Data & AI Insights: Key Learnings from Gartner's 2024 Summit

By Salima Mangalji

Published on April 5, 2024

Sallie Mae customer session at Gartner DA

The Happiest Place on Earth: Disney World!

And the location of the 2024 Gartner Data & Analytics Summit. This conference brings together top data experts from around the world. Yet this year, it was much more than that; it was a showcase of the future, in which leaders shared a vision for the transformative power of data and AI. Gartner is known for setting the tone for the year's data and analytics strategies, and the conference did not disappoint, offering rich insights into how businesses can navigate and leverage data to drive meaningful outcomes in that evolving digital landscape.

Alationauts at Gartner
Alationauts at Gartner

The Alation team was there in full force, and our booth was abuzz with conversations focused on the exciting possibilities of a data intelligence platform with the power of AI.

Here are some of our favorite themes and tips we’re still talking about from the event:

1. “Good things happen when data is being shared.”

The theme at the conference was Collective Intelligence, which describes collaborative action between humans and AI to drive value and solve problems. Collective intelligence will prove especially significant with the rise of AI initiatives in organizations, and establishing a strong data literacy foundation alongside AI training for the “humans in the loop” will be integral to the success of such initiatives.

However, the potential upside of such projects is mitigated by potential risks. In the opening keynote, Gartner's Distinguished VP Analyst Debra Logan and VP Analyst Ehtisham Zaidi laid bare a pressing reality: over half of the respondents in a 2023 survey voiced concerns over their ability to manage AI risks. This underscores the dual challenge data and analytics leaders face: navigating the complexities of AI initiatives while spearheading effective data and AI governance, which reduces the many risks inherent to AI.

2. Implement a modern outcome-driven approach to governance.

In fact, Gartner predicts that, by 2027, 80% of D&A governance initiatives will fail due to a lack of a real or manufactured crisis.  In essence, Gartner is suggesting that without a compelling reason to implement strong governance measures for handling data and analytics, most initiatives are likely to falter. Crises like the pandemic and skyrocketing energy costs have created forcing functions that enabled leaders to scale governance.

Those who succeeded tied D&A initiatives to tangible business outcomes and flexibly scaled with the organization's goals. Although technology has an important role, governance starts with people and processes; it cannot be a cookie-cutter process – hence the importance of driving a data culture within an organization.

Leaders also urgently need to find a quantitative way to demonstrate data governance results and improvements. One way to enforce this is to benchmark your organization’s data culture capabilities with a maturity assessment. This creates a deeper, shared understanding of the status quo, so leaders know what is working within the team and which areas need improvement.

3. To drive innovation, boost data literacy and sharing.

For innovation to thrive, leaders must cultivate an environment of data sharing and literacy while democratizing data access. When it comes to organizational data maturity, data literacy is one of the critical pillars that needs to be addressed. This goes beyond individual skill sets to encompass a whole-of-organization approach to data literacy.

Alationauts at Gartner

To speak to these themes, our VP of PMM, Steve Wooledge, sat down with Elizabeth Friend, Sr. Director of Data Governance from Sallie Mae, for a breakout session. In Advancing Data Culture Through Governance and Leadership, the duo discussed Friend’s team’s approach to the data governance and data culture journey at Sallie Mae. She expanded on three main goals:

  1. Building data governance and leadership 

  2. Building data stewardship to promote management and oversight of their data assets

  3. Building data literacy

Friend shared how Sallie Mae used to do “data governance things” in different areas of the company. They’re now building a strong governance framework bolstered by a data governance steering group which includes all the analytics team leaders from across the enterprise. With Alation as a core component in their enterprise data strategy, Sallie Mae has developed a stewardship program where folks who are already data experts curate and oversee high-value data assets. The first curation effort addressed Sarbanes-Oxley (SOX) related assets to provide a single source of truth for the organization's most critical data.

Well-governed, accessible data will play a key role in Sallie Mae’s transformation from a “loan servicing” company to a full-service educational services company. That’s why they’ve created an Analytics Academy to boost data literacy across the organization, and thanks to the Alation Data Culture Maturity Assessment, Friend and her team have a clear map to follow to enhance organizational data maturity.

Friend is quite confident that Alation will play a key role in that continuing data governance journey. “We want Alation to be the front door for trusted information about data at Sallie Mae,” she concluded.

Sallie Mae Advancing Data Culture Through Governance and Leadership

4. Active metadata will be essential for AI-ready data.

How do organizations move to AI readiness? Speakers agreed that active metadata plays a key role. Active metadata is dynamic in nature and will be essential in the process of getting AI ready.

This is because active metadata is essential in going from chaotic to managed complexity. Systems that capture contextual data, which can be augmented with AI, deliver robust solutions to data users. “We do not want to replace humans with AI," argued Gareth Herschel, Gartner VP Analyst. "We want people to be better with AI. We consistently see human effort and AI effort outperforming just human or AI [efforts].”

To address the overwhelming concern over AI risks, leaders are turning to AI governance and metadata management. These capabilities extend beyond risk mitigation, and can support innovation through assured data quality, observability, and rigorous data qualification processes.

5. Governance is integral to unlocking the value of AI.

Before delivering and acting on GenAI use cases, teams must ensure that critical data is well governed. Is the data accurate and trusted? Is it pursuant to regulations? Are its sources and transformations documented? AI governance addresses these questions with appropriate processes and controls.

Metadata plays a key role in AI governance. This is why there is a call for organizations to leverage their understanding and management of metadata, highlighting that governance is a compliance requisite and a strategic enabler for AI. Indeed, AI readiness relies upon dynamic metadata and a foundation of strong data governance. Teams must ensure accountability to understand who is doing what with the data. This allows organizations to build an audit trail to ensure explainability.

Alationauts at Gartner


The 2024 Gartner Data & Analytics Summit highlighted the transformative power of data and AI, as well as the importance of collective intelligence, robust governance, and data literacy in navigating the evolving digital landscape. Key takeaways include the imperative for outcome-driven governance and the critical role of active metadata in AI readiness, pointing towards a future where data and AI governance with humans in the loop are central to unlocking innovation and driving meaningful business outcomes.

Curious to learn how you can grow your organization's data culture maturity? Get started today.

  • 1. “Good things happen when data is being shared.”
  • 2. Implement a modern outcome-driven approach to governance.
  • 3. To drive innovation, boost data literacy and sharing.
  • 4. Active metadata will be essential for AI-ready data.
  • 5. Governance is integral to unlocking the value of AI.
  • Conclusion
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