Blog
Articles on enterprise migration, integration assessment, platform selection, and the PRISM methodology. Written from 25 years of real engagements.
AI agents are entering enterprise architectures faster than most teams can evaluate them. Here's what they actually are, how they differ from traditional automation, and why data architects need to understand them now.
The opening session of a PRISM assessment reveals more about an organisation's data architecture than weeks of documentation reviews. Here's what actually unfolds when you start looking beyond the surface.
Mid-market organisations face a unique data platform challenge: too complex for simple solutions, too constrained for enterprise platforms. Here's how to navigate the PostgreSQL ecosystem and emerging alternatives without overengineering your architecture.
Most internal API failures aren't caused by code bugs or infrastructure limits. They're caused by upstream systems that suddenly change their calling patterns and overwhelm APIs that were never designed for the new load.
Safari's Intelligent Tracking Prevention creates a hidden gap in GA4 attribution that most marketers never discover. Here's how it breaks your conversion tracking and what data architects need to know.
Most migration overruns stem from the same fundamental scoping errors. Here's how to avoid the traps that turn six-month projects into eighteen-month ordeals.
The right intake questions determine whether your architecture assessment delivers actionable insights or generic recommendations. Here's the PRISM approach to framing assessments that actually guide decisions.
Most AI applications in data architecture are solutions looking for problems. Metadata tagging is different. Here's why the problem is perfectly suited to what AI actually does well.
Your integration architecture is sending warning signs. Here are the five critical signals that indicate it's time for a formal assessment, and why ignoring them costs more than addressing them.
Most architecture assessments are subjective exercises that depend entirely on who's running them. Here's why that's a problem and what repeatable methodology looks like in practice.
After inheriting dozens of integration architectures, I have discovered that teams consistently miss one critical dimension that determines whether their solution will scale. Here is what they are overlooking.