Turn scattered data into reliable, contextually relevant fuel for AI
Turn fragmented data into reliable AI outcomes fast
Dispersed data, missing context and compute costs slow teams’ progress. IBM® watsonx.data® makes data usable and trustworthy for AI on Day 1—unifying data access, automating governance and supporting AI and BI workloads without replatforming.
Start small, validate results fast and scale AI in your current infrastructure.
In the spotlight
Features
Access and query data across cloud and on‑prem environments without moving or duplicating it, giving teams the flexibility to move faster while meeting regulatory and operational needs.
Run each AI and analytics workload on the most efficient engine, preventing overprovisioning, improving performance and helping keep costs predictable as AI usage scales.
Apply consistent access controls, policies and lineage across all data and workloads so your AI agents and analytics operate on trusted, well governed data—reducing risk and increasing confidence.
Leverage an open, standards-based foundation that works with your existing technology stack and supports new tools and workloads as your AI needs grow—without locking into proprietary formats.
See watsonx.data in action
Discover how IBM watsonx.data helps organizations streamline complex data estates, eliminate data silos, reduce rising workload costs and support hybrid cloud deployments—all while maximizing ROI from existing data investments.
Use cases
Deploy the power of agentic AI with OpenRAG to deliver context-rich AI outcomes. Build on open-source foundations to launch your RAG solution in minutes, moving quickly from pilot to productivity.
Enable AI applications, analytics and BI tools to work with connected, context‑rich, governed data across databases, data lakes, documents and object storage to yield consistent and explainable results.
Run AI and analytics workloads on the engines best suited to their performance and cost needs, avoiding one‑size‑fits‑all platforms and optimizing price performance as workloads scale.
Case studies
Resolving help desk tickets faster with AI ready data
CrushBank increased its tickets resolved per day by 40%, using watsonx.data as a central, governed store for structured and unstructured data. This enabled their AI system to retrieve accurate, customer-specific information quickly, cutting average resolution time and improving first call resolution rates.
Resources
Explore how a two-pronged approach—data for AI and AI for data—can help you overcome today’s data challenges. It can also ready your data for AI while using AI to make data management smarter.
See how you can make your enterprise data AI-ready and scale it efficiently and safely. Start by building a data foundation based on structured and unstructured data—turning strategy into impact.
A survey of data practitioners by Forrester indicates over 1/4 estimated they lose more than USD 5 million annually due to poor data quality, with 7% reporting losses of USD 25 million or more.¹
Explore articles and insights on why data quality, access and governance matter for AI, demonstrating how AI at scale starts with a strong data foundation.
Footnotes
1. Millions Lost In 2023 Due To Poor Data Quality, Potential For Billions To Be Lost With AI Without Intervention, Forrester, 31 July 2024.