Several practices of webscale companies are now penetrating mainstream enterprise organizations. The practice of DevOps is perhaps one of the most important. Driven by the adoption of cloud and modernization of application architectures, DevOps practices are quickly gaining ground in companies that are interested in moving fast – with software eating everything - between “write code and throw it across the wall” to creating more pragmatic mechanisms that induce and maintain operational rigor. The intent behind DevOps (and DevSecOps) is quite noble and excellent in theory.
Where it breaks down is in practice. Greenfield deployments remain innocent. Starting out with a clean slate is always relatively easy. Preserving or integrating legacy in brownfield environments is where it becomes both challenging and interesting. For the next several years that’s where the action is.
Enterprises that have invested in technology over the past few decades suddenly find that they can now actually create tremendous legacy inertia to move forward. So, while many have adopted DevOps practices, it has begun in pockets across the organization.
Being focused on the area of Cloud and DevOps Automation, over the last two years Quali has conducted an annual survey that captures the trends at a high level from different vantage points.
Our 2016 Annual DevOps survey yielded 2045 responses to our questions and brought us several insights. Many of these are consistent with our customers’ experiences during the course of our business. Other insights continue to surprise us.
The results of our survey are published in this press release.
It is remarkable that many enterprises continue to be dependent on infrastructure to make applications move faster. Infrastructure continues to be a bottleneck, particularly in on-premise environments. Software defined architectures and NFV have taken root, but the solutions are still scratching the surface. Adoption of automation, blueprinting and environments-as-a-service are happening and greasing the skids, but clearly these need to happen at a faster pace.
The survey also demonstrated some clear patterns on the top barriers inhibiting the rapid adoption of DevOps practices. The rankings were published in this infographic:
Organizations that are planning to accelerate their DevOps initiatives in 2017 should heed these barriers and set up a clear plan to overcome them.
So, how do you grease the skids for DevOps? We’re sharing some of these insights and more in an upcoming webinar on March 22nd that will discuss these barriers in a greater amount of detail. You can register for the webinar here.
Finally, our 2017 DevOps and Cloud Survey is underway. Please consider answering the survey; if you do you may win a $100 Amazon gift card.
In 2016, I had the privilege of consulting with about a dozen major financial services organizations from Toronto to Charlotte, and it became evident that there is a strong need for these highly regulated businesses to adopt an increasing amount of new technology.
Security is the number one driver of new technology adoption (I know, a real shock, right?). Not at all coincidental given my specific line of work, I’ve found that certification labs are under mounting pressure to move work through their organizations more quickly all the time.
When considering the value chain of new technology adoption, there are many individual segments ranging from, “We’re going to look at this new technology” to “We’re ready to apply this new technology to our production network.” Although the certification lab is only one such segment, there are varying degrees of bottlenecks from beginning to end and I gather that the lab’s build-up of work in progress is the most painful and critical issue to solve.
The major problems that I’m hearing from my customers are:
If solved, all of these areas could have a tremendous impact to increase speed and agility in the lab. The real crux of the situation for most labs involves budget constraints. Traditionally, the certification lab is a cost center tasked to perform a whole lot of work with limited resources. Very simply, cash is hard to come by. When dollars do become available, justifying a major investment in a transformative solution is more difficult for what is considered a cost center in the mind of the business. That’s why so many labs revert to the tired (yes, that’s not a typo—I said tired) and true solution of building a new rack or two to accommodate demand.
For the managers, directors and VPs that are in agreement that the more equals more approach is tired and no longer true, the willingness of the enterprise to introduce innovative solutions into the lab becomes easy, and dollars are freed up. And the good news for these organizations is that the solutions to all of their problems are fairly mature and have been vetted by the market. All that is required is money, energy and time to evaluate and adopt the best solution for their specific requirements. Significant ROI can be realized in as little as six to 12 months.
What I am advising organizations to do when requesting a budget increase is to carefully examine what short-term wins will grab the attention of executives and help free up dollars for lab innovation. In other words, solve one problem with a manageable investment in order to justify (or even fund) solving the next or all of the problems they face for increasing work requirements and demands from the business. In this way, they can chip away at major innovation incrementally, one quarter or budget cycle at a time.
So if you’re eager to innovate in your lab, and budget and resource constraints are holding you back, consider solving your problems incrementally. And, if you want to solve them all with a single solution, they are out there (and you might not have to look too much farther than this blog post in your search).
When consulting, I guide my customers toward the solutions that make the most sense given their unique situations. The most dynamic approach to create a short win and freeing up budget for the future is providing business intelligence and visibility where there is currently limited to zero visibility into lab equipment utilization, or individual testing work metrics (problem statement #4 above).
If you can baseline performance with reliable and easy-to-consume metrics, and prove in the light of day that more equipment to meet demand is not the answer, you’ve just opened to door to justifying a major round of funding.