Last week, I had the opportunity to attend this year’s Gartner Data & Analytics Summit where global data leaders came together to explore how organisations are navigating an increasingly complex digital landscape. One theme echoed across every session: as
data ecosystems continue to evolve, the need for trusted, well-governed, high-quality data has never been more critical.
While innovation is accelerating, the summit made it clear it’s the fundamentals that will shape the future. As organisations move quickly to adopt AI and automation, their long-term success will depend on the strength of the foundations they build. Data
quality, governance, and trust are no longer optional; they’re essential.
Over and above the fundamentals, here are five key takeaways from the summit and what they mean for data leaders navigating this evolving landscape.
1. Metadata is the new strategic asset
Once considered a technical detail, metadata has become central to how organisations manage and scale their data strategies. Conversations at the Summit emphasised the value of starting with technical metadata and gradually adding business context to improve
discoverability, governance, and overall data value.
This shift is especially significant, with 70% of Chief Data & Analytics Officers (CDAOs) now leading their organisation’s AI strategy. For AI to be effective, transparent, and trustworthy, it must be built on well-structured data. Metadata can provide that
clarity, helping organisations understand what data exists, where it came from, and how it’s being used. This is all needed to create strong foundations for building AI systems that people can trust.
2. AI agents rely on trustworthy data
AI agents that automate decisions and actions are gaining momentum across industries. But their effectiveness depends entirely on the quality and governance of the data they use.
The summit highlighted the importance of piloting use cases that connect insights to natural language interfaces, while embedding strong governance from the start. With a third of enterprise software expected to include agentic AI by 2028, now is the time
to ensure data pipelines are built on trust and transparency.
3. Data fabric needs scalable governance to succeed
Data fabric architectures are helping organisations manage data across increasingly distributed environments. However, without consistent governance, these systems can quickly become fragmented and unreliable.
Conversations at the Summit underscored the need for a metadata management strategy that spans the entire data lifecycle. This not only supports compliance and consistency but also enables data to be used more effectively across the business.
4. Synthetic data shows promise when the basics are strong
Synthetic data is emerging as a powerful tool for filling gaps in datasets, especially where privacy concerns or limited access make real data hard to use. It’s being applied to train AI models, simulate scenarios, and reduce reliance on sensitive information.
That said, its effectiveness depends on the quality of the underlying data. Before turning to synthetic alternatives, organisations must first address any gaps or weaknesses in their existing data foundations.
5. Governance remains the biggest challenge and the biggest opportunity
Data governance remains a major challenge. According to the Gartner CDAO Agenda Survey, many organisations cite budget constraints, cultural resistance, skills shortages, or inadequate frameworks as key barriers.
Despite these obstacles, governance also represents a significant opportunity. A proactive, business-aligned approach, one that encourages cross-functional collaboration and focuses on practical, scalable solutions, can turn governance into a strategic advantage.
The 2025 summit made one thing clear: data governance and quality are no longer optional, they are essential pillars of modern data strategy. As organisations scale their analytics capabilities and embrace AI, their ability to trust, understand, and manage
data will determine their long-term success.
