Part 5/11:

• Relevance: Is the data timely and meaningful for current business needs?

• Freshness: How current is the data? Are ETL or data ingestion processes maintaining appropriate update cycles?

• Uniqueness: Are there duplicate records, especially in master data domains like customers, products, or suppliers?

• Validity: Do data formats and structures conform to expected standards (e.g., date formats, currency codes)?

Regular validation and external verification (e.g., using Aadhaar validation in India or credit bureau checks) bolster these dimensions, ensuring that internal data validation isn't blind to external discrepancies.

Stakeholders and Evolution of Data Quality Practices

Multiple roles interface in data quality management: