Gartner Identifies Key Data and Analytics Trends for 2025

Gartner has outlined the top data and analytics (D&A) trends for 2025, highlighting emerging challenges, including organizational and human factors. These trends reflect the evolving landscape of data-driven decision-making and the complexities enterprises face in implementing effective analytics strategies.

“D&A is going from the domain of the few, to ubiquity,” said Gareth Herschel, VP 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.”

Key Data and Analytics Trends for 2025 Identified by Gartner 

Gartner has outlined critical data and analytics (D&A) trends for 2025, highlighting emerging challenges and strategic priorities for organizations. These trends emphasize data product usability, AI advancements, metadata management, and decision intelligence to enhance operational efficiency and business outcomes.  

Highly Consumable Data Products
 
D&A leaders should prioritize business-critical use cases and ensure data products are reusable and scalable. Establishing consensus on key performance indicators between data producers and consumers is essential for measuring product success.  

Metadata Management Solutions

Organizations should begin with technical metadata and expand to business metadata for enhanced context. Automated tools that support metadata discovery, data catalogs, and AI-driven use cases can improve data governance and usability.  

Multimodal Data Fabric

Capturing and analyzing metadata across the data pipeline enables automation, supports DataOps practices, and enhances the orchestration of data products, improving operational efficiency.  

Synthetic Data

Organizations should identify gaps in data availability and leverage synthetic data to ensure privacy, reduce costs, and advance AI model development without relying on sensitive information.  

Agentic Analytics

AI-driven analytics can automate business outcomes by integrating insights into natural language interfaces. Governance mechanisms should be in place to minimize errors and ensure AI readiness.  

AI Agents

Beyond large language models (LLMs), AI agents can facilitate adaptive automation and seamless data exchange across applications. Organizations should integrate diverse AI techniques for better analytical performance.  

Small Language Models (SLMs)

SLMs can provide domain-specific, contextually accurate AI outputs while reducing computing costs. Organizations should explore retrieval-augmented generation and fine-tuning for improved AI efficiency.  

Composite AI

Combining multiple AI techniques—such as machine learning, knowledge graphs, and optimization—can enhance AI’s effectiveness beyond generative AI and LLMs. 

Decision Intelligence Platforms

Shifting from a data-driven to a decision-centric approach requires prioritizing critical business decisions, adopting decision intelligence (DI) platforms, and addressing ethical and compliance considerations in automation.  

Gartner Data & Analytics Summit

Gartner analysts will provide further insights at the Gartner Data & Analytics Summit, taking place in Orlando, with additional sessions scheduled in São Paulo (April 28-29), London (May 12-14), Tokyo (May 20-22), Mumbai (June 2-3), and Sydney (June 17-18). Follow updates using GartnerDA on X.

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