Turn data into knowledge to accelerate innovation


Blackberry, Nokia, Blockbuster and Kodak: these are just some of the once-successful companies that failed because they failed to innovate. In our digital world, the difference between sinking and swimming often rests on a business’s ability to provide continuous digital innovations to its customers.

But consumer demands for new technologies and experiences also come with a stipulation: the availability of digital instant gratification must remain uninterrupted. After all, our digital economy doesn’t have normal business hours. It’s always running, and consumers expect to digitally access, interact and purchase at their convenience, at any hour of the day. Digital apps and services must always remain on. And facing an outage or poor system performance, consumers will just switch to another company that provides a better digital experience.

Expectations for innovation and service assurance are at odds with each other. The flow of new components into a company’s production environment generates perpetual changes. And most incidents and outages are sparked by such changing conditions.

How can DevOps practitioners and SRE teams accelerate innovation and minimize system disruptions?

Observability stokes innovation and uptime

Accelerating innovation and minimizing disruptions requires the constant vigilance of a company’s IT stack. But even the most seasoned DevOps practitioners and robust SRE teams can’t manually monitor the mountains of data that today’s IT systems produce. And, although human operators built our modern distributed architectures, they can’t sufficiently monitor such complicated and interconnected systems without advanced tools.

As our distributed systems grow more complicated and interconnected, the number of potential failures in that system increases, as do the number of warning signals and alerts. Humans can’t scale at the same rate as the telemetry coming from these systems, making manual monitoring impossible.

The teams charged with keeping critical systems up and running need automated monitoring tools. Enter AI-driven observability. Intelligent observability continually watches over an ever changing stack, collecting telemetry data across applications, services and infrastructures and analyzing it to understand system performance, application health, customer experiences and more.

Once there’s a system baseline, the intelligent observability platform uses data insights to create a learning cycle and increase system reliability. When a potentially disruptive incident rears its ugly head, the automated platform detects the problem and notifies the team. This quick detection allows DevOps and SRE teams to immediately respond to and fix the problem — sometimes before it impacts the business.

But the benefits don’t stop there. By automating the usual toil and reducing the fear of breaking things, intelligent observability platforms allow teams to move faster and innovate more.

Knowledge is power

Even the most advanced intelligent observability tools can’t fully safeguard systems against incidents and outages. Consequently, DevOps and SRE teams need knowledge when confronted with a difficult system outage. Knowledge provides a path forward, allowing teams to stay cool under mounting pressure. And the pressure to fix incidents is significant considering that a system outage can diminish a company’s reputation, embitter its customers and decrease its revenue.

With the clock ticking, DevOps practitioners and SREs can look to intelligent observability to arm them with knowledge and move their teams from emotion-led decisions to data-driven solutions. Applying AI to observability data adds context to this information and transforms vast amounts of data into actionable insights. It detects anomalies, changes and events and analyzes this data for correlation and causality.

While most modern businesses now see ceaseless digital innovation as a business imperative, many still haven’t unlocked the key to accelerating this innovation. So here’s the secret: eliminate manual monitoring in favor of intelligent observability tools. Only AI-driven observability can help teams decipher heaps of data and reduce toil to increase productivity and push innovation — all while maintaining maximum system uptime and superior performance.

Image Credit: Sergey Nivens / Shutterstock

Chris Boyd is an experienced engineering leader, observability fanatic and loves to challenge the status quo. Driven by improving the lives of fellow technologists when working with Observability products, he takes pride in the teams he builds and the innovative solutions they develop together. You may know him from his work as the Direction of Site Reliability Engineering at GoDaddy from their early days to their successful IPO launch. He currently resides in Mesa, AZ, and is VP of Engineering for Moogsoft, a leader in AI and Service Assurance.

Author: Martha Meyer