Operations | Monitoring | ITSM | DevOps | Cloud

Streaming workloads impose different requirements than classic migration projects. A platform that can support a one-time move from one system to another is not always the right fit when data must flow continuously, stay current, recover cleanly, and serve downstream analytics, applications, or AI use cases without long delays. That is the real shift behind this category. The question is no longer only whether data can be replicated.

Engineering and operations teams have a measurement problem that most other functions don't. The technical metrics are excellent. Deployment frequency is up. MTTR is down. Uptime is at 99.97%. The CI/CD pipeline is running cleanly and the on-call burden has been reduced by 30% since the team adopted a proper incident management process. By every internal measure, the team is performing well. And yet, in the quarterly business review, the conversation keeps returning to the same uncomfortable question: what did engineering actually deliver for the business this quarter?

In today's fast-moving business environment, decisions cannot wait for lengthy reports or scattered spreadsheets. Leaders need clarity, speed, and accuracy to understand what is happening inside their organizations in real time. This is where business intelligence reporting becomes essential. It transforms raw, complex data into structured insights that help teams act with confidence rather than guesswork.

For years, telecom operators treated analytics as a reporting layer. Data went into dashboards, engineers reviewed incidents after the fact, and performance reports helped leadership understand what had already gone wrong. That model is starting to break. Modern telecom infrastructure changes too quickly for delayed analysis to be useful. A latency spike inside a cloud-native core can ripple across services in seconds. A software bug in one region can affect thousands of enterprise users before a traditional monitoring workflow even flags the issue.

Automotive plants now lose up to $2.3 million per hour to unplanned downtime, up roughly 77% since 2019. In our deployments, the model is rarely the problem. The operating model around it usually is.