Part 5/9:
3. Assign Data Quality Scores
For each key data element, organizations should assign scores based on the identified metrics. For example, a Customer ID that is complete, consistent, and unique might score higher than one with missing or conflicting information.
4. Ownership and Accountability
Crucially, the speaker stressed that ownership of data quality should reside at the source—the teams responsible for data collection and entry. They need to be responsible for maintaining high standards, which can be incentivized via KPIs tied to data quality scores.
Transforming Data Quality into a Business KPI
The ultimate aspiration is to embed data quality into an organization’s performance metrics. By doing so:
- Teams become more accountable for data accuracy.