Gartner identifies top trends in data and analytics for 2025 – and AI takes the lead
Data is at the heart of most organizations, fueling everyday business functions. To help digital leaders better prepare their data and analytics (D&A) strategies, Gartner has identified the top D&A trends for 2025.
“D&A is going from the domain of the few, to ubiquity. At the same time D&A leaders are under pressure not to do more with less, but to do a lot more with a lot more, and that can be even more challenging because the stakes are being raised,” said Gareth Herschel, VP analyst at Gartner. “There are certain trends that will help D&A leaders meet the pressures, expectations and demands they are facing.”
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Out of the nine trends Gartner identified, unsurprisingly, AI-related technologies made up more than half of the list, including the biggest AI trend of the time — agents. Agentic AI has begun permeating every business sector, with organizations finding ways to implement the autonomous assistance that AI agents offer.
Gartner advised D&A leaders to use agents to access and share their organization’s data across applications. The analyst also recommended that D&A leaders use AI agents to automate closed-loop business outcomes, where data-driven insights continuously inform and optimize decisions.
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When generative AI first became mainstream, the focus for many D&A leaders and their organizations was developing and implementing large language models (LLMs).
However, greater emphasis has since been placed on the value of small language models (SLMs). These small models are lightweight, tailored, cheaper, and faster to train, which is better for specific use cases. As a result, Gartner advised D&A leaders to consider SLMs for more accurate and contextually appropriate AI outputs.
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As there are so many different tools that D&A leaders can use, Gartner also recommended composite AI, which is the process of leveraging multiple AI techniques to increase technological effectiveness. This approach means exploring technology beyond generative AI and LLMs to take a deeper look at related disciplines, such as machine learning and data science.
Some of the trends that Gartner identified are indirectly related to AI. For example, the analyst encouraged using synthetic data to supplement areas where insight is missing or incomplete. This approach is especially valuable when using data for AI initiatives, as these projects require organized, complete data foundations for training and deployment. Another advantage of synthetic data is that it can replace sensitive data, prioritizing privacy, which is especially important for AI.
Building on this conception, Gartner identified metadata management solutions as an imperative trend, advising organizations to implement tools that automate finding and analyzing metadata. The analyst said various metadata types, including technical and business metadata, can then be used for data catalogs, data lineage, and AI-driven use cases. In its multimodal data fabric trend, Gartner advised collecting and analyzing information at the metadata stage of the data pipeline.
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Other key trends highlighted by Gartner include decision intelligence platforms, which help organizations shift from simply using data to making smarter, decision-focused strategies. The analyst said this shift is critical to success.
Gartner also pointed to highly consumable data products as a trend, emphasizing the need for organizations to create useful, reusable data products that different teams can access to optimize and improve business-critical use cases over time.
