While general-purpose AI can quickly generate plausible data models, these isolated snapshots lack the governance, connectivity to live databases, and security required for enterpr…
As SAP PowerDesigner approaches its end of life, many leading organizations in highly regulated sectors like finance and aviation are migrating to Quest Software’s erwin Data Model…
After analyzing 25 customer meetings across various industries, Glenda O’Keefe identifies five recurring themes regarding the challenges of scaling AI and data products. While tech…
Data modeling is the essential foundation for successful AI implementation, providing the shared semantic definitions required for effective data and AI governance. Without this fo…
The article warns that "semantic chaos"—the lack of shared business definitions—poses a major risk to enterprise AI by enabling models to confidently scale errors and drive costly …
Many organizations waste significant time and resources by rebuilding existing data assets due to poor discoverability and a lack of trust in current datasets. To break this cycle,…
Resisting change in data management leads to lost competitive advantage because fragmented, ungoverned data prevents organizations from reacting quickly to market shifts. To remain…
Recent research reveals that even organizations with mature data programs are rebuilding assets instead of reusing them due to a fundamental lack of trust in existing data quality …
The article argues that enterprise AI success depends less on acquiring more data or larger models and more on implementing a "navigation layer" of business context to ensure data …
Many organizations struggle to derive value from data because their existing datasets are not designed for practical use, leading to inefficient, manual preparation processes. To b…
To maximize Snowflake ROI, organizations must prioritize proactive architectural design and preparation rather than relying on reactive cost-cutting. By implementing precise data m…
While the core principles of data modeling remain unchanged, the landscape has split into two distinct needs: enterprise-level precision for governance and modern stack collaborati…