Data Governance in the AI Era: A Conversation with Data Governance Coach Nicola Askham

Key Takeaways:

• Data governance has transitioned from a regulatory necessity to a strategic asset that enhances innovation.
• Robust data governance supports AI applications, ensuring data is AI-ready, and facilitating sound AI governance practices.
• You need data integrity – accurate, consistent, and contextualized data – to build trust in AI applications and improve overall decision-making processes.

The Gartner® Data and Analytics Summit in London brought together data professionals from around the world to share the latest strategies, ideas, and innovations that are enabling them to harness the full potential of their data.

During this conference, Tendü Yoğurtçu, PhD, Chief Technology Officer at Precisely sat down with Nicola Askham, the renowned Data Governance Coach to chat about the evolving landscape of data management and how organizations should be leveraging data governance to get the most from AI technologies.

Watch their full discussion below and read the recap for all you need to know.

The Evolution of Data Governance

Data governance has come a long way. It hasn’t always been the most exciting buzzword in the tech world – in fact, it had built up a reputation of being a “boring” necessity for some industries, rather than the vital component of business strategy that it’s blossomed into today.

Askham observed that “in the early days, data governance was mainly done by regulated sectors … it was all about what you had to do to keep the regulators happy rather than the business value.” But today, this perspective has shifted significantly. The integration of advanced technologies like artificial intelligence (AI) has spotlighted the importance of business-friendly data governance frameworks that do more than just tick regulatory boxes – they drive innovation.

Modern Data Governance for the AI Era

The AI revolution has brought with it a wave of generative AI applications, requiring robust data governance to ensure success. Yoğurtçu noted that Gartner keynotes highlighted the importance of AI-ready data and focused on the execution and uses cases of data governance rather than on broader initiatives, “we have been talking about artificial intelligence for multiple decades, but generative AI has heightened the awareness that this is now also an opportunity for non-technical users, and it’s driving more focus to the data foundation and on making data AI-ready.”

Askham notes that data governance is directly linked to AI governance, as the data for these applications needs to be relevant and of high quality, “Data governance and data quality work very closely together, and in my opinion, always work well as part of the same team … AI governance should sit there too, because we’re all worried about the same thing – that it’s the right data, good enough to be used, and that we’re using it in the right way.”

It’s clear that for AI to truly soar, it must be underpinned by sound data governance that ensures the data is reliable, relevant, and rigorously maintained.

For Trusted AI, You Need Data Integrity

In the quest for trusted AI, data integrity forms the bedrock of successful applications. Yoğurtçu articulates three pillars of data integrity: ensuring data is accurate, consistent, and contextualized.

Building context by enriching internal data with third-party data is critical for uncovering hidden insights that improve your data’s completeness, enhance decision-making, analytics, and AI and machine learning projects.

And yet, many organizations have yet to embrace its power. As Askham says, “We need to be shouting about that to get people to understand that context is everything. You can’t just look at some raw data and say whether it’s good or bad.”

A Tale of Two Cities: Data Mesh and Data Fabric

As organizations strive to manage their increasingly complex data landscapes, two concepts have emerged at the forefront of data governance conversations: data mesh and data fabric. “I call them ‘A Tale of Two Cities’,” says Yoğurtçu, “because data mesh is more about the distributed architecture and framework and pushes the ownership to the business users … and in the case of data fabric, the architecture is more focused on driving intelligence from metadata.”

While these two concepts have their differences, both approaches require a mature data foundation and a deep understanding of metadata to deliver on their potential.

Moving Forward: What’s Next in Your Data Governance Journey?

What are your biggest takeaways from this conversation between Tendü Yoğurtçu, PhD and Nicola Askham? How is your business embracing data governance in the era of AI and advanced analytics?

Watch the full video to hear all of the insights from Tendu and Nicola and get inspired for your own journey towards more informed, effective data governance today. And if you’re ready to take the next step, contact our experts today.