In the world of distributed systems, log data is everywhere—yet most of it goes unused. That’s a problem. As complexity explodes, leaving unstructured logs on the table means losing the very context that could help prevent outages, cut downtime, and strengthen security. Bill Peterson, Senior Director of Security and Observability at Sumo Logic, joined me on Data Driven to unpack why this is happening and how enterprises can rethink their approach.

Why Scale Makes Log Analysis Hard

At the heart of the issue, Peterson explained, is scale. “The volume and complexity of the amount of logs, metrics, and traces… up to terabytes a day for a large application, makes it really difficult to know where to start.”

Traditional tools can’t cope with inconsistent formats and lack of standardization. Add to that organizational silos, and teams struggle to act on the full picture.

But logs remain the backbone of modern SRE strategies because they deliver context. Metrics and traces may show that CPU spiked, but only logs explain why and how. This context enables real-time incident investigations and behavioral pattern detection—essential when more data and more people pile into incidents, but time never increases.

From Reactive to Predictive: The Next Phase of SRE

The shift underway is from reactive to predictive. Peterson described how detection-as-code and AI-driven anomaly detection are transforming incident response. By embedding detection rules directly into CI/CD pipelines, engineering teams can treat incident management as code. Layer in AI to sift through anomalies, and teams are cutting false positives by 50–60% and aiming for MTTR zero.

The evolution of log analysis mirrors this shift. Peterson noted it began with finger-pointing over “what happened,” matured into investigating “why it happened,” and is now advancing toward “what will happen.” Automation and playbooks are the entry point, with AI enabling predictive insights like: “You’re about to lose 1,000 servers in seven minutes if you don’t act.”

This transformation also reshapes culture. Peterson emphasized that observability is now a shared responsibility. Practices like chaos engineering force DevOps and SecOps teams together, while unified data architectures break down lingering silos. AI, far from replacing engineers, removes the “struggle work,” letting humans focus on problems that require judgment.

Logs, especially unstructured logs, remain the ultimate source of truth. Structured data fits neatly in a table, but those messy error dumps and cryptic hex codes often hold the clues that matter most. Without analyzing them, teams may only see 30% of their system’s reality—unacceptable during peak events like Black Friday surges in retail.

Sumo Logic’s approach is to unify structured and unstructured data under one roof, ensuring organizations don’t have to choose. As Peterson put it, “If you see something happen on one server, wouldn’t it be nice to know if it’s today’s problem or next month’s problem? Logs can help lead you to that answer.”

For SRE and DevOps leaders, the call to action is clear: step back and ask if you’re doing justice to your data. If the answer is no, it’s time to rethink your strategy. The good news, Peterson reminded us, is that “good ideas get funded.” Making the case for unified log analysis isn’t just a technical win—it’s a business one.