Part 6/11:
Many organizations struggle with who owns or is responsible for data quality. The speaker emphasized the importance of assigning data stewards and owners who are accountable for data integrity, completeness, and transformation. Without clear ownership, data quality issues persist and compromise analysis.
4. Accuracy and Transformation Transparency
Transformations applied to raw data—such as calculations or aggregations—must be transparent. If the origin and modifications of data are unclear, it undermines trust. Close collaboration with business teams to co-create and validate transformation logic prevents misinterpretations.