It’s easy to make the case for building a data mesh within your business. By enabling a decentralized approach to data ...

Have you ever invented something, seemingly out of whole cloth, only to do a simple Google search to find out it’s a ...

We often say AI’s mistakes are “by design,” but they’re really not. AI wasn’t built to fail in these specific ways — its ...

Like every other business, your organization must plan for success. In order to do this, the team must have a dependable plan ...

The future of data engineering and AI-driven analytics is both exciting and complex. As technology continues to evolve at a rapid pace, so too does the demand for a workforce capable of harnessing the ...

Principal Enterprise Architect at SAP America, specializing in digital transformation, AI, IoT, and cloud-native architectures. With over a decade of experience, he advises enterprises on optimizing ...

Data policies serve as the guardrails for how organizations manage their most valuable asset: data. Just as communities ...

Digital co-workers are no longer hypothetical. AI-driven agents (“Agentics”) are creeping into every function, every decision ...

Organizations often assume they have data governance under control, but in reality, many are simply reacting to data chaos rather than actively managing it. This isn’t due to negligence or a lack of ...

This column will expand on a Systems Thinking approach to Data Governance and focus on process control. The vendors of myriad governance tools focus on metadata, dictionaries, and quality metrics.

My name is Bill Burkett, and I am a data modeler. I don’t call myself that often and sometimes have misgivings about doing so. I often get the feeling that being a “data modeler,” when considered in ...