Strong data governance is foundational to robust artificial intelligence (AI) governance. Companies developing or deploying responsible AI must start with strong data governance to prepare for current or upcoming regulations and to create AI that is explainable, transparent and fair.

Transparency about data is essential for any organization using data to drive decision-making or shape business strategies. It helps to build trust, accountability and credibility by making data and its governance processes accessible and understandable. However, this transparency can be hindered by incomplete or unclear data set metadata, often requiring time-consuming manual investigation to resolve.

To help address this issue, IBM partnered with the Data & Trust Alliance and 18 other enterprises to co-create and test the Data Provenance Standards, the first cross-industry standards for metadata to help describe data origin, lineage and suitability for purpose. Our case study, “Optimizing data governance with the Data & Trust Alliance Data Provenance Standards,” describes our testing methodology and the results we observed.

Advancing trust and data quality with Data Provenance Standards

During our testing of the Data Provenance Standards, we observed improvements in overall data clearance review time. Our initial findings also suggest that the Data Provenance Standards can enhance overall data quality.

Due to the promise of these early results, we are aligning our internal data standards with the Data Provenance Standards where appropriate. This alignment helps us efficiently respond to the rapidly increasing volume of data clearance requests while maintaining our high standards for responsible data acquisition. This is crucial because, as a company that has been in operation for over 110 years, we know that trust is a key reason for our longevity.

For IBM, building trustworthy AI means having clear principles for trust and transparency, putting those principles into practice, and embedding ethics into every facet of the AI lifecycle. For example, IBM® Granite™ foundation models are among the most transparent in the world, thanks in part to their adherence to data governance and risk criteria enabled through our existing data clearance review process.

These new, cross-industry Data Provenance Standards can help fill a critical gap, enabling greater transparency about data provenance and fostering the development of trustworthy and responsible AI across all industries. We welcome their adoption across the data ecosystem and are ready to support clients in implementing their own data governance frameworks.

Explore the study Learn about responsible AI at IBM
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