Etico’s Data Quality (DQ) Framework provides a practical structure to assess, monitor, and improve data so it consistently delivers measurable business value.

1. Foundational Elements

• Data Strategy – a business-aligned strategy that defines why and how data supports the organization’s objectives.
• Principles & Policies – core beliefs and mandatory rules that guide all DQ activities.
• Standards & Requirements – consistent definitions and business rules that specify what “good” data looks like.
• Metadata – clear documentation of data meaning, lineage, and ownership.
• Tools – technology to capture, catalog, and manage data effectively.

2. Data Quality Management Activities

• Proactive Prevention – embed quality checks at the point of data entry and integration.
• Profiling & Assessment – evaluate existing datasets to detect anomalies and measure quality against defined dimensions.
• Root-Cause Analysis – uncover and address systemic causes of poor quality.
• Remediation – apply corrective measures to clean, enrich, and reconcile data.

3. Data Quality Outputs

• Rules – codified checks and business logic to enforce standards.
• Scripts – automated routines for profiling, validation, and cleansing.
• Reports & Dashboards – regular reporting to track metrics, trends, and compliance.

4. Outcomes and Business Benefits

• Increased data quality and reliability.
• Better, faster decision making.
• Improved organizational knowledge and insights.
• Greater trust in analytics, AI, and automated processes.