Absolutely! With Sifflet's Data Sharing, you can connect your favorite BI tools like Looker, Tableau, or Power BI to our shared tables. This lets you build tailored dashboards and reports using real-time metrics from your observability data, helping you track KPIs, monitor SLA compliance, and visualize trends across teams or domains.

While data quality monitoring is essential, many enterprises need broader observability that includes pipeline health, infrastructure performance, and downstream usage. Platforms like Sifflet provide this full-stack visibility, helping teams achieve SLA compliance, streamline incident response, and ensure data reliability throughout the entire lifecycle.

When a data incident occurs, Sifflet’s Sage and Forge tools kick in. Sage consolidates all alerts into a clear incident narrative, while Forge recommends fixes based on past resolutions. This streamlines incident management workflows and helps teams restore data trust quickly and efficiently.

Sifflet centralizes documentation for all your data assets, including DAGs, models, and dashboards. This makes it easier for teams to search, explore dependencies, and maintain strong data governance practices.

By including Fivetran connectors and source assets in the lineage graph, Sifflet gives you full visibility into where data issues originate. This makes it much easier to perform root cause analysis and resolve incidents faster, improving overall data reliability.

Great question! AI doesn't usually fail because of bad models, but because of unreliable data. Without strong data observability in place, it's hard to detect data issues like schema changes, stale tables, or broken pipelines. These problems undermine trust, and without trust in your data, even the best models can't deliver value.

Sifflet uses intelligent alerting strategies like business context-aware anomaly detection and lineage-based impact scoring. That means we prioritize alerts based on the criticality of the data asset involved. We also group related issues into a single incident, so your team isn’t overwhelmed with noise. This approach helps reduce alert fatigue and ensures your team focuses on what really matters.

Sifflet is designed for both technical and non-technical users, offering no-code monitors, natural-language setup, and cross-persona alerts. This means analysts, data scientists, and executives can all engage with data quality monitoring without needing engineering support, making it a truly inclusive observability platform.