A strong data quality monitoring framework should be scalable, rule-based and powered by AI for anomaly detection. It should support multiple data sources and provide actionable insights, not just alerts. Tools that enable data drift detection, schema validation and real-time alerts can make a huge difference in maintaining data integrity across your pipelines.

Looking ahead, Carrefour plans to expand monitoring to more than 1,500 tables, integrate AI-driven anomaly detection, and implement data contracts and SLA monitoring to further strengthen data governance and accountability.

By enriching and aggregating operational data, Shippeo creates a reliable source of truth that supports product and operations teams. Their pipeline health dashboards and observability tools ensure that internal stakeholders can trust the data driving their decisions.

Absolutely! In early 2025, we're rolling out advanced root cause analysis tools designed to help you detect subtle anomalies and trace them back to their source. Whether the issue lies in your code, data, or pipelines, our observability platform will help you get to the bottom of it faster.

Table-level lineage helps teams perform impact analysis, debug broken pipelines, and meet compliance standards by clearly showing how data flows between systems. It's foundational for data quality monitoring and root cause analysis in modern observability platforms.

Absolutely! By identifying all downstream assets affected by a dbt model change, Sifflet’s Impact Report makes it easier to trace issues back to their source, significantly speeding up root cause analysis and reducing incident resolution time.

At Sifflet, we're integrating advanced AI models into our observability platform to enhance data quality monitoring and anomaly detection. Marie, our Machine Learning Engineer, has been instrumental in building intelligent systems that automatically detect issues across data pipelines, making it easier to maintain data reliability in real time.

AI depends on trustworthy data, and that’s exactly where data observability comes in. With features like data drift detection, root cause analysis, and real-time alerts, observability tools ensure that your AI systems are built on a solid foundation. No trust, no AI—that’s why dependable data is the quiet engine behind every successful AI strategy.