Gartner Identifies Key Data and Analytics Trends Shaping 2025
These trends will help D&A leaders meet the pressures, expectations, and demands they are facing in 2025.
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[Image source: Krishna Prasad/MITSMR Middle East]
As data becomes ubiquitous across business functions, Data & Analytics (D&A) leaders face mounting pressure to scale their operations effectively while navigating organizational, technical, and human challenges. Gartner, Inc. has identified key trends for 2025 that will shape the future of D&A, emphasizing the need for highly consumable data products, robust metadata management, multimodal data fabrics, and the strategic use of synthetic data.
“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.”
Highly Consumable Data Products Are Crucial for Success
D&A leaders must focus on business-critical use cases to take advantage of highly consumable data products. Scaling and correlating products to resolve data delivery issues is key. Prioritizing the creation of reusable and composable minimum viable data products is essential, allowing teams to refine them over time. Reaching consensus on key performance indicators between producing and consuming teams is also vital for measuring success.
Effective Metadata Management Requires a Holistic Approach
Successful metadata management starts with technical metadata and expands to include business metadata for enhanced context. Organizations should focus on selecting tools that facilitate the automated discovery and analysis of metadata to support data catalogs, data lineage, and AI-driven use cases.
Building a Robust Multimodal Data Fabric Is Key
A solid metadata management practice captures and analyzes metadata throughout the entire data pipeline. The insights and automation from the data fabric support orchestration demands, improve operational excellence through DataOps, and enable the creation of data products.
Synthetic Data Ensures Privacy and Supports AI Development
Synthetic data, used as variations of original data or replacements for sensitive information, is essential for advancing AI initiatives while maintaining data privacy. Identifying areas with missing, incomplete, or costly-to-obtain data is crucial.
AI Agents and Small Language Models Enhance Automation and Accuracy
AI agents are vital for complex automation needs, enabling flexible access to data across applications. Small language models, in particular, provide more contextually appropriate AI outputs, especially in specific domains. Fine-tuning custom models for on-premises use helps manage sensitive data and reduce costs.
Decision Intelligence Platforms Help Transition to a Decision-Centric Vision
D&A leaders should shift from a data-driven approach to a decision-centric one by prioritizing key business decisions, aligning with decision intelligence practices, and evaluating DI platforms. Addressing ethics, legal, and compliance considerations in decision automation is also crucial for success.
