Data Quality

Overview

The Data Quality initiative establishes a native, intelligent framework for ensuring accuracy, reliability, and trustworthiness of data across the IOMETE Lakehouse.

It enables continuous validation, profiling, and monitoring of datasets and Iceberg tables — powered by both rule-based and AI-assisted checks.

This feature empowers data teams to define, enforce, and observe quality standards across domains, aligning perfectly with the Data Mesh principles of ownership, accountability, and transparency.

Planned Features

• Data Quality Rules Engine: Define column-level and table-level checks (e.g., nulls, ranges, uniqueness, referential integrity).
• Data Profiling & Statistics: Automatically generate and visualize descriptive stats (distribution, completeness, outliers).
• Quality Score Metrics: Assign quality scores to datasets and expose them in the Workspace.
• Alerts & Notifications: Notify users of validation failures or significant deviations from baselines.
• Domain-Aware Quality Management: Rules and metrics scoped within each domain for decentralized ownership.
• Historical Trend Analysis: Track data quality metrics over time for early detection of degradation.
• AI-Assisted Rule Suggestions: Recommend new validation rules based on data distributions and past failures.
• Lineage-Integrated Quality Tracking: Trace quality issues back to upstream sources using the Data Lineage graph.
• Monitoring: Surface data quality alerts in the unified observability dashboard.

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