Special Note: This analysis was written by Philip Russom, Wayne's colleague in TDWI's research department who covers MDM and data integration. I don’t know about you, but when I read two, similar ...

As agents assume more of the implementation and operational work, organizations will need to reconsider what data engineering is intended to deliver.

Agents are redefining the rules of who works with enterprise data. The question for most organizations is no longer whether to use agents but how to provide them with the right data. That question is ...

AI adoption is soaring—but many organizations struggle to move from pilots to production. This TDWI Virtual Summit explores how to put AI into action across your enterprise by transforming workflows, ...

Financial institutions are accelerating investments in agentic AI, but turning AI innovation into measurable business outcomes requires more than intelligent agents. It requires trusted data, ...

RAG bootcamp for AI engineering — Attend the Bootcamp and earn a certificate of completion Large language models (LLMs) are powerful—but their true value comes from customizing them with your ...

Build the strong data foundation required for AI, BI, and analytics success. Modern data management requires a holistic approach, encompassing data quality, governance, preparation, and integration.

The rapid maturity of AI is creating a sense of urgency to build a trusted data foundation. Data is the competitive advantage every organization needs. Read this report to see how SAP Business Data ...

Enterprises face mounting pressure to make their data estates AI-ready, yet few have a robust foundation. Most organizations operate a patchwork of legacy and modern tools, from ETL pipelines and data ...

The reliability of your organization’s data is the cornerstone upon which every business decision is based and every deal is struck. Without it, organizations struggle to make informed decisions, ...

AI-driven innovation starts with reliable, business-ready data. When organizations treat data as a product, they can empower teams, accelerate AI adoption, streamline data operations, and drive ...