Ideally, data quality monitoring should begin as early as possible in your data journey. As Dan Power shared during Entropy, fixing issues at the source is far more efficient than tracking down errors later. Early adoption of observability tools helps you proactively catch problems, reduce manual fixes, and improve overall data reliability from day one.

Sifflet focuses on business-context aware observability. Instead of just tracking technical metrics like schema changes or row counts, it connects data health to business impact. This helps teams understand not only what broke, but why it matters and who needs to be informed, making observability tools more actionable and aligned with business goals.

In 2025, expect to see more focus on AI-driven anomaly detection, dynamic thresholding, and predictive analytics monitoring. Staying ahead means experimenting with new observability tools, engaging with peers, and continuously aligning your data strategy with evolving business needs.

With data stacks growing rapidly and teams staying the same size or shrinking, proactive monitoring is more important than ever. These AI agents bring memory, reasoning, and automation into the observability platform, helping teams scale their efforts with confidence and clarity.

We're excited because MCP allows us to build intelligent, context-aware agents that go beyond alerts. With MCP, our observability tools can now support real-time metrics analysis, dynamic thresholding, and even automated remediation. It’s a huge step forward in delivering reliable and scalable data observability.

When a data issue arises, Sifflet gives you the context you need to act fast. Our observability platform connects the dots across your data stack—tracking lineage, surfacing schema changes, and highlighting impacted assets. That makes root cause analysis much easier, whether you're dealing with ingestion latency or a failed transformation job. Plus, our AI helps explain anomalies in plain language.

Unlike Datadog, which focuses on infrastructure and application telemetry, and IBM Databand, which specializes in pipeline health, Sifflet offers true end-to-end data observability. It combines data quality monitoring, data lineage tracking, and anomaly detection into one platform, all powered by AI agents designed to reduce manual effort and boost trust in your data.