How Observability and AI Helps DevOps Practitioners Show Business-Impacting Value
February 22, 2021

Adam Frank
Moogsoft

DevOps practitioners have often operated in the shadows, appearing only to extinguish fires when something breaks. And in a move-fast-and-break-things world, that worked for the most part. But software companies are becoming more product-led — they want to guide customers to some form of valuable outcome. That's opened the door to an incremental approach toward business-impacting value.


While DevOps practitioners are becoming more important to the value any company provides its customers, they still struggle to define the value they bring to internal stakeholders. IT teams speak their own language, which doesn't translate easily into the lexicon of business impact. And as the incremental approach moves into the software industry, the days of measuring whether a project delivered in the end are nearly over. Measuring value along the way is now where companies succeed.

But IT teams have an opportunity to show their value, and it lies in the customer experience. The experience is what leads customers to a desired valuable outcome. In an increasingly digital economy, IT teams are now evermore responsible for creating a reliable, always-on customer experience. It's a business area rife with chances to improve, collaborate and innovate.

With the right solutions, teams can move themselves out of the shadows of error resolution and into the light of innovation. Observability data, drawn from their systems and imbued with context from AI, lets teams automate the issues holding them back. Contextualized data and insights also give them the language to speak to the incremental, product-led approach and the direction to drive key innovations in customer experience improvement. Communicating value becomes a much easier proposition for DevOps practitioners — and they can take their seat at the company table as contributors to value.

Driving Excellent Customer Experiences

Any company that thinks it's not a tech company is already behind the curve. Every customer experience is now delivered digitally, and zero downtime has become a customer expectation. While DevOps practitioners have worked hard to keep up with constant change and zero downtime, there's more they can offer. Their future contributions involve leading change and improving experiences by learning from problems before they create any downtime. That's what will set tomorrow's customer experiences apart — and lead to more customers and business revenue.

Before leading change, however, IT teams need to automate the work holding them at bay. Technology can help. For example, monitoring solutions automate much of the work catching anomalous activity that prevents systems from achieving zero downtime. These solutions check metrics against thresholds and send alerts to teams when anomalies occur. Observability into the system through log events, distributed tracing (traces) and metrics produces alerts that help identify issues before or as they happen.

But automated alerts without context can do more harm than good. Teams will spend too much time hunting for a needle in a haystack of data instead of resolving the issue's root cause and moving on to subsequent iterations and improvement. They need context to capture automation's real benefits.

AI incorporated into system monitoring can make sense of masses of observability data in a fraction of the time the human brain requires to do so and produce insights enabling much-needed context for errors and alerts. It eliminates the haystack, and teams can resolve incidents faster — and before they impact the customer. The customer experience is improved overall, customers want to join and stay, and DevOps practitioners help retain and generate revenue, which all influence a team's value to the business.

Maximizing innovation within IT Teams

DevOps practitioners can bring a lot to the table through innovation — plenty of value-driven tasks and projects await their attention. But they often get caught in a cycle of fixing the same problems every day. This work, called "toil," drains a team's innovative energy. Removing operational toil with automation lets them recommit their cognitive loads to more interesting and value-added projects.

Teams can use AI-driven observability to automate many of the mundane tasks that contribute to toil. It rescues them from waves of Slack messages about issues and moves them right to the root cause. Teams on their own can pinpoint where time is being wasted and more quickly code themselves out of toil.

With more time in hand, DevOps practitioners can then use AI as a resource to innovate instead of repair. Teams can drive more value for customers at their own pace, practicing rapid experimental iterations with AI assisting at machine speed. They can contribute more to the company's goal of continuously assuring a top-tier customer experience. Instead of dealing with blame for problems, IT leads customer experience improvements and does so more visibly within the organization. That means happier IT teams and better customer experiences.

Remaining Agile and Innovating Faster

Freed from toil, IT teams can develop new and improved features, with their error budgets, SLOs and SLAs providing guardrails for reliability. Automating mundane tasks means they no longer soak up teams' budgets and attention. They can be more confident in actually having the budget space necessary to chase innovative opportunities. IT can then move faster on improving reliability and availability beyond fixing errors.

It also leaves time for teams to listen to and implement customer feedback directly on the product. Being agile enough to do this keeps the company ahead of the curve — and competitive in the marketplace. IT becomes a clear center of innovation, developing better, more competitive features to improve the customer experience and demonstrating their business value to company leadership.

By improving the customer experience, increasing collaboration and maximizing innovation, DevOps practitioners can prove their value to the business using the right data and context. They can communicate through the language of customer experience improvements, and better demonstrate their critical business value. Intelligent observability, supported by AI-led automation, empowers DevOps practitioners to emerge from the shadows and take their rightful seats at the table.

Adam Frank is VP of Product and Design at Moogsoft
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