It's all about presentation and relevance. Whether you're using Tableau dashboards or traditional slide decks, your data should be tailored to the decision-maker's needs. This is where data observability dashboards and metrics aggregation come in handy, helping to surface the most impactful insights clearly and quickly so leaders can act with confidence.

Yes, definitely! Companies like Saint-Gobain will be sharing how they’ve used Sifflet for data observability, data lineage tracking, and SLA compliance. It’s a great chance to hear how others are solving real data challenges with our platform.

Great question! Sifflet bridges the observability gap for Airbyte by using our Declarative Lineage API and a custom Python script. This allows you to capture complete data lineage from Airbyte and ingest it into Sifflet, giving you full visibility into your pipelines and enabling better root cause analysis and data quality monitoring.

We prioritize user research and apply UX principles like Jacob’s Law to design familiar and intuitive workflows. This helps reduce friction for users working with tools like our Sifflet Insights plugin, which brings real-time metrics and data quality monitoring directly into BI dashboards like Looker and Tableau.

When choosing a data lineage tool, look for easy integration with your data stack, a user-friendly interface for both technical and non-technical users, and complete visibility from data sources to storage. These features ensure effective data observability and support your broader data governance efforts.

Accessibility is a core part of our design philosophy. We ensure that key indicators in our observability tools, such as data freshness checks or pipeline health statuses, are communicated using both color and iconography. This approach supports inclusive experiences for users with visual impairments, including color blindness.

Retailers are adopting data observability to gain real-time visibility into inventory across all channels, reduce stock inaccuracies, and avoid costly misalignments between supply and demand. With data observability tools, they can proactively detect issues, monitor data quality, and improve operational efficiency across their data pipelines.

AI models are only as good as the data they’re trained on. With data observability, you can ensure data quality, detect data drift, and enforce validation rules, all of which are critical for reliable AI outcomes. Sifflet helps you maintain trust in your data so you can confidently scale your ML and predictive analytics efforts.