Opinions don’t matter, data does – A framework to collect measurable data for DevOps

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.

The three important data categories to mine insights to deliver strategically superior services to customers are–Data from Machines, Data from Customers and Data from Tests. Data from these three areas can bring in quick improvements in the release cycles. Qentelli has worked directly with C-suite to identify which data to capture and how to use them to optimize applications. We have done it without pausing their development efforts, keeping regular deliveries unaffected.

Automate your Delivery Pipeline

It’s no coincidence that organizations are automating more than ever. Organizations have seen how their counterparts are scoring big with Data-driven approach, instead flipping monthly reports or referring to outdated knowledge bases. Automation is essential to support data democratization. Automation creates a continuous stream of data to feed into applications for learning and improving and even produce self-learning and self-healing systems. Radically balanced and future-ready automation initiatives need to integrate with Analytics engine, a core of deriving insights from the infused data.

Consider Infrastructure Managers working on building network infrastructure using manual tools like excel sheets, tedious to manage and prone to human error. The rise in application and network complexity is compounding this challenge. Through automation-assisted “replicated build” which mimics required configuration for developers, testers, pre-production, the same manager can cycle through countless configurations and this can be the base for infusing AI to learn, improve, suggest and predict with every iteration.

Build or Buy – Analytics Engine

Organizations realized data troves created in the DevOps pipeline holds the real worth for the businesses to improve their Delivery Operations. But is it possible for Development or IT or QA teams to understand what they stand for and how they can be mined to derive real business intelligence? Mostly, No! AI can be of help here.

With AI taking concrete shape in businesses, building or buying an Analytics engine for DevOps pipeline gives endless benefits to businesses. The potential use cases of applying Analytics to DevOps data are many, measuring customer engagement after a feature release to real-time visibility of usage, performance, build pass/fail percentage, reliability, measuring errors percentage, etc.

DevOps Dashboard

After applying analytics to the DevOps data, organizations should see results in a comprehensive Dashboard. This dashboard summarizes the view of day-to-day operations and development activities. With up-to-the-minute insights about how applications are performing, and development activities are progressing, it becomes easy to drive delivery decisions with data rather gut feelings or intuitions or human-decided priorities.

For DevOps Dashboard, there are a lot of options mushrooming in the concentrated DevOps tool chain market. Most of them claim to provide full visibility of DevOps data but selecting them requires a targeted approach towards what businesses are expecting from DevOps–be a development philosophy or a strategic differentiator.

Building dashboard can be an intensive exercise and lies outside the expertise area of organizations. It is always advisable to opt for a tool that can be integrated with the existing and diverse DevOps Tool Chain and/or can be customized completely. Qentelli has worked with clients having a plethora of DevOps tools and struggling to get a complete view of multiple projects and teams.

We developed The Engineering Dashboard (TED), our proprietary tool to solve these challenges of distributed tool chain, projects and teams and remove the complexities of aggregating data, stopping organizations from driving their DevOps with data. TED, our Engineering Dashboard is a one-stop unified cognitive dashboard to aggregate, analyse and alert based on data produced within DevOps pipeline.

Reworking on DevOps with Data

Business leaders can look beyond the rear-view mirror of faster deliveries, as DevOps move on from opinions to data. Instead, they can harness the power of Data and Artificial Intelligence to drive application and business decisions based on real-time and predictive insights. They can use Data-driven DevOps to up their game: effortlessly for new feature releases, assuring IT alignment for new initiatives, block security threats and improving application performance. The end results? DevOps is no longer a battle to release faster in the face of growing data complexity. It becomes an opportunity to build Data-driven culture within an organization to benefit customers with more innovation and faster deliveries. Talk to us in more detail about TED and how it can complement your current development tool chain at info@qentelli.com.

Leave a Reply

Your email address will not be published.