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The #1 question to reduce AI costs

High AI costs are often driven by fragmented data that forces models to expend excessive tokens tryi...

The 90-second data model: Why AI-generated data models aren’t a modeling practice

While general-purpose AI can quickly generate plausible data models, these isolated snapshots lack t...

Replacing PowerDesigner: Real migration & modernization success stories

As SAP PowerDesigner approaches its end of life, many leading organizations in highly regulated sect...

25 customer meetings in 3 days: 5 recurring themes about data and AI in 2026

After analyzing 25 customer meetings across various industries, Glenda O’Keefe identifies five rec...

AI won’t fix a broken data foundation: It amplifies it

Data modeling is the essential foundation for successful AI implementation, providing the shared sem...

Lost in translation: Why semantic chaos is the real AI risk

The article warns that "semantic chaos"—the lack of shared business definitions—poses a major ri...

Data product reusability: Cut costs & scale AI with 3 key changes

Many organizations waste significant time and resources by rebuilding existing data assets due to po...

The cost of standing still: what data inaction really risks in an AI-driven market

Resisting change in data management leads to lost competitive advantage because fragmented, ungovern...

The trust paradox: Why better data programs are still rebuilding from scratch 

Recent research reveals that even organizations with mature data programs are rebuilding assets inst...

AI doesn’t have a data problem. It has a navigation problem.

The article argues that enterprise AI success depends less on acquiring more data or larger models a...

From data access to data empowerment: why AI changes the data product equation

Many organizations struggle to derive value from data because their existing datasets are not design...

Snowflake heist: How to crack the code on Snowflake ROI

To maximize Snowflake ROI, organizations must prioritize proactive architectural design and preparat...

The data modeling generation gap: Why teams are rethinking their tooling strategy

While the core principles of data modeling remain unchanged, the landscape has split into two distin...

AI ambition is outpacing data reality and the gap is getting expensive

Many enterprises are struggling to achieve AI success because they rely on inefficient, manual data ...

AI in manufacturing isn’t failing. Your data is.

Many manufacturing AI initiatives are failing because companies are investing in advanced technology...

How a data marketplace unlocks the value of trusted data products

This article argues that a successful AI strategy *requires* a robust data strategy, specifically on...

Your data platform isn’t your data strategy: Why “manage it later” is the lie that breaks AI

This article argues that a data platform (like a data warehouse or lakehouse) is *not* enough for su...

Why data products belong on the P&L — and how to make them pay

This article argues that treating data as a product – with clear ownership, governance, and measur...

Agentic AI needs a foundation you build—not a tool you buy

This article argues that agentic AI is poised to revolutionize data management within months, automa...

Building trusted data products at speed & scale with AI in 2026

This article highlights a shift in data management towards **“data products”**: packaged, curate...

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