Why Data Quality Is the Real Foundation of GenAI Success (Part 2)

From Strategy to Execution: Making Data Governance Actionable

The BARC BI & Analytics Trend Monitor 2024 confirms a clear direction: organizations are no longer debating the importance of data quality — they’re now grappling with execution. While GenAI garners headlines, the real groundwork lies in operationalizing robust data governance and elevating data literacy across teams.

Data governance has evolved well beyond a compliance checklist. It’s now a vital productivity engine that cultivates trust, speeds up AI integration, and improves collaboration throughout the organization. For successful Generative AI initiatives, foundational data governance at the source is non-negotiable. This means prioritizing metadata gathering, tracing data lineage, and embedding security as close to the data’s origin as possible.

Data literacy is a vital competency in Data & Analytics. But how do you approach this?

One idea is to use the test provided by The Data Manifesto community (dataleaders.org). They offer several tools to help you get started with data management. Regardless of whether you use free resources like these, or paid software, clean and reliable data remains the limiting factor for GenAI.

 Organizations that integrate data quality and governance into their daily processes will be the ones to implement GenAI responsibly and successfully. GenAI relies heavily on the quality of underlying metadata (see blog). Every organization has some form of data archive. By applying metadata governance at the source, you create well-described and easily discoverable and trusted data.

 A strong example is the Flemish Government, which actively promotes the use of OSLO standards (Open Standards for Linking Organizations). By using semantic models and consistently recording metadata, it becomes much easier to share government data across different public institutions. This garners trust and enables collaboration.