Technological research and consulting firm Gartner has published its top data and analytics (D&A) trends for 2025.

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

The firm’s analysts presented the top D&A trends that IT leaders must navigate and incorporate into their D&A strategy at the Gartner Data & Analytics Summit in Orlando.

The trends are as follows:

1. Highly Consumable Data Products

D&A leaders, to capitalise on highly consumable data products, should focus on business-critical use cases, correlating and scaling products to alleviate data delivery challenges, the research firm advised.

Prioritising the delivery of reusable and composeable minimum viable data products is essential, allowing teams to enhance them over time.

D&A leaders must also come to a consensus on key performance indicators between producing and consuming teams, which is vital for measuring data product success.

2. Metadata Management Solutions

Effective metadata management begins with technical metadata, and then expanding to include business metadata for enhanced context.

By incorporating metadata types, organisations can enable data catalogues, data lineage, and AI-driven use cases, the consulting firm noted, further stating that selecting tools that facilitate automated discovery and analysis of metadata is imperative.

3. Multimodal Data Fabric

Building a robust data management practice involves capturing and analysing metadata across the entire data pipeline.

Insights and automations from the data fabric support orchestration demands, improve operational excellence through DataOps, and enable data products.

4. Synthetic Data

Identifying areas where data is missing, incomplete, or costly to obtain is crucial for advancing AI initiatives.

Synthetic data, either as variations of original data or replacements for sensitive data, ensures data privacy while facilitating AI development.

5. Agentics Analytics

Automatic closed-loop business outcomes with AI agents for data analysis is, according to Gartner, transformative.

Piloting use cases that connect insights to natural language interfaces and evaluating vendor roadmaps for digital workplace application integration are recommended.

Establishing governance minimises errors and hallucinations, while assessing data readiness through AI-ready data principles is essential.

Recommended reading

• What Are the Current Top Challenges and Priorities for CDAOs?
• Glasgow Uni Develops Energy-Efficient AI for Spatial Data
• Comment | Data Centres in 2025: Challenges & Opportunities

6. AI Agents

AI agents are valuable for ad hoc, flexible, or complex adaptive automation needs.

Beyond relying solely on large language models (LLMs), other analytics and AI forms are necessary.

Gartner stated that D&A leaders should enable AI agents to access and share data across applications seamlessly.

7. Small Language Models

Consideration of small language models over large language models is advised for more accurate, contextually appropriate AI outputs within specific domains.

Providing data for retrieval of augmented generation or fine-tuning custom domain models is recommended, especially for on-premises use to handle sensitive data and reduce compute resources and costs.

8. Composite AI

Leveraging multiple AI techniques enhances AI’s impact and reliability.

The research firm suggested that D&A teams should diversify beyond GenAI or LLMs, incorporating data science, machine learning, knowledge graphs, and optimisation for comprehensive AI solutions.

9. Decision Intelligence Platforms

Transitioning from a data-driven to a decision-centric vision is crucial, said Gartner.

Prioritising urgent business decisions for modelling, aligning decision intelligence (DI) practices, and evaluating DI platforms are recommended steps.

Rediscovering data science techniques and addressing ethics, legal, and compliance aspects of decision automation are essential for success.