Gartner outlines data & analytics trends for 2025

Gartner has announced the leading data and analytics trends expected to shape the industry by 2025, highlighting various challenges that organisations may face, including both organisational and human issues.

"D&A is going from the domain of the few, to ubiquity," stated Gareth Herschel, Vice President Analyst at Gartner. "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. There are certain trends that will help D&A leaders meet the pressures, expectations and demands they are facing."

AI agents feature prominently in these trends, offering solutions for ad hoc, flexible, or complex adaptive automation needs. Gartner advises that beyond relying on large language models, D&A leaders should ensure AI agents can seamlessly access and share data across applications.

The concept of Agentic Analytics involves the automation of closed-loop business outcomes using AI agents for data analysis. Gartner suggests piloting use cases that integrate insights with natural language interfaces and evaluating vendor roadmaps for integrating digital workplace applications. Establishing robust governance to minimise errors and assessing data readiness through AI-specific principles are also recommended.

Small language models are highlighted as an alternative to large language models to achieve more accurate and contextually appropriate AI outputs within specific domains. Gartner recommends providing data for retrieval, augmented generation, or fine-tuning custom models especially for on-premises settings to manage sensitive data and reduce computational costs.

Gartner advises harnessing composite AI, which combines multiple AI techniques to enhance impact and reliability. D&A teams should move beyond generative AI or large language models, utilising data science, machine learning, knowledge graphs, and optimisation to create comprehensive AI solutions.

The focus on highly consumable data products emphasises the importance of addressing business-critical use cases and scaling these products to alleviate challenges in data delivery. Gartner points to the importance of creating reusable and composable minimum viable data products which can be iteratively improved. It also stresses the necessity for consensus on performance indicators between producing and consuming teams to measure data product success accurately.

Metadata management solutions are another key trend. Gartner suggests beginning with technical metadata and expanding to business metadata to provide enhanced context. This approach facilitates the creation of data catalogues, data lineage, and AI-driven use cases through automated discovery and analysis.

The deployment of a multimodal data fabric is urged, capturing and analyzing metadata across the data pipeline to enhance orchestration demands and improve operational excellence through techniques like DataOps, while also enabling the development of data products.

In the domain of synthetic data, Gartner highlights the need for identifying where data is missing or costly to obtain, as synthetic data can offer privacy-safe alternatives for advancing AI initiatives.

Decision intelligence platforms are encouraged as part of the transition from a data-driven to a decision-centric vision. Prioritising urgent business decisions for modeling, establishing alignment with DI practices, and evaluating DI platforms are advised. Addressing ethics, legal, and compliance in decision automation is deemed essential.