TDWI instructor Chris Adamson defines dimensional data modeling as “the nexus of a holistic approach to managing business intelligence, analytics, and governance programs. Used at a program level to ...

Download this TDWI Playbook today to learn how you can advance your data science with AI-infused tools while minimizing risk ...

This TDWI Data Points report focuses on how enterprises are modernizing their data strategies and architectures to meet the ...

The Data Products Blueprint: 10 Key Attributes for Faster Business Impact 6.25.2025 Many organizations are still struggling to turn chaotic data into real business outcomes. If that sounds familiar, ...

This TDWI Best Practices Report identifies current challenges organizations are facing with data strategy and management, as well as shared modernization priorities for achieving data-driven business ...

A recent webinar focused on best practices and challenges for retrieval-augmented generation (RAG) and agentic AI. Download today to read the highlights of this discussion!

Data Quality at Scale Begins with Data Observability 2.28.2025 Data is valuable—bad data is expensive. As organizations rely more on data to fuel decisions and power large language models, the cost of ...

TDWI research shows that while organizations cite generative AI as a top priority, most are struggling with implementation and lagging in maturity. Join us for an insightful TDWI Summit event where ...

What’s Ahead in Generative AI in 2025? (Part One) Predictions for AI in the coming year. By James G. Kobielus The generative artificial intelligence (AI) boom will continue unabated over the next ...

The path to AI-driven innovation is paved with data readiness. These three indicators provide a quick data readiness assessment.

Exploring the transformative power of data-driven decision-making and seven steps to help you implement prescriptive analytics.

Read the new State of Data Quality report today to learn about the current state of data quality management, the top challenges organizations face, and best practices for improving your data quality ...