Articles and opinions about the value of DevOps blanket the Internet, yet many companies are still struggling to make it work. This is an issue not only at the fundamental process level, but also on an organizational level as both delivery teams and system users push back on the idea of change.
Continuous flow, an approach where each release priority, no matter how minor, moves all the way through development and testing before teams start on the next one, is supposed to streamline DevOps and make adoption easier. Unfortunately, while it can result in significant functional improvements, this “foreign” way of doing things can make resistance even greater.
In an earlier blog, I discussed how organizations can mitigate the pain of moving to DevOps and gain buy-in from both end users and Dev/Test teams by equipping them with what they miss most from legacy approaches. You can read it here: Moving to Continuous Flow: Don’t Throw Your Value Out in the Process.
Orasi has also produced a webinar series that addresses these and other priorities that firms should consider on the road to continuous flow actualization. You can access all of them on this page: Orchestrating DevOps: From Customer to Code, A Webinar Series.
Extracting All the Insight in Continuous Flow DevOps
Now, back to the matter at hand. Making system users and Dev/Test teams more comfortable in a new process is an important part of the adoption cycle, but it certainly isn’t the end game. To make DevOps and continuous flow reach the pinnacle of success — where it is a seamless process that repeats itself until all defects are eliminated and feature changes are accomplished — project managers and their teams must have total visibility into what’s going on “under the covers.” That requires robust analytics.
You’ll hear so called “experts” at a lot of firms touting their analytics mastery around metrics common to continuous flow, like Flow Velocity, Flow Distribution, and Flow Load. These and other flow metrics are certainly important for accelerating delivery and realizing the promise of continuous flow. What they don’t explain, up front, is that access to most of these metrics requires you to use their single-tool stack or to dramatically change your existing process. These changes can be expensive and intrusive.
Here’s the good news. There are hundreds of potentially valuable, actionable metrics that can be extracted directly from continuous flow processes using the tools and processes you already use. Unfortunately, most software teams don’t realize they exist — and aren’t taking advantage of what they already have.
Getting to the Goods
How is this possible? Development and testing often require teams to use multiple tools and those tools interact with one another. For example a firm might use Atlassian JIRA and Micro Focus Application Lifecycle Management (ALM). Developers work with JIRA, while testers use ALM. Like all software lifecycle tools, both of these platforms produce metrics of their own.
Here’s where it gets interesting. While the intelligence from each of these tools holds value, if that data can be aggregated, it can lead to a much higher level of insight. For example, full, cross-platform analysis might reveal that problems identified during regression testing are being caused by a specific development team (or developer). It could even reveal significant outliers, like the fact that more regressions occur on Mondays than Fridays. Imagine how powerful that much granularity can be.
The unfortunate truth is that software teams are leaving far more metrics “on the floor” than they are using to glean insight. It’s time to pick them up and make use of them.
If you’re curious to learn more, sign up for the fourth webinar in the series mentioned above, Integrating DevOps Metrics Analysis. Slated for April 14, 2020 at 2pm EST, it offers a free, no obligation opportunity for you to experience for yourself how powerful DevOps analytics can be.
