platform together in one team that many of our customers actually keep those as separate, but in this case it really wasn’t important to make that separation. You could be separating the platform team into two separate individual ones as well. The thing that I would caution you is you need to make sure that you then have a very crisp contract between the platform team and the infrastructure team.

I’ll be honest with you, that’s a little bit harder to find at the moment, so that’s part of the reason I’ve put them together.

Again, server build-out, network build-out, they are part of the platform team providing the view of the infrastructure up to the App team.

The next one that we’ll talk about here is what I like to call the control functions.

There is information security for example, and change control. Why did I move them at the same time? Change control was usually often coming out of the infrastructure team, and information security coming out of the chief security office. I moved them at the same time because they share a common characteristic. They are functions that today can stop a deployment. They are functions that on every release, on every release into production, they need to give their blessing.

We’ve seen when it comes to the very end, and we find problems in information security or any other types of security, it can actually stop things. There’s a great huge ball of things that we need to check off.

These functions here, information security and change control should engage with your teams that are providing the platforms, and the automation around the deployments to ensure that their concerns are satisfied. Their concerns are not wrong. It’s just the way that we’ve been solving them is something that’s in need of transformation.

In Part 2, we’ll talk about Ops.

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Looking Into E-DevOps (Enterprise DevOps)

DevOps is quickly evolving as foundational capability to get business and IT agility. While most companies are able to adopt DevOps automation (CI/CD pipeline) at the team or system level, they are not able to scale it at an enterprise level.

Like the Agile practice of Scrum needs to be built over with enterprise frameworks like SAFe, Scrum of Scrum, LeSS, DaD, etc., E-DevOps also needs to be built on certain key framework principles.

Below are some of the key framework principles that one should consider for successful DevOps at the enterprise level:

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CI/CD Pipeline

An organization should define a standard continuous delivery (SCD) procedure per strategic technology. In addition to defining an SCD pipeline per technology, some addition points to consider are:

  • Single source control and repository
  • Single service/release orchestration solution


There are several models for managing platforms for a CI/CD pipeline. As with any model, it’s a trade-off between flexibility, standardization, support model, and cost factor.

A cloud model would be using the existing CD-as-a-service by cloud providers. Central hosting can be on-premise or on the cloud but is managed by the client. Team setup is where each team creates and maintain its pipeline. The base image of the pipeline can be managed by centrally. Containerization is just an extension of this model with a different deployment strategy.

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Tooling Support

Support for CI/CD tooling at least the SCD (baselined) should be centrally controlled. The tool’s lifecycle management (LCM) support with guidelines and coaching of the team for adoption can be defined on the model.

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CI/CD Maturity

There are several models for looking at CI/CD maturity. It’s good metrics to measure depth and breadth per system/technology so that adoption can be tracked. We see integration of security, risk, and other operational aspects as part of the pipeline.

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While we will continue to see DevOps itself evolve into new mature avatars like DevSecOps, ChatOps, BotOps, NoOps, etc., it is important that foundation enterprise setup is done right.

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3 Critical Keys for DevOps Success — Experts and Customers Speak Out

Here are some highlights:

  • Chris Condo, a Senior Analyst from Forrester Research, presented the roles played by value stream mapping, automating everything, and self-service environments in DevOps success.
  • Aloisio Rocha, from NetEnt, transformed their ability to onboard new customers and sell into new markets by reducing customer deployment times from 4 hours (with downtime) to less than fifteen minutes with no downtime. They can now update their entire customer base in two weeks instead of the 18 months it used to take, a 4000% increase in speed.
  • Manish Aggarwal, from Intel, shifted quality left with a self-service catalog to manage and automate their 20,000+ test scripts. They now run 1000-fold more tests per day and increased annual releases by 2500%.
  • Gary McKay, from Somos, future-proofed his company and changed a mainframe operating mindset to an Agile/DevOps culture with microservices. Instead of taking 36 hours and 30-40 people, their releases now take less than an hour with only 5 people.

As a bonus, Aloisio graciously answered some questions from the audience with details on how they got their 4000% increase in performance (his numbers, not ours).

Q: How long did it take you at NETENT to go from 18 months to < 2 weeks?

A: It took about a year and a half with an iterative process. This included evaluation of tools, whiteboarding, learning the tool, building the pipelines, and some refactoring. And also developing trust in the automation pipelines.

Q: Did you stop and pause?

A: No we did not. It just took time to get to a stable, trustworthy and valuable automation pipeline.

Q: Did you get external help?

A: Yes, we had training and some professional services consulting from Electric Cloud in the beginning to help us understand the tool and how we could use it to fit our specific needs and environment. The rest we did on our own.

Q: What are the hurdles you faced?

A: At first we started the project with a few people that were only partly dedicated to it. So freeing up those people was one hurdle. It had a huge impact when we went for creating a real team with these people, dedicated to the task.

-Getting buy-in from stakeholders to help drive the initiative was another one. We were not showing progress and value fast enough because of the way we worked. Our initial approach was to build the automation pipelines as complete as possible before delivering. This led to a longer time before we got feedback from users, buy-in from stakeholders, and a loss of momentum.

We changed our approach and started working with MVP’s (minimum viable pipelines) instead. Which led to faster feedback, which in turn led to more stable and trustworthy automation and faster time to value, which got us buy-in.

Q: Any suggestions for organizations in a similar boat?

A: If you have just started, try to show value or potential early. Prototype it or MVP it. Another key is to get feedback early and consistently – it is critical for building stability and achieving trust in what you are doing.

-It does take time to do something like this, so it is important to manage expectations, but also like I mentioned to show constant improvement and value deliveries. This is key in getting the users and stakeholders to buy into the future potential, the end goals, and to make it tangible.

-Value delivery analysis and/or value stream mapping are always key tools in these types of initiatives. But keep in mind that they can get very large, complex and sometimes not even understandable depending on the level you are doing them. It makes sense sometimes to split or layer these mappings so that you can get a clearer picture of were to focus efforts and then start to work more efficiently.

So there you have it:

  1. Set expectations early
  2. Start fast with a minimum viable pipeline
  3. Communicate constantly
  4. Improve the value as you go, and,
  5. Adapt as you learn from your feedback.

To see how you, too, can enjoy up to 4000% faster deployments and still have your evenings and weekends anxiety-free, watch the webinar!

#c9d9: Electric Cloud and DZone Come Together for Continuous Discussions Episode 89!

Remember to check out Electric Cloud and DZone’s DevOps Toolchain podcast taking place on July 17 at 10 am Pacific Time! Electric Cloud’s Anders Wallgren and DZone’s Tom Smith will be joined by guests from SmartBear, Atlassian, New Relic, and GitHub to discuss the current state of each stage of the DevOps toolchain. You can learn about the Continuous Discussions podcast here!

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Webinar Recap: Bridging the Gap Between Dev and Ops

On a recent webinar, Gene Kim, founder of IT Revolution and co-author of “The Phoenix Project,” “The DevOps Handbook” and “Accelerate” and Anders Wallgren, Electric Cloud CTO, shared concrete tips for bridging the gap between Dev and Ops. During the discussion, each shared new trends in organizational structures, the importance of value stream mapping, tips for pipeline monitoring and tracking, and much more.

Continue reading for some of the top takeaways!

Wallgren speaks about the dichotomy of developer and operations teams and why the tension between the two exists:

“On the developer side, it’s really about creating new technologies, new features, experiment, move fast, change the tooling as new tools become available and are easier and better to use, etc. The motto for Dev is change the status quo, because we need to do more releases and features. Whereas on the Ops side, it’s more about maintaining the status quo. The goal is very much to keep the lights on and keep the money flowing in, and kind of be the engine for the company.” Wallgren goes on to explain how DevOps is helping operations teams handle the throughput and constant change from developers.

So, how do we bridge Dev and Ops and all the other organizations within a company? Wallgren explains that it comes down to understanding that we’re really all working towards the same goal:

The overarching goal for everybody in the company is to provide value to our customers. And then, in return for that value, you get money or respect or fame or, whatever it is that we’re looking for. Knowing and understanding that there is this shared goal of what we’re trying to do and that we aren’t safely siloed in our own organizations and don’t have to care at all about what comes before or what comes after is a very strong belief in the DevOps community as well as shared visibility. For all these different stakeholders and organizations to come together and smoothly and efficiently and repeatedly interoperate and deliver value to our customers, that involves everyone. It is a development problem, it’s a quality problem, it’s an operations problem, all the lines of business have to participate, even HR, finance, and partners, and so on have to get involved in this.”

Image title

Source: DevOps Enterprise Forum — Organizational Design for DevOps.

Referencing the image above, Kim explains the organizational evolution to Model 3:

“What we’re often seeing is the shift to Model 3 – product orientation or cross-functional organization. So, beyond technology, we typically do this when we want to optimize for quickly delivering value to customers. That means that any group can independently deliver value to customers because they have all the disciplines within their teams. They don’t need to beg, borrow or steal resources to do things that customers demand. By pulling people out of the silos, they have genuinely shared goals. The idea is that you get almost all of the benefits of model three without having to reorg everybody.”

Kim goes on to share some recent work about to be published around ticketing systems and makes a resonating statement:

“It is my big learning share that it doesn’t really matter where you are in the ticketing system, whether you are the producer of tickets, the consumer of tickets, or the doer of the tickets, when you’re trapped in this Byzantine labyrinth, it is sometimes a very joyless activity.”

Wallgren emphasizes the importance and value of value stream mapping:

“Value stream mapping helps you figure out, “what are we doing?” How much of this is productive work and how much of this is just wait states where there’s three hours of work being applied here but it’s three days of duration? Can we make that not be three days and take the wait states out of the system? I think part of the reason value stream mapping is so valuable and so useful is, it gets all the collaborators and all the stakeholders literally in the same room to walk through from when code gets checked into how it makes it into production.”

When modeling your pipelines, there are many things to take into consideration, but the more your pipeline looks like your value stream, the better. Wallgren goes on to share:

If you have your pipeline put together based on your value stream and you’ve coded it, you now have documentation of how you do things. And if you then show that you ran that pipeline and that’s how you built and qualified, tested, released your code, then that is the proof that you do what you document.”

Kim breaks down the amazing Amazon Prime Now transformation story Tisson Matthew shared at DevOps Enterprise Summit San Francisco 2017. Here’s the gist of it:

“Tisson Matthew as an engineering director within the transportation department had to cut across over 300 functions across the Amazon enterprise. It was one of the most heroic stories just because of the level of leadership and political sophistication needed to do that. There was no central planning function that could make sure that Tisson got what he needed. Basically, he was having to work with 300 parochial selfish people who only cared about their area and cared less about this new idea that’s been kicking around. So essentially, he had to use political skill and his Jeff Bezos card to emphasize that this is the most important initiative and they would use that to scramble the priorities. I share the story because I think we’re moving towards this product orientation [Model 3 above] where teams are more capable of delivering value to customers themselves. But for something like Amazon Prime Now, no one team could deliver Prime Now. They had to cut across essentially all of Amazon.”

Helpful hints on pipeline tracking from Wallgren:

“If we work on integrating data tracking as part of our pipeline efforts, then it becomes a very powerful tool for collecting and then reflecting on a lot of the interesting information. When you’re thinking about pipeline tracking, try to automate as much of the collection of data from the point tools as possible and the environments and all the pipeline jobs that run through it, whether it’s a build CI type pipeline or release pipeline or what have you.”

Architecture is at the root of productivity and joy in the day-to-day work of software delivery, explains Kim:

“These days what the findings show is that architecture has everything to do with how people do their daily work. Is it done on demand or are we hamstrung by ticketing systems? Everything should be done on-demand, self-service, not through ticketing systems and waiting two weeks, four weeks, six weeks to release. It’s those characteristics that allow us to get immediacy and fast feedback. Those are the conditions that allow us to get focus and flow not just in the Lean sense but in the sense of these are the conditions that allow us to have a mental state of effortless productivity and joy.”

Believe it or not, these tips and insights above don’t even begin to scratch the surface. To gain more invaluable information shared from Gene Kim and Anders Wallgren during the webinar, watch the entire replay here.

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How Do You Fit a Core Banking System Into a Few Containers?

The following is an excerpt from a discussion that was held at DevOps Enterprise Summit London 2017 by Amine Boudali, Senior Project Manager, Nordea and Jose Quaresma, DevOps Lead DK, Accenture about the experience and lessons learned when setting up the Core Banking Platform in a containerized environment. You can watch the video of the presentation here.

How Do You Fit a Core Banking System into a Few Containers?

We want to share with you our journey through core banking, how we’ve implemented the platform, some of our challenges and how we’ve gone about easing the pain we were facing to tackle them.

But first, a brief introduction to Nordea.

  • Nordea is the largest financial services groups in the Nordics.
  • We have around 11 million customers separated into 700 towards retail or private customers and then 700 corporate.
  • We came together as a collection of mergers and acquisitions, which actually led us to a relatively complex process landscape, but also an application landscape.
  • In some of the Nordic countries, you can have anywhere from four, five, or more core banking systems that are all more-or-less doing the same thing, triggering an initiative towards simplification, which then became the largest transformation program in banking in Europe.

For this initiative, we had a focus on delivering incremental and frequent business value but also positioning Nordea to be the digital bank of the future.

But, How Do We Do That?

In order to achieve this, Nordea partnered with Temenos as a software provider as well as Jose Quaresma’s team at Accenture as a system integrator.

Since the beginning of the project almost two years ago, we maintained a big focus on the following guiding principles:

#1 — Automation

Automation was a very big goal for us. This was both on the environment provisioning, on the test automation, and also on our CICD pipeline.

#2 — Everything As Code

Then we also had this goal of having Everything As Code, and here it’s both for the environment configuration but also again for the delivery pipeline. You really want to be able to have everything in the repository to be able to replicate things and have better control on the changes being made.

#3 — Short Feedback Loops

This would enable the developers and testers to get quick feedback on whatever they were doing and with what they were testing, which is helpful for allowing them to learn fast to fail fast so that they can move forward with their development and testing.

B.C. — Before Containers

For our first goal, automation, while we weren’t in CICD, we were on our way there. We had continuous integration, we had automatic build and deployment to our development and test environment. But we really wanted to focus on pushing that further up the environment so we could get the full advantage of the work we were doing there.

From the Everything As Code perspective, we did not have any configuration management in place. We were using a custom made solution at Nordea, which meant that having Everything As Code was still something that we were striving towards.

Finally, from the perspective of our goal for Short Feedback Loops, we had daily builds and deployments, which was something that we’re very happy with considering the complexity of the system, but there were some challenges that prevented us from taking this further.

For example, there were some intricacies of deployment that actually required us to restart the WebLogic servers when we are deploying. So if we wanted to build and deploy — every time there was a change, we would run the risk of having an environment that is more often being restarted than actually up! That’s not helpful with the short feedback loop.

Top Challenges

As we continued on our journey, we had some big challenges to overcome.

  1. At the beginning of our program, there was a lot of proof-of-concept that required several environments to adapt in very short time. If we needed to organize and orchestrate multiple units to provision that environment, it would take a lot of time. We’re talking months or weeks to provision one full-fledged environment. Couple that with the complexity of the deployment and orchestrating the downtime the product required — and you can see why we had a lot of challenges when it came to frequent deployments.
  2. With fragmented environment configurations, there was no full control over the whole stack, which meant that provisioning from this unit might lead to a different outcome depending on the time, the humor, or even the state of that person doing the work.
  3. Likewise, when you think about a core banking system, this isn’t a “system of engagement” type application; this is the core where everything ties together. You have upstream integrations coming from your channels. You also have downstream integrations into your accounting, accounting flow, data warehousing, etc. It’s really the heart of the body in that way. So it’s not just about how we stand that up, but also how we simulate and also mock these integrations.
  4. Finally, with shortening feedback loop to developers and testers, we wanted this to not only be about developing and testing in our current state but also the future.

Easing the Pain

So, where did we end up?

With long environment provisioning, we went from weeks and months to under one hour.

This was amazing for us. One hour.

Here we are not talking about a database provisioning, we are not talking about an application server provisioning. We are talking about a full-fledged environment, in under an hour by using this solution.

We’re pretty happy with that.

For the proof-of-concept challenges we had, we developed better life-cycle management. In the past, standing up an environment and decommissioning it required that you go back to these units to decommission it or reuse it, which meant that you would need to sequence your proof-of-concept.

With this system, we worked to rev up the environment, used it for the proof-of-concept so that there were no dependencies, and we can decommission it when we want to.

Now onto the challenges we had with complex deployment and orchestrations.

Here, for the first time, we were able to use this product to do live deployments. Which meant that this came down from one hour of downtime to zero downtime. Of course, this is not production-ready. We still have small things to iron out from a business perspective, but we are able to do live deployments.

With our fragmented environment configurations, this is a full infrastructure as code. We talk to the developers and testers and the teams that are developing on this platform, they help us to improve the environment so they can actually put in requests or merge request and then we review that and take them in. This is more to bring them into the world of how we provision the environments.

We shortened feedback loops. With the ability to provide that end-to-end integration to the core banking system, we were also able to do frequent deployments to the development environment but also concepts such as time travel. This concept gives us the possibility to do the end of year reporting, the end of month reporting, or for example interest accumulation — ahead of time. We don’t need to wait for that time to do it. We are able to basically fast-track the time from today and do those type of testing in order to ensure the quality of the product.

Now You May Be Thinking, “Okay, This Looks Awesome, but How Did You Do It?”

Our answer, and we’re not by any means saying that it’s the answer, was to use Red Hat OpenShift platform to start this transformation.

We had two teams of about 5 people, with a few people were focused on installing the platform, and then we had the others focused on setting up of the application into the new platform.

Here the migration was very much a lift and shift migration. We didn’t want to be thinking about which technology stack we should really be using in this platform, but more “let’s grab the technology stack that we have right now running, and see what it gives us.”

This took us around half a year, and what we ended up with was a setup where we have a core banking application project the OpenShift sense, that mostly consists of three containers:

  • An Oracle database container and that one is attached to persistent volume to persist the data
  • A WebLogic container where the Temenos core banking application is running on
  • An Oracle ServiceBus container that is interacting with the T24 system (in this case with the WebLogic container to trigger the integrations and the services being used there)

Currently, we have around 30 of these projects running in our development and test environments and being used by both teams to test features that they want to play around with, they can use it for the end of year reporting or testing using Time Travel Features, etc.

Now let’s take a look at what this looks like, here is a picture to illustrate how the live deployment with OpenShift works:

Here we are combining the built-in features from OpenShift with the application. Here we have a deployment in progress and the one you see on the left is the container that is currently running. That’s where the traffic is being routed through. But then you have a container on the right that is being deployed with the updates, and that one is just starting.

What OpenShift is doing here for you is that while the container on the right is starting, the traffic will go through the one on the left, the old one, but then once OpenShift sees it — a new one is ready and deployed, which shifts the traffic to the new container and kills the old one.

But While It’s Our Answer, but It’s Not the Final Answer.

We do have new challenges that we are now starting to focus on.

First, there was a certain lack of awareness within the different teams of the platform. The teams are very busy working on the different features and the things that they have to release. Then we came in and we have this platform with all these new features. So we’ve shifted to thinking more about how we communicate with the teams and inform them about this new platform and the things that they can do with it.

Another very important paradigm shift happening is around “treating our servers as pets to treat them as cattle,” to steal the famous analogy. If you’re doing manual changes in the servers, they might be gone in half an hour, or whenever you do the next deployment. It’s key to have that mindset shift.

Next, we are pretty aware that we do have some heavy containers running on the platform. While the system is not heavier than it was before when it was running in a more standard VMware kind of environment, it is still very heavy.

Which means that it doesn’t allow us to take full advantage of a containerized environment where we could have a very quick way of loading environments. Instead, we have containers that are slow to load, that are quite heavy, and fairly big.


���� London: June 25-26, 2018

Las Vega����s: October 22-24, 2018

Looking Forward

Looking forward what we will be thinking about how we manage our users, customers, but also people, and figuring out when we’ll say that this feature is ready for you to use.

Currently, the platform is in a Dev and Test environment, but we’re hoping to bring it to a production-ready state sometime this year.

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Case Study: IBM DevOps Transformation

Rosalind Radcliffe is an IBM Distinguished Engineer responsible for DevOps for Enterprise Systems. She helps navigate transformations for both IBM and their clients.

Below, we’ve transcribed the key takeaways and main highlights of her presentation where she shows how a traditional z/OS product that’s been around forever can also transform (which you can also watch on YouTube here.)

What We Wanted

When we started this process, we had release cycles that varied from relatively short to 18+ months. Our goal was to bring all of our z/OS development tools together in a single delivery pipeline in order to:

  • Deliver better value to our clients
  • Make sure that they had an integrated stack
  • Make sure it was easier for them to adopt
  • Allow us to deliver function faster and more efficiently for them (we were shooting for monthly deliveries)

With our wonderful set of products to delivering together instead of separately, we’d have a full DevOps pipeline for z/OS Explorer and all the products that sat on top of it that could give us the value and give you the value, and make it easier for all of us.

What We Did

We created a single development pipeline.

Now this was a challenge. We have 17 separate products. They actually are still separate products, and in some cases, they’re totally separate. But we wanted the IDEs and the environments to be built together, so we literally stopped development for a while and said, “Other than critical customer situations, we’re not gonna do anything else. We’re gonna build a delivery pipeline.” And it took about four months.

We decided to do this in pieces. So we started with the base set of products; so z/OS Explorer plus CICS tools to get that base there. Then, we added products throughout the year, and we continue to add products.

So any product that has an Eclipse-based IDE, that is related in any way, shape, or form to mainframe development will end up on this pipeline.

The important thing to recognize about this is, while yes, it’s Eclipse-based IDEs, it’s also the backend pieces of the development for this. It’s not just Java development, it’s traditional PLX development that we use internally.

It’s an assembler development.

And we have the DevOps pipeline for all the parts of this. We don’t use separate tools. We have one toolchain, whether or not you’re building the Z side or the distributed side, we have one SEM. No separation, all the code is together. Everybody can see what everybody’s working on, etc.

We want to make sure that the pipeline is consistent, that when you’re doing DevOps, it’s doesn’t matter if you’re doing DevOps for Z or distributed, it’s one story, one set of processes, one set of capability.

Fun Facts

  • 94 releases, 17 products
  • We’ve gone to one-month releases for fixes (and small function)
  • It is a single repository, so they’re all sitting in this environment. They have a set of automated tasks with them. They run every night so that we can deploy the function and we have what we need to deliver customer value.

What’d We Learn?

Well, we had an advantage. We had the CICS team who had already started. They had started in 2005. So we had a lot of good lessons learned on how to do things and how to do this transformation.

But, what we ultimately learned was that:

  1. You have to give the teams time to work. You have to give them the education, the training, the understanding. They have to know what they’re gonna be doing in order to do this. However, they will less productive, in some cases, as they start. In our case, they were already using modern development tools so that wasn’t a problem. In the CICS team case, in 2005, they weren’t, so it does take somebody who’s been using a green screen a little while to learn how to use an Eclipse-based tool. Their productivity was not the same day one. So give them time to do this. Give them time to learn.
  2. We spent the time to work across teams. We took the lessons learned from each team and adopted them into the pipeline to help improve the next product team’s adoption.
  3. Management support is essential. Imagine telling a development team, “You’re not shipping any function for the next four months.” See how well that goes over with the sales team saying, “I want new function.” No, you’ve gotta stop. You have to get support. You have to acknowledge that this transformation is then going to give you value so that you can provide much more value, much faster.

But What Else Did We Do?

  • We took CICS team information and their capabilities and the rest of organization and we created a CICD community so that the organization has the support across all of IBM in transformation.
  • We have slack channels so that we can discuss what’s going on, so people can ask questions, so people can information about how to do this.
  • We have a golden topology for z/OS development. If you’re internal in IBM, we have one topology that specifies the set of products that you’re going to use, and I’ll share that externally as well so you can all see it. But it has things like Jenkins on it, it has rational team concert on it, it has zUnit to do united testing. It’s the standard set of tools and products you would need to do development.

Closing Thought

As you can see, there is no reason that the mainframe should not be included in your DevOps transformation, so please remove the line ‘mainframe excluded’. I need everyone to help the mainframe developers understand that, yes, the mainframe can do DevOps.

We need to kill this concept of two speed IT because it doesn’t work. Clients explain that it doesn’t work. We need to help everyone understand multi-speed IT is really what we need.

Attend the DevOps Enterprise Summit

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How Your Systems Keep Running Day After Day

John Allspaw is co-founder of Adaptive Capacity Labs and former Chief Technology Officer of Etsy. As an engineering leader and researcher with over 20 years of experience in building and leading teams engaged in software and systems engineering, Allspaw has spent the last decade bridging insights from Human Factors, Cognitive Systems Engineering, and Resilience Engineering to the domain of software engineering and operations.

Also the author of two books, “The Art of Capacity Planning: Scaling Web Resources” and “Web Operations” (O’Reilly Media), Allspaw continues to contribute to the IT and DevOps communities through speaking and collaboration on new, exciting research.

In fact, we were lucky enough to host John at the last DevOps Enterprise Summit in San Francisco, where he took to the stage to talk about “How Systems Keep Running Day After Day.”

Below, we’ve transcribed the key takeaways and main highlights of Allspaw’s presentation, enjoy!

John Allspaw at DOES17 San Francisco

How Your Systems Keep Running Day After Day

What I want to talk about is new. It is different, and I feel very, very strongly about this.

To help set the stage, my thesis for my degree in Human Factors and System Safety was “Trade-Offs Under Pressure: Heuristics and Observations Of Teams Resolving Internet Service Outages.”

Some of you may have heard of this, what’s called the Stella Report.

At a high level, this report is the result of a year-long project of a consortium of industry partners. IBM, Etsy, and IEX, trading company, a trading exchange in Manhattan. Over this year, folks from the Ohio State University Cognitive Systems Engineering Lab, David Woods, Richard Cook, and a number of other folks looked deeply at an incident in each of those organizations.

They found these six themes and were common across all of them.

Certainly, the results are quite important. It’s how that research was done that I want you all to take a look at.

Here are my main takeaways from the report:

  1. We have to start taking human performance seriously in this industry. If we don’t, we will continue to see brittle systems with ever-increasing impacts on our businesses and on society.
  2. We can do this by looking at incidents going beyond what we currently do in postmortems or post-incident reviews or after-action reviews
  3. There do exist methods and approaches from the study of resilience in other domains, but they require real commitment to pursue. Doing this is both necessary and difficult, but it will prove to be a competitive advantage for businesses who do it well.

First, I want to start with a little bit of a baseline, a bit of a vocabulary that’s going to be important as I sort of walk you through this. I’m going to describe a sort of picture, a representation, like a mental model of your organizations, and it’s going to have an above-the-line region and a below-the-line region.

If you imagine what we have depicted here, this is your product, your service, your API, or whatever your business derives value from and gives to customers. Okay? Inside there, what you see is your code. You see your technology stack. You see the data and some various ways of delivering this, right? Presumably over the internet or some other sort of way. But if we stay here, nobody’s going to believe me that that’s what we call the system, because it’s fine, but it’s not really complete.

What’s really connected, and what a lot of people have been talking about here in the DevOps Enterprise Summit community is all the stuff we do to manipulate what goes on in there, and so we have testing tools. We’ve got monitoring tools. We’ve got deployment tools and all of the stuff that’s sort of wired up. These are the things that we use. You could say that this is the system, because many of us spend our time focused on those things that are not inside the little bubble there, but all of the things that are around it, but if we were to stay just with this, we won’t be able to see where real work happens.

What we’re going to do here is, we’re going to draw a line that we call the line of representation, and then dig a little deeper. What we see here is you. All the people who are getting stuff ready to add to the system, to change the system. You’re doing the architectural framing. You’re doing monitoring. You’re keeping track what it’s doing, how it’s doing it, and what’s going on with them.

Now, you’ll notice that each one of these people have some sort of mental representation of what that system is. If you look at it a little bit more closely, you’ll see that none of them are the same. By the way, that’s very characteristic of these types of roles. Nobody has the same representation of what is below the line.

To summarize, this is our model of the world, and it includes not just the things that are running there, but all of you, the kinds of activities you’re performing, the cognitive work that you’re doing to keep that world functioning. If we play with this a little bit more, we end up with this kind of model. This model has a line of representation going through the middle, and you interact with the world below the line via a set of representations.

Your interactions are never with the things themselves. You don’t actually change the systems.

What you do is that you interact with the representation and that representation is something about what’s going on below. You can think of those green things as the screens that you’re looking at during the day, but the only information that you have about the system comes from these representations. They’re just a little keyhole. Right?

What’s significant about that is that all the activities that you do, all of the observing, inferring, anticipating, planning, correcting, all of that sort of stuff has to be done via those representations, so there’s a world above the line and a world below the line, and although you and we mostly talk about the world below the line as if it’s very real, as if it’s very concrete, as though it’s something that that’s the thing, here is the surprise.

Here is the big deal – you never get to see it.

It doesn’t exist. In a real sense, there is no below the line that you can actually touch. You never, ever see code run. You never, ever see the system actually work. You never touch those things.

What you do is that you manipulate a world that you cannot see via a set of representations, and that’s why you need to build those mental models, those conceptions, those understandings about what’s going on. Those are the things that are driving that manipulation. It’s not the world below the line that’s doing it. It’s your conceptual ability to understand the things that have happened in the past, the things that you’re doing now and why you’re doing those things, what matters, and why what matters actually matters.

Once you adopt this perspective, once you step away from that the idea that below the line is the thing you’re dealing with, and understand that you’re really working above the line, all sorts of things change.

What you see in the Stella Report and that project and other projects that we’ve been engaged with is taking that view, and understand what it really means to take the above-the-line world seriously. This is a big departure from a lot of what you’ve all seen in the past, but I think it is a fruitful direction that we need to take.

In other words, these cognitive activities (see below) in both individuals and collectively in teams up and down the organization are what makes the business actually work. Now, I’ve been studying this in detail for quite a while here, and I can tell you this. It doesn’t work the way we think it does.

Finally, to set this frame up, the most important part of this idea is that all of this changes over time. It is a dynamic process that’s ongoing. This is the unit of analysis. Once we take that frame, we can ask some questions. We can ask some questions about above the line like this.

“How does our software work really, versus how it’s described in the wiki and in documentation and in the diagrams? We know that those aren’t comprehensively, they’re not comprehensively accurate.”

“How does our software break really, versus how we thought it would break when we designed safeguards and circuit breakers and guardrails?”

“What do we do to keep it all working?”

Question: Imagine your organization. What would happen if today at six o’clock all of your companies took their hands off the keyboard? They don’t answer any pages. They don’t look at any alerts. They do not touch any part of it, application code or networks or any of it. Are you confident that your service will be up and running after a day?

The question then is how to discover what happens above the line. Well, there’s a couple things. We can learn from the study of other high-tempo, high-consequence domains, and if we do, we can see that we can study incidents. (Note: when I say “incidents,” I mean outages, degradations, breaches, accidents, near-misses, and glitches – basically untoward or unexpected events).

What makes incidents interesting? Well, the obvious one is lost revenue and reputation impacts on a particular business. I want to assert a couple of other reasons why incidents are interesting. The one is that incidents shape the design of new component subsystems and architectures. In other words, incidents of yesterday inform the architectures of tomorrow. That is, incidents help fuel our imaginations on how to make our systems better, and therefore what I mean is, incidents below the line drive changes above the line.

That’s the thing. This can cost real money. Incidents can have sometimes almost tacit or invisible effects, sometimes significant. Right now, a lot of people are splitting up a monolith into micro-services. A lot of people do that because it provides some amount of robustness that you don’t have. Where do you get that?

You’re informed by incidents.

Another reason to look at incidents is that they tend to give birth to new forms of regulations, policies, norms, compliance, auditing, constraints, etc. Another way of saying this is that incidents of yesterday inform the rules of tomorrow, which influence staffing, budgets, planning, roadmaps and more. Let me give you an example: In financial trading, the SEC has put into place Regulation SCI. SCI, is probably the most comprehensive and detailed piece of compliance in modern software era. The SEC has gone and been very explicit. We have this as a reaction to the flash crash of 2010 to Knight Capital, BATS IPO, Facebook IPO. It is a reaction to incidents.

Even if you go back a little bit further, it’s often cited that PCI DSS came about when MasterCard and Visa compared notes, realized they lost about $750 million over 10 years, so incidents have significant, and by the way, I can, as a former CTO of a public company, I can assure you that this is a very expensive, distracting, and inevitably a burdensome albatross for all of your organizations. Incidents are significant in this way too, but if we think about incidents as opportunities, if we think about incidents as messages, encoded messages that below the line is sending above the line, and your job is to decode them, if you think about incidents as things that actively try to get your attention to parts of the system that you thought you had a sufficient understanding of but you didn’t, these are reminders that you have to continually reconsider how confident you are about how it all works.

Now, if you take this view, a whole bunch of things open up. There’s an opportunity for new training, new tooling, new organizational structures, new funding dynamics and possibly insights that your competitors don’t have.

Incidents help us gauge the delta between how your system works and how we think your system works, and this delta is almost always greater than we imagine. I want to assert perhaps a different take that you might be used to, and it’s this. Incidents are unplanned investments in enterprise, in your company’s survival. They are hugely valuable opportunities to understand how your system works, what vulnerabilities in attention exist, and what competitive advantages you are not pursuing.

If you think about incidents, they burn money, time, reputation, staff, etc. These are unavoidable sunk costs. Something’s interesting about this type of investment, though. You don’t control the size of the investment, so therefore the question remains, how will you maximize the ROI on that investment?

When we look at incidents, these are the type of questions that we hear, and it’s quite consistent with what researchers find in other complex systems, domains. What’s it doing? Why is it doing that? What will it do next? How did it get into this state? What is happening? If we do Y, will it help us figure out what to do? Is it getting worse? It looks like it’s fixed, but is it? If we do X, will it prevent it from getting worse, or will it make it worse? Who else should we call that can help us? Is this our issue, or are we being attacked? This is consistent with many other fields. Aviation, air traffic control, especially in automation-rich domains.

Another thing that’s notable is that the beginning of any incident, it’s often uncertain or ambiguous about whether or not if this is the one that sinks us. At the beginning of an incident, we simply don’t know, especially if it contains huge amounts of uncertainty and huge amounts of ambiguity. If it’s uncertain and ambiguous, it means that we’ve exhausted our mental models. They don’t fit with what we’re seeing, and those questions arise. Only hindsight will tell us if that was the event that brought the company down or if it was a tough Tuesday afternoon.

Incidents provide calibration about how decisions are focused, about how attention is focused, about how coordination is focused, about how escalation is focused. The impact of time pressure, the impact of uncertainty, the impact of ambiguity, and the consequences of consequences. Research validates these opportunities.

“We should look deeply at incidents as, “non-routine challenging events, because these tough cases have the greatest potential for uncovering elements of expertise and related cognitive phenomena.”

– Gary Klein, the originator of naturalistic decision-making research.

There’s a family of well-worn methods, approaches, and techniques. Cognitive task analysis. Process tracing. Conversational analysis. The critical decision method. How we think postmortems have value looks a little bit like this:

An incident happens. Maybe somebody will put together a timeline. We have a little bit of a meeting. Maybe you’ve got a template, and you fill that out, and then somebody might make a report or not, and then you’ve got, yeah, action items, finally. We think that the greatest value, perhaps maybe the onliest value, is where you’re in a debriefing and people are walking through the timeline and you’re like, “Oh, my God. We know all this.”

This is not what the research bears out. The research bears out that if we gather subjective and objective data from multiple places, behavioral data, what people said, what people did, where they looked, what avenues in diagnosis did they follow and weren’t fruitful? Well-facilitated debriefings get people to contrast and compare their mental models that are necessarily flawed. You can produce different results, including things like bootcamp, onboarding materials, new hire training. You can have facilitation feedback if you build a program to train facilitators. You might make roadmap changes, really significant changes based on what you learn.

I can tell you this from some experience. There is nothing more insightful to a new engineer or an engineer just starting out in their career than being in a room with a veteran engineer who knows all of the nooks and crannies explaining things that they may not have ever said out loud. They have knowledge. They may draw pictures and diagrams that they’ve never drawn before because they think everybody else knows it. Guess what? They don’t. The greatest value is actually here because the quality of these outcomes depends on the quality of that, that recalibration. This is an opening to recalibrate mental models.

From the Stella Report, it “informs and recalibrates peoples’ models of the how the system works, their understandings of how it’s vulnerable and what opportunities are available for exploration.”

In a lot of the research, in all of the research contained in the Stella Report, and it fits with my experience at Etsy as well, one of the reflection’s strongest from people who do this in a facilitated way to do this comparing and contrasting. “I didn’t know it worked that way.” Then there’s always other, “How did it ever work?” Which is funny until you realize it’s serious. What that means is, the way not only I thought it worked a different way. Now, I cannot even imagine, I can’t even draw a picture in my mind of how it could have possibly worked. That should be more unsettling. By the way, I want to say this is not alignment. Like I said, via representations, we necessarily have incomplete mental models. The idea is not to have the same mental models, because they’re always incomplete, because things are always changing, and because they’re going to be flawed. We don’t want everybody to have the same mental model because then everybody’s got the same blind spots.

Blameless – going back to the blog post that I wrote in 2012

“Blameless” is table stakes. It’s necessary, but it’s not sufficient. You could build an environment, a culture, an embracing, a sort of welcoming organization that supports and allows people to tell stories in all of the messy details, sometimes embarrassing details, without fear of retribution, so that you could really make progress, and in understanding what’s happening, you can set that condition up and still not learn very much. It’s not sufficient. It’s necessary, but not sufficient. What I’m talking about is much more effort than typical post-incident reviews. Right? This is where an analyst, a facilitator can prep, collating, organizing, analyzing behavioral data. What people say, what people do. There’s a raft of data that they can sift through to prep for debriefings, a group debriefing, or a one-on-one debriefing, going beyond … Postmortems hint at the richness of incidents. Following up on this takes a lot of work.

By the way, everyone’s generally so exhausted after a really, a stressful outage or incident or event that sometimes everything becomes crystal clear. That’s the power of hindsight, and because it seems so crystal clear, doesn’t seem productive to have a debriefing, because you think you already know it all. The other issue is that postmortem briefings are constrained by time as well. You only have the conference room for an hour or two. Everybody is really busy, and the clock is ticking, so this is a challenge for doing this really well, even given those research methods.

The other issue, especially if you build a debriefing facilitation training program like I did at Etsy, there’s still challenges that show up. What I like to call it is, “Everyone has their own mystery to solve,” or, “Don’t waste my time on details I already know.” In a cartoonish way, you can think about it as this way:

Because you may only have an hour, you need to extract as much learning as you can. All work is contextual. Your job to maximize ROI is to discover, explore, and rebuild the context in which work is done in an incident, how work and how people thought above the line.

Assessments are trade-offs, and those are contextual.

In closing, all incidents can be worse. A superficial view is to ask, “What went wrong? How did it break? What do we fix?” These are very reasonable questions. If we were to take a deeper level, and we could ask, “What are the things that went into making it not nearly as bad as it could have been?” Because we don’t pay attention to those things and don’t identify those things, we might stop supporting those things.

Maybe the reason why it didn’t get worse is because somebody called Lisa, and Lisa knows her stuff. Something from research is that experts can see what is not there. If you don’t support Lisa, and you don’t even identify that the reason why it didn’t get worse is because Lisa was there. Forget about action items for fixing something for a moment. Imagine a world where Lisa goes to a new job.

Useful at a strategic level is a better question. “How can we support, encourage, advocate, and fund the continual process of understanding in our systems? And really take “above the line” in a sustained way?

Where do we go from here? I’ve got some challenges for you:

  1. Circulate the Stella Report in your company and start a dialogue. Even if you’re too busy or you’re not in a position to read it yourself, give it to people who do. Ask them what resonates. Ask them what doesn’t make sense. Ask them, start a dialogue.
  2. Look deeply at how you’re handling post-event reviews. Most importantly, go find the people who are the most familiar with the messy details of how work gets done and ask them this: “What value do you think our current post-incident reviews really have?” and listen.
  3. Take the responsibility to learn more and faster from incidents than your competitors. You’re either building a learning organization or you’re losing to one who is.
  4. We need to take human performance seriously. This discussion is happening. It’s happening in nuclear power. It’s happening in medicine. It’s happening in aviation, air traffic control, in firefighting.

The increasing significance of our systems, the increasing potential for economic, political, and human damage when they don’t work properly, and the proliferation of dependencies and associated uncertainty all make me very worried. If you look at your own system and its problems, I think you’ll agree that we have to do a lot more than acknowledge this problem. We have to embrace it. What you can help me with, please spread this information, these ideas and my presentation from DevOps Enterprise Summit San Francisco 2017.

John wants to hear from you. What resonated with you about this? What didn’t? What challenges do you face in your org along these lines? Go tell him – he’s on Twitter.

Attend the DevOps Enterprise Summit

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Enterprise DevOps: 4 Further Considerations for Metrics Measurement, Part 2

This is the tenth in a series of blogs about various enterprise DevOps topics. The series is co-authored by Sacha Labourey, CEO, CloudBees and Nigel Willie, DevOps practitioner.

In our previous article, we set out some principles on metrics and what and how to measure. In this blog, we’ll add a few more insights into metrics and their measurement.

Specific Considerations

As stated in our original article, we do not intend to define comprehensive metrics. We do want to take the opportunity to suggest a couple of areas we feel worthy of further consideration which are not regularly discussed. They are:

  • Lost opportunity time
  • Business value points
  • RAG status (red, amber, green)
  • Unexpected consequences of measurement

Lost Opportunity Time

Moving to a new way of working and a new culture is difficult. Indeed, it is often far more challenging than the technical challenges. In our experience, in large enterprises, rebuilding the bridge between the business and IT is a critical step. Sometimes IT commences the transformation without sufficient engagement with the business. The product manager is critical, ensuring that the correct initiatives are delivered at the correct time. To measure the success of business engagement, we recommend you consider measuring the time between any deliverable being available for release into production and the actual production release. Please note we are aware of the release challenges of approvals and sign-offs – this is not an attempt to measure this step.

In simple terms, “lost opportunity time” is an attempt to understand whether technologists are currently working on the correct initiatives for the business. If content is being developed which is not required for production release as soon as it is complete, one could argue that the individuals concerned would be more profitably engaged working on other initiatives. Using this metric, we believe business unit and portfolio managers could identify potential areas where effort could be better optimized.

A real-world example: the lost opportunity time metric was triggered by a conversation Nigel had with a team at a former employer, a few years ago. Everybody had been challenged to be more Agile, and a lot of teams were trying to do the right thing. Nigel was chatting to a team who advised they had moved to sprints and had now completed four sprints. He then asked if they had all made it to production, only to be told, “None have yet, as we can’t find anyone in the business to sign off on the release.”

Business Value Points

This is a common concept. The product team provides an indication of the effort involved in delivering each requirement in their backlog, by the same token business value points are a measure of the value to the product owner of each requirement. The rationale behind the two complementary measures is to enable a balance between effort and value to be achieved. This was adopted by the Agile community as an aide to backlog grooming. In our experience, you should not attempt to standardize the values of these points between product owners and teams; they have value within a team, but not between teams.

It is very easy to find yourself in a position where new, high priority deliverables are passed to the technologists regularly. It is less common for the business to volunteer that something that was a high priority has now assumed lower priority. The net result can be an increase in high priority items over time and the team spinning more and more plates. To an extent this can be inevitable, and increased throughput increases capacity, but it is a significant risk. Taking early steps to enable the enterprise to articulate value and demand can significantly enable the quality of decisions and agility.

A real-world example: a product team has been working through a backlog for some time. Each item on the backlog was provided with business value points by the product owner. We can show that 70% of the business value has been delivered from the backlog. From the effort points for the remaining requirements, we can see based on current cadence it will take six months to deliver the rest of the backlog. Further, no significant new requirements have been added to the backlog by the product owner for a couple of months.
Based on this information, technology management can have a conversation with the business about the likelihood that this product is moved toward BAU soon and members of the team potentially moved to new initiatives on the business backlog.

In short, using metrics of this type can enable the enterprise to better identify the point at which products move from active to BAU or, where resources are constrained, which current initiatives could be quiesced to enable new priorities to commence.

RAG Status

In a large enterprise, particularly prior to a DevOps transformation, the technologists often end up under-represented at a senior level within a company. Timeliness and budget are always discussed at a senior level, technical health less regularly. One initiative to encourage a level of discussion on technical health is to incorporate this as a core measure on any executive dashboards. RAG status dashboards (red, amber or green) are common in large companies. Project or programme managers will provide a RAG status for timeliness and one for budget adherence.

We recommend an additional RAG signifier to those that are traditionally discussed. Each product should have a technical lead who is accountable for the technical health of the product. They will have access to metrics that indicate the quality, and quality trend, of the product. There are several products available that provide this type of metric. The information about defects, architectural consistency, maintainability, etc. is not something that can be easily discussed at a senior level. A more useful approach is for the technical lead to use their judgment to provide a RAG status for product quality. This can sit alongside the other two indicators in discussions.

The relationship between the product owner and the technical lead is critical. The technical lead should be in a position where they feel comfortable recommending remedial or evergreening work on the product to the product owner, rather than pure business value delivery. The product owner should be confident enough in the technical lead to represent the need to carry out this work to the business stakeholders.

Real-world example: The quality RAG status is something Nigel has experienced, and has seen it reduce pressure on technologists to just deliver, regardless of the degradation in technical quality.

Beware Unexpected Consequences

Remember the act of measuring something amends behavior. You should be aware of unexpected impacts on behavior of any metric collection. Using the examples above, you could see:

  • Soft product launches to artificially reduce lost opportunity time.
  • Business value inflation of requirements to retain your product team.
  • Pressure on technologists to change their quality RAG status to avoid difficult discussions. In the extreme, objectives could be written that state “product quality is not to reach red status” which, while admirable, in one way can lead to a suppression of proactive reporting of issues.

Follow the Enterprise DevOps blog series from Sacha and Nigel:

  1. Enterprise DevOps: An Introduction
  2. Enterprise DevOps: I Wouldn’t Start from Here: Understand Your DevOps Starting Point
  3. Enterprise DevOps: Context is King
  4. Enterprise DevOps: Creating a Service Line
  5. Enterprise DevOps: On Governance
  6. Enterprise DevOps: The Spine is Critical
  7. Enterprise DevOps: Move to Self-Service
  8. Enterprise DevOps: The Peoples’ Front of Judea, or Don’t Sweat the Small Stuff
  9. Enterprise DevOps: 13 Principles of Meaningful Metrics Measurement, Part 1
  10. Enterprise DevOps: 4 Further Considerations for Metrics Measurement, Part 2 (this post)

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Enterprise DevOps: Principles of Meaningful Metrics Measurement, Part 1

This is the ninth in a series of blogs about various enterprise DevOps topics. The series is co-authored by Nigel Willie, DevOps practitioner and Sacha Labourey, CEO, CloudBees.

We shall start this blog by stating that we are firm supporters of the idea that the key metrics of IT performance are:

  • Lead time for changes
  • Release frequency
  • Time to restore service
  • Change fail rate

We also recognize that when running a program across a large enterprise, you will be subject to requests for other metrics from your key sponsors. These may include: Speed of adoption, impact of changes, cost of the program, any associated savings, etc. The key metrics are outcome metrics; however, there are also a set of lower-level metrics that drive the outcomes.

In this article, we are going to start by trying to define a set of principles around metrics. We shall not attempt to define a comprehensive set of lower-level metrics, as many of these are specific to circumstance or organization. Rather, we trust that the principles will act as a guide to enable you to understand what, and how, you should obtain meaningful metrics.

Principles of metrics:

1. Only collect actionable metrics – Ask yourself what you will do if your proposed metric changes? If you can’t articulate an action, you probably don’t need to collect the information.

2. Focus on the value to the business, not the level of work to IT – The fundamental rationale of DevOps is increasing business value. Your metrics should reflect this. Please note IT performance measurements are valid if they impact business value and meet condition one above.

3. Collect a few simple-to-understand metrics – Don’t turn metrics collection into an industry.

4. Metrics should be role-based – Business value metrics to the business, program-based metrics to program leads, technical debt metrics to technicians – understand the target community and rationale and don’t inundate people with metrics. We like the description that metrics should be offered as a “smorgasbord, where consumers select those which are pertinent to them for their personal dashboard.”

5. All metrics collection and collation should be automated – This should be self-evident to the DevOps practitioner, who is looking to automate delivery of technical capability. First, manual collection of metrics is time-consuming and counter to the program you are delivering. Second, you cannot obtain real-time information manually; as a result, you trend away from a proactive physical and, instead, toward a post-mortem.

6. All metrics should be displayed on a unified dashboard – Don’t expect people to hunt for them. This unified dashboard can be personalized for the consumer and the team, as per point four. The key consideration is that the customer should be able to find all the metrics they want in one place.

7. Prioritize raw numbers over ratios – With the exception of change fail rate which is correctly a ratio, we recommend the collection and display of raw data. This is particularly pertinent for metrics aimed at technicians. This both promotes the use of a holistic application of these numbers by the technical specialists within the team and reduces the risk of team-level metrics being used to compare performance across teams with no contextual understanding.

Because this is an important point, we are going to further explain. In most large enterprises, there is an annual discussion around levels of reward and bonus. Anything that is collected as a ratio can be grasped at by enterprise management to argue for their team. Nigel has been involved in many of these meetings over the years (far too many) and managers always fight for their teams to be highly rewarded when the organization starts fitting teams across a bell curve of performance. With ratios, you end up with conversations that go like this, “My team has a ratio of 75% and your team’s ratio is 71%, which supports my argument for a higher reward.” 90% of these metrics are meaningless at a comparative level and, by using raw numbers that the team itself can use to amend their behavior, you meet the primary need and reduce the risk of numbers being taken out of context and used for meaningless comparison. Of course, all meetings leak like sieves and the teams soon get to hear, rightly or wrongly, that their bonus was affected by, for example, the ratio of new lines of code compared to amended lines. They then amend their behavior to impact their bonus, not the organization’s needs.

8. Use the correct metric for the use case – Different use cases demand different metrics – for some products, it is velocity; for others, it is stability and availability. This principle is for the consumer of the metrics rather than the supplier, but it is critical. Our blog on context should make it clear that the primary success indicator for each product may not be the same.

9. Focus on team, not individual metrics – DevOps looks to drive a culture of cooperation and teamwork to deliver success. As your culture starts to change, you should see a greater focus on the recognition of teams rather than individuals. To support this, your metrics should focus on this too.

10. Don’t compare teams, compare trends – If we accept point 8, that different teams have different primary metrics, we should also accept that each team will have different goals. Additionally, if raw data is used for many metrics it makes little sense to compare teams. Rather, the product teams, business units, and key sponsors should compare trends within their teams and units.

11. Look for outliers – While avoiding direct comparisons between teams, it is still sensible to look for outliers. If these are identified, you should look for clues as to why certain teams are either significantly over or underperforming their peers. These can often provide significant learning points that add value to others.

12. Lead time is time to production, not time to completion – This is a fundamental principle. It is repeated here, as from our experience initial stages of adoption are often accompanied by a focus on reducing the time to production readiness. The last step of continuous delivery is often a follower, and it is critical that lead time measures time to production and nothing else. You should also be wary of soft launches being adopted if the formal production release to market is not a close follower.

13. Use secondary metrics to mitigate unintended consequences – For example, a focus on time to market could negatively impact quality, which is why the key metrics of IT performance contain both. If you focus on a specific metric, you should ask what the negative impacts of that focus could be and monitor trends in this space. This applies even if you have taken the conscious decision that you are happy to suffer these consequences.

In our next blog, 4 Further Considerations for Metrics Measurement, we will take the opportunity to suggest a couple of areas we feel worthy of further consideration which are not regularly discussed in organizations.

Follow the series from Sacha and Nigel:

  1. Enterprise DevOps: An Introduction
  2. Enterprise DevOps: I Wouldn’t Start from Here: Understand Your DevOps Starting Point
  3. Enterprise DevOps: Context is King
  4. Enterprise DevOps: Creating a Service Line
  5. Enterprise DevOps: On Governance
  6. Enterprise DevOps: The Spine is Critical
  7. Enterprise DevOps: Move to Self-Service
  8. Enterprise DevOps: The Peoples’ Front of Judea, or Don’t Sweat the Small Stuff
  9. Enterprise DevOps: 13 Principles of Meaningful Metrics Measurement, Part 1 (this post)

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Q-and-A With Mirco Hering and Gene Kim

Recently, Gene Kim had a chance to speak with Mirco Hering about Mirco’s new book, DevOps for the Modern Enterprise.

Gene Kim: What inspired you to write DevOps for the Modern Enterprise?

Mirco Hering: To be honest, there are a number of DevOps books in the market and I was wondering whether I had something unique to say. But then I realized that not a lot has been written of the complex organizational environments that people find themselves in with legacy applications and multiple vendors working along with people from their own organization. I wanted to address the challenge of a management mindset that was finely tuned for a different world where IT was considered to be predictable. I wanted to share what I have learned about those environments, and what I’ve learned from many failures and near-misses as well as my successes. My goal was to help with some simple activities that everyone can do in their own organization and to help the reader set themselves up for a successful transformation into a modern enterprise. I hope to be able to reach more people with this book than I would ever be able to personally work with, and help them on their journey to a better more positively exciting workplace.

GK: You describe vividly the incredibly complex ecosystems that enterprises and their consulting/system-integrator partners operate in-what would you want everyone in both parties to know that would help them?

MH: First that, in my experience, both sides genuinely want to find win/win scenarios and do the right thing. Engagements and relationships were created with the right intentions originally. Over time the environment and capabilities changed, but we did not keep up with the contracts and incentive models used. We still have the previous mindset and knowledge of what good contracts look like. It takes courage to question those and change the rules of engagement for the DevOps and Agile way of working, but it is worth doing as both sides stand to win from it. Find people on the other side that have the same interest and start talking directly (and, ideally, face to face) and you will be able to shift the engagement over time one step at a time. The game of telephone that is sometimes introduced by having several intermediaries is distracting from the real goal. Find the right person in the other organization that has the same interest and initially find creative ways to make it work with sub-optimal contracts in place. You don’t have to throw existing contracts out and start from scratch. Make sure you are on the same page and find small creative increments of change. It’s amazing what can be done when people on both sides are on the same page and push in the same direction.

GK: If your readers take away just one thing from your book, what do you hope that would be?

MH: I hope they find at least one exercise that they will run in their organization, and learn something useful for their transformation from the book. The one thought I want them to take away is that you don’t need complex frameworks to drive your transformation, but rather that you need the right mindset and principles to drive it, everything else is additional help that you can tailor to your needs. Implement a strong continuous improvement process based on the scientific method to drive your transformation. And remember that the mindset that made IT successful in the past is not the mindset that we need now and will need in the future. Okay, I guess that was two things, but I think they go hand-in-hand.

GK: You state that “cultural change that is the hardest is also the most impactful.” For something that feels so abstract, what one concrete change can your readers implement today to help them down the path of a broader cultural change?

MH: A truly open-minded and blameless continuous improvement process is a very good central change engine for cultural change. It should span across organizational boundaries. Once you are in a room where people from all across your organization and from your vendors collaborate to find ways to improve, you will know that the culture has shifted. I found that collaboration is easy when it is just one on one, but that culture manifests itself when larger groups come together. The before mentioned rigorous experimentation and evaluation process to assess progress at the core of continuous improvement takes the personal agendas off the table. This process can be aimed at all aspects of IT delivery, your overall business processes and your engagements with stakeholders. As soon as this process is somehow constrained or involves finding blame or excuses, it loses many of its benefits and negative culture will sneak back in. Once results improve through the right behaviors, your culture will start to shift too. You cannot change culture by itself, in my opinion; you have to achieve results with a new way of working which will, in turn, shift the culture.

GK: What is the biggest challenge(s) facing legacy IT organizations today?

MH: The biggest challenge for legacy IT organizations is that the complexity of their IT environments is shaping their worldview. They see it as a reason for why change is not possible. While it is true that it is harder, it is absolutely possible. The problem is that looking at “unicorns” and trying to emulate them is not going to make you successful. You cannot just use the “Spotify-model” or adopt the “Netflix-culture.” You have to do the hard yards of adapting your own organization in your ecosystem to the new world. The good news is that it is fun once you start understanding what success feels like, and I have seen many organizations starting to make that shift.

GK: In your vision, what will the modern IT organization look like in ten years? What will be their biggest challenge(s) and their biggest strengths?

MH: Looking 10 years in the future basically sets me up for an embarrassing follow-up interview, so let me make this follow up entertaining by pushing the boundary. The modern IT organization in 10 years will use a mix of technologies (cloud, on-premise, custom build and packaged) similar to many today, but has detangled them to evolve at the appropriate speeds independently from each other. Business stakeholders work in end-to-end teams that operate and change the systems which run the business and leverage commodity services from in-house and external providers for most of the non-differentiating aspects of their IT landscape. The challenge will have moved from a problem of scarcity (too slow, too expensive) to one of plenty (lots of change, fast-moving). We will write blog posts about the need to not react to every data point or trend and to work on “out of the box” innovation that requires us to stay a course for a while even when data and customers suggest we need “faster horses” to develop the equivalent of “cars.” IT challenges will not be technology issues, they will be business challenges of identifying what is providing value and what is unnecessary and can be cut back. Data aggregation and synthesis will be at the core of IT as the remaining constraint and artificial intelligence is employed as a partner in our governance processes.

DevOps for the Modern Enterprise by Mirco Hering is now available for purchase at all major book retailers in print, ebook, and audiobook formats.

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DevOps and Continuous Delivery: Time to Thrive in the Digital Age

As digital technology continues to disrupt and transform businesses across industries and around the world, the ability to rapidly deliver high-quality software will make the difference between survival and extinction for many companies. Ultimately, successful adoption of DevOps and the process of continuous delivery are likely to determine whether an organization thrives or fails in the digital age.

DevOps is a practice that gives organizations the ability to develop and deploy software faster and more efficiently, enabled by automated processes such as continuous delivery. DevOps focuses on cultural transformation and making it easier for development and operations teams to collaborate and achieve shared objectives. Continuous delivery is a process that lets developers continuously roll out tested code that is always in a production-ready state. With continuous delivery, application development teams use automation to deliver updates faster and with fewer errors. Once a new feature or update is complete, the code is immediately available for deployment to test environments, pre-staging or live production.

Organizations Save Big With DevOps and Continuous Delivery

Together, cultural change and process automation accelerate the creation and delivery of high-quality software, saving many organization millions of dollars and hundreds of thousands of developer hours every year that can now be spent on innovation and not on administration.

A CloudBees assessment of more than 100 companies that have adopted DevOps found that continuous delivery processes helped them save an average of $3,500 and 66 hours per developer, per year. The CloudBees analysis, which is based on a conservative cost of $53 per hour for a developer, includes companies that represent a variety of sizes, industries, and regions. These organizations employed an average of 1,530 developers across 11 teams, which translates into annual savings of $5,355,000 and 100,980 hours, a gain of 12,622 development days per year. For larger organizations, the gains are even more impressive. A company with 10,000 developers, for example, could save $35 million and 660,000 hours annually, for a gain of 82,500 developer days every year. What’s the true value of these cost and time savings? Returning hours back to developers, to focus on innovation that keeps a company ahead of the competition, retains existing customers and attracts new ones.

The impact is compelling across industries, company sizes, and geographic regions. For example, Capital One has increased deployment frequency by 1,300%. “With the CloudBees Jenkins Platform we’ve created a service for our developers that’s scalable and stable,” says Brock Beatty, director, software engineering, Capital One. “As a result, the time they would’ve spent managing infrastructure is now spent developing business applications. That has contributed to our ability to increase deployments from a couple per year to now deploying every two weeks.”

The value is visible for small-to-medium sized organizations as well. “In thinking about how we can deliver better products faster for our customers, we adopted a service-oriented mindset – breaking systems and workflows into smaller pieces that can be delivered or executed quickly with an automated pipeline,” says Jack Waters, senior vice president of engineering at WatchGuard, which provides enterprise-grade network security appliances and wireless security hardware. “We’re now able to get big things done noticeably faster. Whether it’s implementing encryption or changing the way our back-end databases are set up. Activities that would take months are now taking days to weeks to complete.”

DevOps and Continuous Delivery Are Good Business

The value of adopting DevOps and continuous delivery is not just about improving the software development process; the ultimate advantage is the array of business benefits it delivers.

DevOps and continuous delivery allow organizations to drive innovation that sharpens their competitive edge while reducing costs, increasing revenue and ensuring faster time to market. Using DevOps and continuous delivery, companies can improve collaboration and productivity, while reducing risk. They’re also able to strengthen brand equity, improve customer service and satisfaction, and create a working environment that makes it easier to attract and retain top talent.

Hurwitz & Associates recently completed a CloudBees-sponsored study of 150 top IT decision makers, 77 percent of whom reported either company-wide or business unit implementation of continuous delivery. When asked how implementing continuous delivery had affected their business, 81 percent said continuous delivery is helping their organization bring value to customers and deliver on business goals. Approximately 44 percent reported significant improvements in their organization’s ability to provide customer value and meet business objectives. Equally important, none of the participants reported a decline in meeting business goals after implementing continuous delivery.

As accelerating innovation and responding rapidly to shifting market and customer demands becomes more critical for the success of every organization, a growing number of business leaders are recognizing IT as a strategic asset. Being able to shorten application delivery times, improve software quality and quickly adapt to change while dealing effectively with security, availability, and compliance is just the type of competitive advantage organizations are seeking.

“We can’t afford to be complacent about what we have already offered; it’s about what else we have to offer our customers,” says Waters. “Having a healthy continuous delivery pipeline has really helped us stay competitive, maintain the level of quality that WatchGuard is known for and deliver new products with a high degree of confidence.”

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How Do I Find and Fix DevOps Bottlenecks in Enterprise Software Delivery?

DevOps, like Agile, has transformed enterprise software delivery. Thanks to sprints, prioritization, CI/CD, and release automation, organizations are building and deploying software products faster than ever. That pesky bottleneck between code commit and deploy has been all but eliminated, which should ensure better time to value for customers.

Yet if your flow time – i.e. end-to-end lead time – is still too long, unpredictable, and unmeasurable, it’s likely you’ve only shifted the bottleneck further upstream. Sure, automation has sped up handoffs and communication between developers and operations, but what about everything else that happens in the process?

What about all the other manual processes that take place before and after a piece of code is written? If there are still manual handoffs at key stages of the process, then your overall workflow is still being impeded by bottlenecks outside of the DevOps stage.

Download this short e-book to learn how to target DevOps bottlenecks via connected lifecycle data

As Dominica DeGrandis, our Director of Digital Transformation, explains in her latest article for TechBeacon, you can only identify and remove these bottlenecks if you can see them. A LOT happens before “Dev” and after “Ops.” A lot of creative thinking and activity ensures the right product is built, maintained and delivering value to the end user. And unless you can trace and automate the flow of work from ideation to production, you won’t be able to optimize the process. You need to collect and consolidate all data that pertains to planning, building, and delivery of the product.

So how do you avoid bottlenecks and accelerate your DevOps (and other IT) transformations? First, you need to ask some important questions:

  • Get the right metrics – are you measuring right thing?
  • Do you understand how value flows across the process?
  • Can you easily obtain real-time metrics across the process?
  • Are you able to produce accurate traceability and other performance reports?

If the answer is “no” or you’re not sure, then it’s likely your software delivery value stream is still a mysterious black box of activity, and not optimized as a result. With no visibility into the end-to-end process, how do you know where to look for bottlenecks? How do you where the opportunities are to create more value?

The good news is that you can “reveal” and optimize the software delivery process by connecting and automating the flow of work between teams and tools via value stream integration.

For a deeper look into how to find and remove bottlenecks, check out Dominica’s piece Break through those DevOps bottlenecks.

Download this short e-book to learn why your Agile and DevOps initiatives are struggling to scale

For a more dynamic discussion, request a personalized demo of your software delivery value stream. We can help you connect your value stream network, spot bottlenecks, and dramatically improve how fast and well you deliver innovative software products.

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How COBOL Persists: People, Process, and Technology


Over the years, I’ve seen technology trends come and go. Some to change our lives forever, some to disappear without a trace. However, in the most successful cases, the technology has become mainstream thanks to refined processes for its use, and a tidal wave of enthusiastic, skilled practitioners. No-one is scared of their mobile interfaces anymore, and Smart TVs are easier to operate.

In the same way that devices have become commonplace, a focus on programming has come to the forefront. However, even programming languages come and go with the times. Remember when PERL was all the rage? Ruby on Rails? Flash? Now languages like C#, Python, and probably the best example, JavaScript, are popular and simple to learn. Meanwhile, there is one programming language that, despite the claims of its impending demise, has stood the test of time, and continues to evolve for the modern workforce-COBOL. In my ignorance, I questioned whether what was clearly a ground-breaking innovation decades ago was going to be able to remain relevant in 2018. After looking further into things, I found that my doubt was completely unfounded.

An Age-Old Tradition

It was only very recently the last year or so that I learned about COBOL technology. COBOL, the Common Business-Oriented Language, was developed nearly 60 years ago by a small group of computer professionals called the Conference on Data Systems Languages (CODASYL) that included Grace Hopper, the “mother of COBOL“, and Jean Sammet. The main objective of the committee was to develop a standard business-oriented language. COBOL was ground-breaking because it could run on more than one manufacturer’s computer. Because of its ease of use and portability, and because the US Department of Defense required COBOL on all its computer system purchases, COBOL quickly became one of the most used business programming languages in the world.

Interestingly, as I looked at the phenomenal success and continued pervasiveness of COBOL it became clear that it was founded on strong technology, clearly, but also that the processes built to leverage it were simple and clear, focused on business outcomes, modelling business processes; and the people side was taken care of because COBOL was easy to learn – its English-like syntax was designed with ease-of-use in mind. Little wonder it’s one of the few genuine constants in the IT world.


Looking across the three scales of people, processes, and technology, the facts made it clear. I’ll start with technology – after all, that’s what COBOL is.


From what I read initially, I heard People often complain or worry about businesses, banks, and government agencies using and the pejorative “legacy” code label.

While the concept of COBOL is from 1959, today’s incarnation of COBOL is modern and far from what I (or anyone else) should consider legacy. It possesses the same contemporary look and feel of any other language, works within standard development IDEs like Visual Studio and Eclipse, supports the latest releases of enterprise technology such as Cloud, Mobile, Managed Code, can live inside a Virtualised or Containerized environment, can support web services, object orientation and an API model. In short, whatever you are doing today, your COBOL systems will support it.

Moreover, COBOL still does all the things it’s known for, precise and rapid calculations, managing massive amounts of data, precision and accuracy and reliability that the enterprise systems it powers rely on. In 2014, the Wall Street Journal reported that each day 80 percent of the world’s business transactions rely on COBOL. COBOL is contemporary and is strongly entrenched in businesses around the world.

Mike Madden, development service manager with the British women’s clothing firm JD Williams, was asked why they still use COBOL on their mainframes. His reply: “Simple – we haven’t found anything faster than COBOL for batch-processing,” Madden says. “We use other languages, such as Java, for customer-facing websites, but COBOL is for order processing. The code matches business logic, unlike other languages.” According to IBM’s Charles Chu, “…there are 250bn lines of COBOL code working well worldwide. Why would companies replace systems that are working well?”

Unsurprisingly the well-known Computerworld survey from a few years back found that 64% of respondents stated that their organization or systems used COBOL, and 48% used it significantly. More recently a survey by Micro Focus revealed the top modernization priorities, which reported;

  • 85% of COBOL applications are strategic
  • 65% rank knowledge transfer as the critical skills issue
  • 52% of COBOL developers are using Agile practices

Which brings me on to the other two important criteria: processes and people.


The modern era of computing has witnessed a significant shift in how applications are being brought to market. The Agile era is upon us and statistics show anything up to 80%+ adoption of the modern “DevOps” process of building applications.

It begged the question; does that preclude old-school approaches like COBOL?

COBOL technology, in its modern incarnation, is a modern language and works alongside and within agile and DevOps development practices. Indeed, COBOL’s pervasiveness, ease-of-use, and integration with contemporary toolchains make it an ideal candidate for DevOps-style process improvement, whether on the mainframe or in a distributed environment.

…and People

Another major concern with COBOL is the fact that many of the programmers are aging and retiring. A Computerworld survey found that more than half of COBOL programmers in organizations surveyed were over the age of 45. Fast forward a few years and the 2017 mainframe survey by BMC talked about a resource pool half of whom were under the age of 50, suggesting a significant shift in the demographic of the COBOL IT shop.

In 2013 Micro Focus collected research on the lack of COBOL courses being offered in academia. Shockingly, despite the fact that 71% of university respondents believing that today’s businesses will continue to rely on COBOL code and applications for the next 10+ years, only 27% offer IT courses that include COBOL programming as part of their curriculum. More recently, evidence suggests a change of attitude. IBM has been working to keep mainframe skills alive in younger generations with its “Master the Mainframe” contest, as well as formal degree programs. Micro Focus has furnished a group of over 300 academic partners that teach students COBOL with contemporary COBOL technologies. In fact, Micro Focus taught a class at the most recent SHARE Academy and also spoke on the topic of skills at the SHARE Sacramento event. In his recent blog post Derek Britton, Director of Strategy and Enablement at Micro Focus, asserted that, “a combination of attitude, motivation, and support… technology is the key to overcoming any skills concern.”


In my short time learning about the COBOL world I am astonished more isn’t said about its power and prevalence in the IT world. If the success of a technical innovation can be measured across three axes of people, process, and technology, then the scores for COBOL in 2018 remain very impressive. Micro Focus remains committed to COBOL, its pervasiveness should be no surprise to any of us.

Learn More About COBOL

If any of this information about COBOL is news to you, I urge you to learn more.

Share your thoughts with us by leaving a comment, or find @MicroFocusCDMS on Twitter, or create a post on the Micro Focus COBOL Community page.

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Enterprise DevOps: Move to Self-Service

This is the seventh in a series of blogs about enterprise DevOps. The series is co-authored by Nigel Willie, DevOps practitioner, and Sacha Labourey, CEO, CloudBees.

Hopefully, most of you have read The Phoenix Project. If not, we highly recommend it. (It is actually required reading for all new CloudBees employees.) Those of you who have done so will be aware that one of the critical issues experienced by the author was a critical dependence on Brent, the key technologist, who solved everybody’s problems. It becomes clear in the book that Brent, as competent as he is, is a logjam as everything passes through that one individual.

Any centralized team introduces exactly this risk. It is one of the fundamental objections that many people raise whenever the idea of a central DevOps team or function is raised. We acknowledge this is a valid concern. Our previous article on creating a service line advises some potential approaches.

Regardless of the structure, or structures, you choose it is critical that you avoid becoming a bottleneck to progress. In a large enterprise, this can be very difficult, as DevOps initiatives tend to be high profile, flagship programmes. As a result, senior stakeholders are extremely keen to demonstrate rapid progress. This, of course, is a significantly better problem to face than customer apathy! Something it sometimes pays to remind yourself as you try to keep many plates spinning.

Many vendors now deliver capabilities as services. For example, CloudBees Jenkins Enterprise provides a delivery framework that couples a level of central control and consistency with customer self-service. It also decomposes the previous architecture based around larger, shared masters to a more flexible approach based around individual team- or project-specific masters, spun up in minutes. All this supports a self-service approach to be pursued in a larger enterprise.

Whether using external or internal capabilities, we recommend that you prioritize customer self-service as a core competence. There are fundamentals that need to be completed to achieve this; exposure of services via API’s, defined patterns that integrate into an end-to-end flow, templated pipelines with drag and drop capabilities, etc.

Today, many larger enterprises follow a matrixed organizational structure. We do not intend to discuss the advantages of various organizational structures in these articles, we would, however, pass on specific advice for anyone working in an organization of this type. It is a given that any service will need maintenance and upgrades. These potentially impose a short service outage. From experience, if a service is shared across business streams within the organization, agreeing on an outage can involve significant negotiation. It is human nature to believe your personal priority has primacy. In matrixed organizations, cross-business stream priority requires consensus. When architecting any service, in addition to the usual considerations (availability, scalability, service latency, etc.) it is useful to consider this factor when allocating consumers to servers, masters, availability zones, LPAR’s, etc.

In short, key considerations when providing a centralized capability should be:

  • Your customers can achieve self-service of the capabilities they require wherever possible.
  • Any service should, wherever possible, consider segmentation via customer to assist prioritization discussions around outages.
  • Once the fundamental foundation has been created, your focus should be on customer experience and ease of adoption.

The role of any central IT team is to enable its customers to deliver rapidly via fully supported, consistent automation capabilities. It is not acceptable to become an impediment to progress to the entire enterprise by imposing dependencies on one group of individuals.

Follow the Series From Sacha and Nigel

  1. Enterprise DevOps: An Introduction
  2. Enterprise DevOps: I Wouldn’t Start from Here: Understand Your DevOps Starting Point
  3. Enterprise DevOps: Context is King
  4. Enterprise DevOps: Creating a Service Line
  5. Enterprise DevOps: On Governance
  6. Enterprise DevOps: The Spine is Critical
  7. Enterprise DevOps: Move to Self-Service (this post)

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