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.

Data observability was crucial in maintaining high data quality for loyalty programs and marketing campaigns. With real-time metrics and anomaly detection in place, Carrefour was able to improve customer satisfaction and retention through more accurate and timely insights.

Sifflet Insights integrates directly into your BI tools like Looker and Tableau, providing real-time alerts about upstream data quality issues. This ensures you always have accurate and reliable data for your reports, which is essential for maintaining data trust and improving data governance.

Semantic quality monitoring ensures that the data feeding into your AI models is contextually accurate and production-ready. At Sifflet, we're making this process seamless with tools that check for data drift, validate schema, and maintain high data quality without manual intervention.

If your team struggles with delayed dashboards, inconsistent metrics, or unclear data lineage, it's likely time to invest in a data observability solution. At Sifflet, we even created a simple diagnostic to help you assess your data temperature. Whether you're in a 'slow burn' or a 'five alarm fire' state, we can help you improve data reliability and pipeline health.

dbt Impact Analysis is a new feature from Sifflet that automatically comments on GitHub or GitLab pull requests with a list of impacted assets when a dbt model is changed. This helps teams enhance their data observability by understanding downstream effects before changes go live.

For sure! Our CEO, Salma Bakouk, will be speaking about the top data trends to watch in 2025, including how GenAI and advanced anomaly detection are shaping the future of observability platforms. You’ll walk away with actionable insights for your data strategy.

Absolutely. MCP gives LLMs the ability to retain memory across multi-step interactions and call external tools, which is incredibly useful for root cause analysis. At Sifflet, we use this to build agents that can pinpoint anomalies, trace data lineage, and surface relevant logs automatically.