Combining data catalogs with data observability tools creates a holistic approach to managing data assets. While catalogs help users discover and understand data, observability tools ensure that data is accurate, timely, and reliable. This integration supports better decision-making, improves data reliability, and strengthens overall data governance.

We’ve introduced Augmented Resolution to help teams group related alerts into a single collaborative ticket, streamlining incident response. Plus, with integrations into your ticketing systems, Sifflet ensures that data issues are tracked, communicated, and resolved efficiently. It’s all part of our mission to boost data reliability and support your operational intelligence.

Sifflet is built to scale. It supports automated data quality monitoring across hundreds of assets, as seen with Carrefour Links monitoring over 800 data assets in 8+ countries. With dynamic thresholding, schema change detection, and real-time metrics, Sifflet ensures SLA compliance and consistent data reliability across complex ecosystems.

The very first step is to set clear objectives that align with your company’s level of data maturity and business needs. This means involving stakeholders from different departments and deciding whether your focus is on exploratory analysis, business intelligence, or innovation through AI and ML. These goals will guide your choices in data stack, platform, and hiring.

To prevent disruptions, it's essential to use data lineage tracking. This gives you visibility into how data flows through your systems, so you can assess downstream impacts before making changes. It’s a key part of data pipeline monitoring and helps maintain trust in your analytics.

At Sifflet, we believe data quality isn't one-size-fits-all. Our observability platform blends technical robustness with business context, offering customized data quality monitoring that adapts to your specific use cases. This means you get both reliable pipelines and meaningful metrics that align with your business goals.

Modern data catalogs capture four key types of metadata: technical (schemas, formats), business (definitions, KPIs), operational (usage patterns, SLA compliance), and governance (access controls, data classifications). These layers work together to support data quality monitoring and transparency in data pipelines.

If you're in a regulated industry or handling sensitive data, observability tools can help you stay compliant. They offer features like audit logging, data freshness checks, and schema validation, which support strong data governance and help ensure SLA compliance.