We’re seeing a big shift where data consumers—like analysts and business users—are finally getting a seat at the table. That’s because data observability impacts everyone, not just engineers. When trust in data is operationalized, it boosts confidence across the business and turns data teams into value creators.

MCP helps LLMs become active participants in data quality monitoring by giving them access to structured resources like schema definitions, data validation rules, and profiling metrics. At Sifflet, we use this to detect anomalies, enforce data contracts, and ensure SLA compliance more effectively.

Data quality management works best when it's a shared responsibility. Data stewards often lead the charge by bridging business needs with technical implementation. Governance teams define standards and policies, engineering teams build the monitoring infrastructure, and business users provide critical domain expertise. This cross-functional collaboration ensures that quality issues are caught early and resolved in ways that truly support business outcomes.

Look for tools that support your data sources and destinations, offer automation, and ensure compliance. Features like schema registry integration, real-time metrics, and alerting can also make a big difference. A good tool should work seamlessly with your observability tools to maintain data quality and trust.

Great question! If you're constantly dealing with missing values, duplicates, or inconsistent formats, a data observability platform can be a game-changer. It provides real-time metrics and data quality monitoring, so you can detect and fix issues before they impact your reports or decisions.

Great question! While data quality monitoring focuses on alerting teams when data deviates from expected parameters, data observability goes further by providing context through data lineage tracking, real-time metrics, and root cause analysis. This holistic view helps teams not only detect issues but also understand and fix them faster, making it a more proactive approach.

Classification tags help define the structure and importance of your data, which in turn makes it easier to configure real-time metrics and alerts. For example, tagging a 'country' field as low cardinality allows teams to monitor sales data by region, enabling faster anomaly detection and more actionable real-time alerts.

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.