In early June, the Data Governance and Information Quality (DGIQ) conference, organized by Dataversity, took place at the picturesque Catamaran Resort Hotel and Spa in San Diego, CA. With over 500 data specialists in attendance, the conference provided a comprehensive program featuring tutorials, general sessions, special interest group discussions, and post-conference seminars. Here’s a recap of the highlights and key insights from this event.
AI Governance: The New Frontier
Data Governance has long been a critical, albeit often underappreciated, aspect of enterprise operations. With the advent of AI, however, there is an emerging need for AI Governance, which is poised to become as indispensable as its data counterpart.
At the DGIQ conference, it became clear that AI Governance is not just a buzzword but a crucial evolution of data governance practices. Here are some key takeaways from the sessions I attended:
Key Insights and Takeaways
- AI Governance as an Extension of Data Governance:
- AI Governance is seen as an extension rather than a replacement of Data Governance. According to Kelle O'Neil of First San Francisco Partners, "AI Governance is the evolution of Data Governance," emphasizing that generative AI is data governance’s “big break.”
- Frameworks and Best Practices:
- The successful implementation of any governance effort, be it data or AI, hinges on robust frameworks, models, and best practices. These need to be tailored to the specific needs of the organization.
- Starting Small:
- Starting small with Data Governance, particularly at a project level, is critical. ThermoFisher Scientific, for instance, has integrated Data Governance into their project-level framework, facilitating rapid adoption and secure funding, thereby ensuring better organizational buy-in.
- Industry Insights and Use Cases:
- Engaging with industry leaders and examining real-world use cases provides invaluable insights. These interactions at the conference highlighted the overlapping needs of Data and AI Governance.
- Clear Data Ownership:
- For a successful integration of AI Governance into existing Data Governance programs, it’s crucial to establish clear data ownership and develop a self-sustaining program.
- Focus on the Present:
- As Robert Seiner of KiK Consulting advises, focusing on the “Here and Now” is essential for handling immediate requirements and setting a foundation for successful AI Governance.
- Adopting Holistic Frameworks:
- Rob Bagley of Perficient emphasized the importance of adopting holistic frameworks like PACE to operationalize AI effectively across the organization.
Conclusion: Is AI Governance the New Data Governance?
While AI Governance may not completely replace Data Governance, there is significant overlap and the two are intrinsically linked. The conversation within organizations must evolve to define what governance means in the context of AI and how it will shape future business practices.
At Core BTS, we are committed to helping you navigate this evolving landscape. Our expertise in Data Governance, coupled with emerging best practices in AI Governance, ensures that we can guide you through this transformative journey, ensuring your organization is well-prepared for the future.
Looking Ahead
If you missed this conference, Dataversity is hosting the East version of the DGIQ conference in December in the DC area. It's a great opportunity to stay ahead of industry trends and continue the conversation on Data and AI Governance.
For more details about the event, visit the DGIQ website.
Check out Core BTS’s AI solutions to learn how we can help you navigate the evolving landscape of data and AI governance.
