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Tag: Lean

Lean Value Stream Mapping for DevOps
Value Stream Mapping is a crucial step in assessing an organization’s DevOps capability. We create a value stream map of the software development lifecycle early in any DevOps engagement, because it helps us to-
- Provide context for our architects and technical experts who are, in parallel, examining tools and technology.
- Establish a performance baseline, against which the improvements delivered by adopting DevOps will be measured. (See my other blog post on Box Scores for more on this).
- Begin the process of winning hearts and minds, and engender cultural change. By working side-by-side in a workshop, Dev and Ops resources, as well as those involved in planning, test scheduling, and other activities, start to gain an understanding of how the work they do is linked to steps upstream and downstream. This lays the groundwork for closer coupling of those activities in the future.
What’s interesting is that the template we use for our DevOps Value Stream Map is almost identical to that which we use for other service processes. It’s been used by IBM Lean Coaches to assess processes as diverse as Marketing Campaign Development, Order-to-Cash, Mortgage Applications, and Production Support to name but a few. This template was evolved from more traditional Value Stream Maps used to capture manufacturing processes, and a slightly different template also exists for innovation or product development processes.

At the start of every Lean project – whether it’s in DevOps, Insurance Claims, New Product Development, or Financial Forecasting, I review the Value Stream Map template. I ask myself and my team if the questions make sense for this process and if the boxes we are seeking to fill are relevant. Some of the language might be slightly different, and the outputs will certainly differ – from a high frequency process where a step takes minutes and seconds, to a creative process where a step could take weeks. But almost without exception, the template remains the same.

We ask the same questions across all these types of processes – and now we ask those questions of the Software Development Lifecycle. Those questions are:
- What activities are being performed?
- Who delivers those activities? How much of their time is dedicated to those activities?
- What is the yield, i.e. ‘right first time’? What % of rework is generated? What causes rework?
- What tools are required to complete the step? And what other inputs are required?
- How many units (e.g. work packages) are ‘work in progress’ within this step?
- How many units are waiting in the ‘inbox’? How will they be prioritized or scheduled? How long will they, typically, wait for?
- How long does the step take? How much of that time is effort, or touch time, versus duration, or in-process lead-time?
When all’s said and done, all Value Stream Maps have something fundamentally common: they are all about flow – and DevOps is no different.

About the Author/ Katie Sotheran

Katie is an IBM Lean Master based out of Los Angeles, California. Katie has many years applying Lean principles to processes as diverse as beverage distribution in Spain, lubricant product development in the Netherlands, Insurance Sales in the UK … and IT operations, all over the world.
May 19, 2015
Why do DevOps? Winning hearts and minds with a Lean boxscore

Change is difficult. It can be painful, disruptive, and costly. Making a case for change requires us to win over hearts and minds. In my experience, we need to win the hearts of the ‘do-ers’, the people whose daily work is going to be disrupted. And the minds of the executives, whose financial support and personal sponsorship we need to make change happen.

A Lean boxscore allows you to address both the hearts, and the minds. Boxscore is a simple tool which we use to compare the current state with the future state, and turn that comparison into a compelling case for change.

Step One: Current State Baseline
Let’s start with where we are today, by capturing performance metrics that tell an accurate story of today’s development lifecycle. This may mean looking beyond the project plan to the reality, by the way. By interviewing or workshopping with development and operations teams, we get a true picture of the way the development process currently performs.

We’re always interested in end-to-end leadtime, of course – from the moment the business provides a prioritized set of requirements, to the moment that functionality is in production and working as intended. We’re also interested in how much wait time there is within the process – how much time between steps, waiting for an approval, a developer, or a tester. We do this by creating a value stream map, capturing all those metrics and validated by the teams who work within this process every day.

We want to look beyond leadtime and wait time too. We capture the number of resources involved in each release, the number of handoffs, and the number of bugs caught at each stage of development and deployment. If we can get to it, we might capture the percentage of time a developer spends writing code for their current release, as opposed to firefighting issues from the last release, arguing over schedules, trying to secure an appropriate environment, or other ‘non value adding’ activities. We might also capture some usage statistics, or other measure of customer satisfaction, and any relevant performance indicators, like server response time.

Before moving on, it’s crucial to ask stakeholders: ‘is this a fair reflection of the way things currently work?’

Step Two: Future State Target
This next step requires some imagination, and some bold thinking. Imagine a world where you’ve adopted DevOps: you’ve removed the waste within the development lifecycle and instilled the ‘shift left’ principle in all your working practices, your ‘4 in a box’ teams are rapidly developing, testing and deploying code, Cloud-based environments give you immediate provisioning, and automation and smart tooling have taken the bellyache out of testing. What does that future look like? How quickly can you develop – if provisioning is immediate? How many bugs do you expect to put live – if testing is automated and your production environment matches your test environment? How many changes to scope – if you’re working with smaller batches and faster delivery?

We create a new value stream map, which details the way this brave new process will work in practice. This allows us to set sensible but challenging targets for our future state.

Before moving on, be sure to ask stakeholders: ‘are these targets achievable, if we make the changes we have outlined?

Step Three: So What?
Finally, we get to the money question. What’s it all worth? For each improvement there will be an impact. Many of these will be financial. Ask your stakeholder ‘if we can get this new functionality out to market 2 months faster, what does that mean for revenue?’ Or ‘if we reduce the number of defects in production by 90%, can we reduce our operating costs?

Putting a dollar value on the financial impact will win over those minds with a logical, fact-based argument. Now, investing in a DevOps pilot seems like something they can’t afford NOT to do.

And the case for change for hardworking developers, testers and production resources? Metrics like number of handoffs, bugs and rework, and batch sizes can easily be translated into ‘how much pain you have to live with each day at work’ to win those hearts over.

April 9, 2015
Think you’ve established Kanban? Why you might only be halfway there
As a Lean Coach working in IT I often hear development teams refer proudly to their scrumboards as an example of Kanban. To which I say, ‘that’s a great start… but it’s really only just the start’.

Let’s start with what a scrumboard is, and does. A scrumboard is a visual display of work, showing the progression of user stories in development. As you see some stories moving through the phases of development, and others getting stuck, you huddle to figure out how to get them unstuck. Now you’re using your visual display for visual management, which is a great Lean solution.

Change management boards, like the one used in The Pheonix Project, are another good example of visual management. The physical display of changes, shown by release date and system, is a great way to get a handle on the amount of change and risk you’re throwing into live production. When you start moving them around to limit the risk, again, you’re using visual management.

The word ‘kanban’ roughly translates as ‘signboard’, and a scrumboard really is a good start. But it’s only half the story. Until you limit the number of stickies on your change management plan, or the number of story cards on your scrumboard, you’re really only half way to kanban nirvana.

True kanban requires you to limit the amount of work in progress and to establish the pull of work. In a manufacturing plant, a kanban card is attached to each unit in production. When a completed unit is, say, taken off the shelf by a customer, the card is sent back into the production line to signal that there is now an empty slot on that shelf which needs replenishing. Limiting the kanban cards sets a constraint around the amount of work in progress – and this limit should reflect the capacity of the process. In the same way, you can limit the number of story cards, boxes on your scrumboard or spaces in your change management plan. When a scrumteam complete a story that frees up a slot for them to pull the next story into their workload: the visual display should reflect this by showing each team’s capacity. In addition to visual management, you can create rules around work in progress, requiring each scrumteam to limit their intake so they have no more than, say, two workpackages in progress at a time.

This is all well and good, but what do you do when demand from the business requires a greater capacity? Historically you’d have thrown that extra demand into the mix, asking your teams to do more with the the same amount of resource, maybe pushing back a release date to accommodate the extra work.

Kanban requires you to create additional capacity in the system to allow for the additional demand either by:
- rebalancing the load, for example switching some resources from a work package that is ahead of schedule
- removing or deprioritizing other work and putting work on hold
- increasing resources by bringing in new team members
All of these actions will generate some churn, so it’s going to be worth a tough conversation with your customer first. ‘Are you sure you want me to disrupt the current sprint, or the next release, to squeeze this in? How about we pick it up next week, when we’ll have some new capacity?’ By limiting your work in progress, you’ll be speeding up the flow of development into production, so you can make that ‘next week’ promise with increasing confidence.

So the next time you walk over to that scrumboard, why not take the next step, and limit the work in progress to truly establish kanban.
April 8, 2015
Five essential Ops strategies that help make DevOps the real deal
With its roots in Agile and Lean thinking it’s not surprising that DevOps is often looked at through a development lens. After all, it’s proving absolutely essential to support accelerated delivery times; providing a much needed jolt of velocity to protracted development cycles. But of course with the increased need for speed there’s always the danger that quality can be compromised – so much so that faster time-to-value can very quickly become faster-time-to-failure.

In what seems like a byzantine age of enterprise computing, IT operations tended to call the shots when it came to delivering quality. Sure, a transactional system might suffer a glitch or two, but since constituents were largely internal and customer’s generally one step removed from the application and business process, the impact could often be controlled. Ah, so much for the good old days.

But as we know this model hasn’t just changed, it’s been completely disrupted.

Quality is now a different headed beast that bites hard in many places. It bites when customers engage directly with your brand during discrete mobile moments – like when they compare your goods in the shopping aisle with a mobile app, or assess car insurance based on your ability to provide usage-based policies from their in-vehicle software system. Yes, speed is important in the application economy, but it’s also a high quality customer experience that shapes the winners from losers.

The fabulous thing about DevOps is the fundamental premise that teams can collaborate in real-time to establish, maintain and continuously improve the quality of software systems and applications. For operations this doesn’t mean waiting for applications to reach production stage before enacting performance management processes. Rather, it involves instantiating best practices throughout the development cycle – that way teams resolve quality issues much earlier in development (where they are cheaper to fix), while also preventing a cumulative buildup of crippling technical debt.

So with application quality and customer experience being essential for DevOps success, what are some of the ops related strategies we can employ to support better business outcomes?

I see five essential, ‘rubber meets the road’ strategies ops can use to help DevOps become the real deal:
1. Focus collectively on the user experience – too often organizations become detached from business reality by looking inside-out from a silo’d technology and diagnostic perspective. DevOps practitioners put themselves in the shoes of the customers by constructing, visualizing and managing end-to-end user-centric service views that traverse functional boundaries. Additionally, user-experience management will involve gaining deeper insights into real-time performance of externally facing applications and mobile services, bringing to the forefront analytics to guide decision making and drive continuous improvement.
2. Give Developers a clear view of how code works in the real world – operational intelligence shouldn’t wait until applications are moved into production. Rather, it should be instantiated across every stage of the lifecycle – whether that’s during regression and unit testing, or pre-production. Many teams recognize this by for example activating monitoring during release automation; giving developers critical insights into both functional and non-functional code behaviors. Others go further, taking user-experience to another level by exposing key behavioral and usage-based intelligence – especially for newer mobile apps, APIs and digital services.
3. Create a single source of the truth using a common framework – to be fully effective, developers and operations should reference a single version of the truth when it comes to problem triage and resolution. Traditionally this has proved difficult with teams having discrete tools providing diagnostic insights into one aspect of technology. This however falls short due to the complex interdependencies across technologies and results in finger pointing and blames games when problems can’t easily be pigeon-holed. To address this, operations teams use a common unified framework that quickly discovers and exposes infrastructure and application configurations, and importantly the relationships and interdependencies that exist between them. Now, root-cause can be quickly established from either an application or infrastructure perspective, but always prioritized according to business and customer impact.
4. Define common goals across teams and use shared metrics – traditionally development and operations teams have been incentivized according to separate goals that are often diametrically opposed (e.g. speed of producing code –v- resilience and availability). By using shared customer and business-centric metrics, developers and operations build a ‘common language’ for evaluating progress and understanding the impact of change and continuous delivery. My advice here is quality over quantity; building out metrics that are actionable by all stakeholders. For example, this could include lead and cycle times to pinpoint operational bottlenecks and waste during deployments, or transaction response monitoring in pre-production to pinpoint code deficiencies and avoid technical debt associated with provisioning unnecessary capacity.
5. Converge around a common understanding to transcend organizational boundaries – a collection of diagnostic based monitoring and alerting provides little more than vanity metrics and feel-good green lights. Sure, they can alert operations to when something is broken, but they do little to drive the more essential element of DevOps success – feedback. Therefore common understanding involves operations automatically correlating performance information end-to-end so as to expose far more beneficial insights that developers can use to improve the code base.
At the recent Gartner Data Center, Infrastructure and Operations Management Conference in Las Vegas in December, I presented a session – Unified Infrastructure Management is Critical to Enhancing the Ops in DevOps – that further described these strategies together with supporting use-cases. It was certainly a dynamic and lively session, highlighting the viability of DevOps. As organizations look to increase speed to market of new applications while both lowering cost and increasing quality, DevOps will continue to gain traction, and I can’t wait till everyone truly understands its transformative power.

About the Author

Aruna Ravichandran is vice president of DevOps Solution Marketing and Management at CA Technologies, responsible for cross-portfolio solution marketing for DevOps across all product lines. Before joining CA Technologies, Aruna held several management positions in product marketing and engineering at Juniper Networks and HP. Aruna earned an MBA and master’s degree in Computer Engineering from Santa Clara University and holds a bachelor of science degree in computer science from Bangalore Institute of Technology. Ravichandran is on twitter @aruna13 and on linked in
January 9, 2015
The DevOps Culture Cocktail

As I have written in previous posts, I believe that IT has accumulated decades of cultural debt that is now due. We are a young industry that grew up in silo neighborhoods, each with it’s proprietary practices, language and population of like-skilled specialists. Left unchecked, the resulting “framework culture” contributed to IT’s cultural debt and is partially the motivation behind DevOps.

The goal of DevOps is not to undervalue or replace the frameworks that are in place. On it’s own, DevOps is not a framework – in fact, successful DevOps relies on combined effect of best practices such as Agile, Lean and ITSM. By recognizing and adapting the best of each, IT can formulate a potent recipe for enhancing performance and increasing customer satisfaction.

So let’s belly up to the bar and mix ourselves a DevOps Culture Cocktail by taking the best guidance from the top shelf of each of the frameworks and practices.

Ingredient 1: ‘Git R Done’ Scrum

Scrum is the most prominent of the Agile frameworks and with good reason: it focuses on getting work “done” in manageable increments while reducing work in progress.

Pair it with a Kanban Board

While deceptively simple, Kanban is a powerful method for visualizing workflow, identifying constraints and keeping a team focused.

Ingredient 2: Automation

There is a lot of debate around the balance of automation use in DevOps, but there is no dispute that automation and metrics play a key role in successful DevOps.

Ingredient 3: IT Service Management

ITSM is the deployment’s “ever after”. Agile and repeatable service management processes lead the way to stable continuous delivery and increased flow.

Ingredient 4: Lean

Let’s make it a skinny by creating more value for customers with fewer resources and less waste.

Ingredient 5: People

The most important ingredient. DevOps relies on the way people think, behave, interact and trust their colleagues. According to Lloyd Taylor, “You can’t directly change culture. But you can change behavior, and behavior becomes culture.”

Finally, add a splash of common vocabulary and a shot of the alcohol of your choice and you now have a DevOps Culture Cocktail! Serve it daily, share it with your neighbors or sip it while reading a good book like The Phoenix Project. It does get more potent with age.

I recently had the opportunity to present the DevOps Culture Cocktail Party at Fruition Partner’s FruDevCon Conference. One of the attendees, a hobby mixologist, formulated her vision for a signature DevOps Culture Cocktail during my presentation. We asked the hotel bartender to mix it up, passed it around and a new cocktail was born. Here goes:

The DevOps Culture Cocktail
1 part pear vodka
1 part St. Germain
1 part Chardonnay
2 parts simple syrup or 7 up

Top with a spear of pineapple and a cherry.

Improvement recommendations are of course welcome. Could this become a staple at all future DevOps events?

October 9, 2014
The DevOps Diet (Getting Lean)
IT has gotten fat – fat silos, fat processes, fat procedures. Adopting DevOps is a great opportunity to look at some of our unhealthy habits and identify where we can eliminate unproductive waste from our process diets. DevOps will ultimately teach IT how to exercise more with less effort, trim the fat and get lean.

According to lean principles, the seven main areas of waste are:
- Defects – variations from requirements that result in interruptions and re-work
- Overproduction – delivering something more or before it is required
- Inventory – carrying excess raw materials, work in progress (WIP) or finished goods
- Over-processing – doing more work than is required
- Motion – moving people or equipment more than is required
- Transportation – moving products from one location to another
- Waiting – doing nothing or moving slowly while waiting on a previous step
What are the greatest sources of waste in your IT organization? Are defects being regularly passed downstream? Are ITSM processes overproducing through complexity and bureaucracy? Are Agile teams dealing with excessive work in progress and less finished product? Are unresolved bottlenecks resulting in frequent delays while waiting for something to get done or someone to be available? Could automation reduce “motion sickness” and make transportation nonstop? These questions will hopefully facilitate dialog between Dev and Ops about analyzing and streamlining existing workflows.

While waste is fattening, it is not necessarily ineffective. Projects and other tasks are still getting done and therefore some areas of waste may be difficult to recognize. To become leaner, start simple by identifying and eliminating one unhealthy habit from your IT diet. Engage stakeholders from Dev, Ops, customers and suppliers (if appropriate). Discuss ways to improve the work with healthier habits. Agree on what to do next or what not to do anymore.

Every diet should be supported by recipes for success. The upcoming DevOps Cookbook by Gene Kim, et al. will provide more tangible guidance and recipes for healthy IT habits. More on that in future blogs.

Most of all, identifying and eliminating waste through Lean DevOps is a great opportunity for collaboration. After all, it’s always better to diet with a friend.
August 11, 2014
Lean versus the bandwagon, lean wins

I’ve just finished reading “The Phoenix Project,” by Kim, Behr and Spafford. To be honest, it was on my reading list but had I not picked up a copy at Camp DevOps I don’t know that I would have ever read the book. My main issue with books of this ilk, such as “Who Moved My Cheese” and “The Energy Bus,” is that the story is often contrived for purposes of delivering the learning points. So, I was surprised by how much I was able to relate to the issues in Phoenix giving it an air of reality I was not expecting.

Phoenix follows the life of a new VP of IT Operations through his early weeks on the job. Plucked from obscurity as the Director of Mid-range Computing after his management chain was seemingly asked to depart the company, Bill’s eyes are opened to a world of heroics and chaos that surrounds him in IT and has had detrimental impact on the company’s financial and competitive performance. For anyone who has logically questioned, “how can that happen?” in their own companies, the authors do a great job of illustrating the paths that lead to the unimaginable outcomes we see every day in our own IT world.

That said (spoiler coming), I ask the authors, did we really need to make Bill’s dad an alcoholic? Sorry, completely tangential point, but interestingly, one of the few times where I thought the authors had reached into their “drama box” for reasons that are still unclear. Nonetheless, after reading this book, I reflected upon the content that is being created around DevOps movements and realize that there is a polarization effect occurring.

Camp 1 I categorize as the “lean camp”. This camp represents those that believe DevOps should be the application of lean manufacturing principles on IT. In this perspective IT is represented as a critical hub through which business is enabled and transacted. In accordance with this perspective, work enters the hub through multiple endpoints and must be prioritized and coordinated in order to ensure that a) critical IT resources are sourced appropriately and with maximum value to the business and b) constraints and bottlenecks are minimized, thus allowing work-in-progress to complete successfully. Anyone who has studied any aspect of logistics, manufacturing or supply-chain management understands the number of variables involved in this type of scheduling, but applying this to IT is significantly more challenging since it requires the cooperation at both the 30,000 foot view and the 10 foot view, with the latter required to understand the impact of scheduling conflict and help foster collision avoidance that could lead to outages.

Camp 2 I categorize as the “bandwagon camp”. This camp represents individuals that are using DevOps as a lever for their careers. Certainly, DevOps is the hot topic of the day and having it on the resume is certainly good for a few extra brownie points and higher salaries. Ask these individuals what DevOps is about and you hear answers, such as, “it’s about changing the culture in IT,” “it’s about getting development and operations to cooperate more effectively,” or my personal favorite, “it’s about automation.” The truth is that DevOps needs aspects of all of these factors but driven under an umbrella of work described in the Camp 1 description above. Acted on independently under the guise of DevOps and we will see DevOps nose dive into Gartner’s trough of disillusionment very quickly.

Will things get fixed in Camp 2? Probably. Certainly implementing controls around who can make changes and when they can occur may help many businesses limit the unplanned work that is crippling their ability to address other work efforts. However, since this is not the focus of the DevOps effort, unplanned work will continue to come from other areas, it’s just the nature of the beast and without correcting the actions that foster unplanned work, it will continually have a negative impact on the business.

The authors of Phoenix have done a fantastic job of providing IT leaders with a goal for their DevOps programs. If they are wise, they will rapidly adopt the principles defined therein and make sure that all other so-called DevOps efforts in the company are aligned for purposes of reigning in the chaos and free-wheeling efforts of rogue IT employees. I recently met with a VP of IT for one of the major food distribution companies. I was awed by how effectively his IT organization operates, but now I realize, that his company is primarily a logistics and supply-chain company that happens to move food. It only makes sense that the principles they use to run their business would be adopted for IT as well; especially with their IT leaders coming from other parts of the business. In combination with Phoenix, the meeting locked it for me that the lean principles are key to positioning IT as the business enabler needed to be competitive in the current era.

June 2, 2014
DevOps Contrarian

I admire and appreciate the ideas of DevOps as a result of living through two decades of working in IT and experiencing the pain of what I would call the “failed state of IT”. The failed state of IT being something inherently broken, something that couldn’t survive on its own, something that eats its budget and provides no value and something where the people working in such a system don’t even see the worth of the system and stop challenging it all together. In order to correct or prevent these “failed states” I often apply my experiences learned through DevOps and Lean processes and values.

DevOps to me has been about recognizing and pulling yourself out of this “failed state” concept of IT and doing so with a little bit of empathy, a little bit of common sense and a lot of science. I don’t necessarily mean pure science in trying to come up with an underlying law that describes and predicts your system of work, but the foundations of science – The ability to question everything, the ability to collect data, analyze data, measure data and develop analysis thereof. By thinking “DevOps Science” you choose to develop models that describe your work, define your flow, show the value of your system and tailor it to your business needs and to research the best models to achieve your end goal and strive to continually improve those models. DevOps is about putting this science to work, and doing so with collected intelligence & experimentation behind it.

Where I find myself being a DevOps contrarian of sorts is in the often over simplified representation of DevOps as an applied model. I don’t have any issue whatsoever with the concepts described below by themselves; they create amazing discussions and speak to very core foundations of DevOps, but they’re often only spoken of superficially.

DevOps is not just about Culture.

I often see people speak of DevOps as an applied “Cultural Invention”, an innovation found to be useful to a group of people in their behavior. While that is a great description from a 30 foot view as if you’re looking from the outside, it really doesn’t help one understand culture in any sense to help foster it. DevOps culture isn’t about copying what others do and hoping for success, it’s about developing it within realizing that its affected by both forces encouraging change and forces resisting change and finding the balance there of is that is important. You can’t simply force DevOps on an organization and call that culture. The real power of DevOps as a cultural invention is being able to develop DevOps values that directly apply to your organization so that it can be passed down generation to generation and survive on its own. Thinking scientifically, we learn from these shared examples of cultural invention and their successes by experimenting with them to see how they fit our organization as there is no universal law of culture but merely models we can research and identify with. It’s great to build upon the success for others, but the only way to validate those successes is to test them yourself!

Another danger of thinking applied culture is the development of cultural optimums, I parallel this to what Garret Hardin coined “The Tragedy of the commons”, whereby individuals, acting independently and rationally according to each one’s self-interest, behave contrary to the whole group’s long-term best interests by depleting some common resource. I won’t claim to define what your resource is that you’re depleting, but it’s something to keep in your mind as you think of culture because it’s important to understand culture external of yourself as much as it is within. The value of culture isn’t in how you rationalize it for your own goals, but how it operates on its own and if that culture is appropriate for the good of the entire system (business). The strongest metaphor in DevOps for depleting the wrong resource is wasted resources not working towards your common goal. Are you actively engaged on the right tasks? Right automation? Right flows? Are you merely creating more work or are you adding value? DevOps isn’t something you apply methodologically, its something you experiment with methodologically.

DevOps is not just about automation.

Automation is the process for operating with minimal or reduced human intervention. If you’re focused on automating processes are you still improving that process or did you think to yourself “I automated that, now I’m free to work on other stuff”? Automation can tempt you into doing “more work” if not kept under control and that creates a real danger of losing focus, having too much work in progress and actually hurting your overall performance.

Often times the best value in the systems we call “Systems Automation“ – the Chef, CFEngine, Puppet, Ansible & Saltstack systems is not necessarily their amazing capability to automate processes but their ability to document processes. Those cookbooks, manifests, scripts and DSL’s you’re using are absolutely the best documentation of your system you can have. They provide a great resource when done in such a way to easily describe the processes that they’re automating so that they’re well enough documented for improving them. You can’t automate that which you don’t understand and the best way to understand them is to make sure the processes that describe them are easily readable, documented, tested, and validated. They should be part of your experiment, not just testing for failure but testing for improvement.

Through the lens of science, we can look at automation more as standardizing on a process and applying the best resource to that process. Sometimes it is compute power instead of human power but don’t fall into the trap that the process is said and done (Automated all the things!) and time to move on! Science is a never ending process by where you build upon your conclusions!

One conclusion I and many others have come across is that people are the most flexible resource you have! It may be a better value to your business to have the flexibility of a human resource if the process you’re trying to build requires flexibility. Even when planning for automation, requiring human intervention is a perfectly valid result and a justification you can sell to your business that has been proven through experimentation & data. Don’t ride the automation bandwagon when your data shows otherwise!

DevOps is not just about measuring.

What are you doing with the data you measure? What value does measuring give your organization? When you have the flu and you take your own temperature, what are you measuring for? Do you have the cultural aptitude in your organization to accept the ramifications of what you measured? Can your organization stop working when it’s “sick”, take the time to heal and start back up after taking corrective or remediation actions?

Measuring is extremely important, but measuring isn’t what DevOps is. Measuring is a part of what makes your organization responsive, culturally adept, and accountable for itself. It’s what allows you to convey value, to compare itself against and strive for improvement. If you’re not measuring, what are you improving against? Inversely, If all you’re doing is measuring, what is the value of your measurements? What are they the basis of? I like to think of biological systems when I start thinking of computing systems where there is a cost of energy to do something. Are you utilizing that cost the best way you can by measuring what you are or should you evolve the system to spend that energy elsewhere? Are you collecting the right metrics to even comprehend your systems? Do your metrics allow for capturing exaptation and evolution thereof? When you experiment, measure, rinse & repeat you get patterns; measuring helps identify those patterns. Creativity, experimentation and the process of science allow you to act upon those patterns and create value from those patterns. Some people call that innovation.

DevOps is not just about sharing.

There are people who believe that unless you are open source, have a GitHub repo and publish your code, you’re not really sharing and you’re not DevOps. I certainly hope we’re not going down this path and certainly hope we can appeal to the broader ideas of sharing. Sharing is about the joint use of a resource and sharing is knowledge charity. Sharing goes hand in hand with culture in not just expressing your needs and sharing your knowledge, but having the ability to listen, feel empathy and accept what others share as well. When it comes to collaboration & sharing it’s much easier to accomplish if everyone sees it through the lens of science, not only do you speak to DevOps values more rationally but you can even accept opposing views and ideas much easier because you have evidence to them and aren’t emotionally tied to their success, you’re tied the process that challenges their success as a way to improve them!

DevOps through the lens of Science.

The beauty of DevOps through the lens of science is that science is about challenging ideas, accepting failure, learning from history/prior experiments and having the strength to not only say those successes and failures are a valid output of your experiments but also come up with a new experiment or new hypotheses and do the process all over again. This helps define your culture, this helps setup what you should automate, this helps you validate what to measure and this develops what you should share. Through the lens of science you learn through experimentation, testing & verification what works for you, what creates value, what brings you closer to your goal. When honestly done you also learn what doesn’t work for you, where you failed, and how you can use those lessons in failure to continuously improve. A failed hypothesis after all is still good data!

Why so much focus on the sciences? Personally, I enjoy astrophysics not because there is direct monetary value in understanding the cosmos around us but because of the philosophical nature of being, the beauty of knowing and the questions it gets you asking as the more you know, the more you wonder. That wonder, is what makes DevOps so great to me. That wonder is what drives continuous improvement. That wonder is what drives the “what next…”. That wonder is what pushes “question everything”. That wonder is always what pushes me to accept and understand the science we do know and trust it because it’s based upon the values that are respected by thousands of scientists all over the world through peer review and consensus. That foundation is not the assumption that we think we know it all, but that we know enough to describe something and we trust that description good is enough that we challenge you to break it. I would highly recommend that any DevOps practitioner cross pollinate their technology passion with the sciences as there is nothing more exciting than realizing commonalities of biology, physics, astrophysics and the laws of nature within your own thoughts and patterns as they’re applied to DevOps.

So be a DevOps contrarian; for its not accepting DevOps as the answer, it’s the challenge there of that will make it aspire to bigger and better things. It’s not that Culture, Automation, Measuring and Sharing is wrong by any means, but they’re just scratching the surface of what you need to understand to make DevOps “Work” for your organization.

I’m not only a DevOps contrarian, but I’m an OS contrarian, a Developer contrarian, a Systems Contrarian. I’ve made the best of running Windows, Oracle Linux, Oracle Database, eBusiness Suite, Weblogic and much more. Not because I’ve personally identified or I am idealistically aligned with any of these tools & systems, but because they solve the needs of the business; they create value, they operate and perform admirably and we have proven their worth. It’s no utopian system by any stretch of the imagination, but I find great reward in the continuous experimentation of making these systems work and improving upon them in a very DevOps way.

After practicing DevOps & Lean principals and having over two decades of IT experience, I’ve realized the “failed states of IT” and people who thrive in them are people who stopped critical thinking, stopped challenging the status quo and stopped experimenting. They stopped having a contrarian.

March 25, 2014