What Gartner’s 2025 Summit means for your data strategy
At the 2025 Gartner Data & Analytics Summit, the spotlight was on how organisations can scale their data strategies in an increasingly complex environment.
Top data trends like complexity, trust and empowerment are now central to effective data leadership, shaping how decisions are made across organisations.
From AI agents to synthetic data, the future of analytics is intelligent, automated and fast-moving. But none of it works without a solid foundation of trusted, governed, high-quality data.
In this article, we share our five key takeaways from the summit and what they mean for your organisation, and how Aperture Data Studio can help.
1. Metadata is the new foundation for AI and analytics
Metadata is no longer just a technical detail; it’s a strategic asset. According to Gartner, organisations should “start with technical metadata and add business metadata to enable context”.
At the event, Experian discussed with Diane Elvers, Senior Director of Data Governance and Standards at AstraZeneca, how metadata is critical for AstraZeneca. It plays a key role in tracking where data resides, how it’s used, and ensuring compliance, especially in the context of strict regulatory requirements around patient data. These regulations demand that data is not only protected but also clearly identified, and metadata is essential to meeting those expectations.
This focus on metadata is even more relevant today as 70% of Chief Data & Analytics Officers (CDAOs)[1] are now responsible for their organisation’s AI strategy and operating model. High-quality metadata underpins trustworthy AI by ensuring data is discoverable and governed.
2. AI agents are here, and they need trustworthy data
AI agents that automate decisions and actions (agentic analytics) are on the rise. But they can only succeed if the data they are trained with and rely on is accurate and trustworthy.
Gartner recommends piloting use cases that connect insights to natural language interfaces, evaluating vendor integration plans, and ensuring strong data governance. Applying AI-ready data principles is also key to minimising errors and maximising impact.
Looking ahead, Gartner predicts[2] that by 2028, 33% of enterprise software applications will include agentic AI, a huge step up from less than 1% in 2024, with at least 15% of day-to-day work decisions being made autonomously through AI agents. This marks a significant shift in how businesses will operate, moving from insight generation to automated, intelligent action.
3. Data fabric adoption is growing but governance must scale with it
Gartner identifies[3] data fabric as a foundational architecture for modern data management, enabling scalable, automated data integration across distributed environments. Enabling them to connect disparate data sources and have seamless access, management and governance of data across their business. But without strong governance, these architectures risk becoming fragmented and unmanageable.
To address this, Gartner recommends building a metadata management practice where tools share metadata across the entire data pipeline. Ensuring metadata is consistent, accurate and readily available to support business goals and compliance.
4. Synthetic data is promising, but only if your real data is solid
Synthetic data is gaining traction as a way to fill gaps where data is missing, incomplete, or sensitive. Many companies are already using this to improve AI model training, mitigating privacy concerns, and testing various scenarios without compromising sensitive information.
However, Gartner cautions that organisations must first “identify areas where data is missing, incomplete or expensive to obtain” before relying on this. In other words, synthetic data is only as good as the data it’s based on.
5. Data governance is key to overcoming analytics challenges
In a session on the top five analytics and AI challenges, it was emphasised that a modern data and analytics strategy must foster data quality and governance as a foundation for real-time insights and cross-functional action. The discussion highlighted the importance of establishing trust, demonstrating value, and adopting a solutions-first approach.
This message is reinforced by findings from the Gartner Chief Data and Analytics Officer Agenda Survey, which identified several persistent barriers to success:
These challenges reflect the growing complexity of data leadership, and the need for tools that help organisations move from reactive data management to proactive, insight-driven decision-making.
The Gartner Summit made one thing clear: data governance and quality are no longer optional, they’re essential. Whether you’re leading data strategy in a global enterprise, scaling analytics in a growing business, or modernising legacy systems—trusted data is your most valuable asset. And that’s where Experian’s Aperture Data Studio comes in.
Contact us to learn how Aperture Data Studio can help your organisation.
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[1] Gartner Survey Finds 70% of CDAOs Are Responsible for AI Strategy and Operating Model Gartner
[2] Capitalize on the AI Agent Opportunity Gartner
[3] How Data Fabric Can Optimize Data Delivery Gartner