You can measure the ROI of a data observability platform by tracking key metrics like the number of data incidents per year, time to detection, and time to resolution. These real-time metrics give you insight into how often issues occur and how quickly your team can resolve them. Don’t forget to factor in qualitative benefits too, like improved team satisfaction and stronger data governance.

Absolutely! When data consumers can see historical performance through data observability dashboards, it builds transparency and trust. They’re more likely to rely on your data if they know it’s been consistently accurate and well-maintained over time.

Sifflet’s observability platform is designed to provide real-time metrics and proactive monitoring through advanced data quality checks, anomaly detection, and custom health scores. This helps data teams catch issues before they escalate, ensuring your data products stay healthy and consistent.

When paired with dbt, Sifflet provides robust data quality monitoring by combining dbt test insights with ML-based rules and UI-defined validations. This helps you close test coverage gaps and maintain high data quality throughout your data pipelines.

Not quite. While data quality monitoring helps ensure individual datasets are accurate and consistent, data reliability goes further by ensuring your entire data system is dependable over time. That includes pipeline orchestration visibility, anomaly detection, and proactive monitoring. Pairing data quality with a robust observability platform gives you a more comprehensive approach to reliability.

The MCP (Model Context Protocol) Server is a new interface that lets you interact with Sifflet directly from your development environment. It's designed to make data observability more seamless by allowing you to query assets, review incidents, and trace data lineage without leaving your IDE or notebook. This helps streamline your workflow and gives you real-time visibility into pipeline health and data quality.

Integrating data observability helps you stay on top of data issues before they impact your users. With real-time metrics, pipeline error alerting, and dynamic thresholding, you can catch problems early and ensure your data meets SLA requirements. This proactive monitoring helps teams maintain trust and deliver consistent, high-quality data services.

jobvalley enhanced data visibility by implementing Sifflet’s observability platform, which included a powerful data catalog. This centralized hub made it easier for teams to discover and access the data they needed, fostering better collaboration and transparency across departments.