DevOps started as part of the agile movement and making great strides since then. As we take stock of the progress from the time it started, we see organizations adopting it at a rapid pace. But companies are able to realize only a fraction of DevOps potential and benefits. Adapting to an era of always connected and Data-driven apps, organizations have to build solid muscles around executing Data-driven DevOps for multiplying DevOps benefits. More honestly, software deliveries are no longer guess estimates or instincts; they need a bolster from real-time data from machines, customers, tests and IT operations.
In our experience, with most of the organizations there’s a lack of understanding between traditional reporting and advanced analytics that involves Machine Learning. One of the leading financial advisory firm did heavy investments in the DevOps program, when we did a thorough examination, most of their processes were below average. Client sprints did not match the capacity of the teams and teams were not operating on the defined acceptance criteria basis. They regularly mentioned using DevOps tools, but when asked data, they presented manually extracted reports. The question is, how deeply your data is driving DevOps, and not DevOps experts’ opinions? In this article, we argue the next-phase in DevOps is real Data and not tools and technologies and no more opinions.
Build a framework to collect data for DevOps
All good in theory, but how? Moving from waterfall to agile and then introducing DevOps was itself not a simple proposition for organizations, now enabling Data in every software decision requires major changes towards organizational mindset–from room full of opinions to data-advocated opinions and IT infrastructure to capture data in real-time.
Even in DevOps Data Beats Opinions
Most of you remember chasing release timelines of features that’s not even get used by customers. Or for another instance, changing the environments without understanding the data flow and running into compliance issues. These were normal scenarios where people backed their decisions with opinions or human memories and ran into problems.
The profound shift in the development practices and customer demands need re-examination of DevOps practices. While the primary goals of agility, speed, efficiency and collaboration remains same, Data has to get intertwined in DevOps decisions. Building or maintaining Data-driven application with DevOps requires greater collaboration between different teams and data from different sources. Development, Operations, Security and Governance, Data Science, Marketing, Customer service, Product Management, and Leadership everyone has to get involved.