Enterprise ArchitectureSep 23, 2026How to Modernize Enterprise Data Architecture in 2026 (New Tools Won’t Do It)Modernizing enterprise data architecture in 2026 isn’t about adopting the new stack — everyone has it. It’s about making your architecture able to change: decoupling, portable data, clear ownership. Why new tools won’t fix a tangled design, and how to sequence the work.Read

Enterprise ArchitectureSep 22, 2026Data Governance for Regulated Industries: The Binder Nobody ReadsEvery regulated company has a data governance policy; few have governance. Why real governance in regulated industries is an architecture decision — definitions at the source, ownership that’s structural, access enforced by the platform — not a binder.Read

AI & Enterprise StrategyJul 20, 2026Your AI Strategy Is Actually a Data StrategyEvery board meeting eventually arrives at the same question: “What’s our AI strategy?” After assessing a global manufacturer, the honest answer was simpler — the data isn’t ready. AI doesn’t create clarity from chaos; it amplifies whatever foundation you already have.Read

Enterprise ArchitectureJun 18, 2026The Illusion of “We Can Do It All”: Lessons from Enterprise Tech StacksVendor lock-in looks efficient — until flexibility disappears, data gets hard to reach, and someone else’s roadmap constrains yours. Why loosely coupled, best-of-breed architecture is what keeps tomorrow possible.Read

Enterprise ArchitectureMay 12, 2026When domain ownership turns into data fragmentationClean domain boundaries erode one rational shortcut at a time. Why domain ownership is necessary but not sufficient — and why enterprise architecture is the accountability that keeps source-of-truth integrity intact.Read

Enterprise ArchitectureMay 1, 2026Why one data model doesn’t fit allRethinking data warehouse and lakehouse design across layers. Why the right answer isn’t a single modeling philosophy — it’s the right model for each layer, workload, and purpose.Read

Enterprise ArchitectureApr 15, 2026Why 2 + 2 is not always 4 in enterprise dataThe math didn’t change — the context did. Why precision, ambiguity, and clarity matter more than the numbers themselves in enterprise data systems.Read

Enterprise ArchitectureApr 9, 2026What decades of working with databases taught me (and why the newest one surprised me most)From Sybase System 4 to MotherDuck — a journey through every generation of database technology, and why the architecture around it matters more than the engine itself.Read

AI & Enterprise StrategyApr 7, 2026Who pays when agentic AI breaks?On a remodeling job, the rule was simple: whoever does the work absorbs the cost of mistakes. In agentic AI, every hallucination and retry is billable to the user. What if liability were a design constraint?Read

LeadershipApr 3, 2026Toxic Work Environments - Part 3 of 3: Preventing Them Before They Take RootThe most important leadership skill in preventing toxic work environments isn’t control — it’s self-awareness. Strong leaders acknowledge what they don’t know, surround themselves with deeper expertise, and prioritize clarity over sounding aligned.Read

Enterprise ArchitectureApr 2, 2026Your data stack works. Can you operate it?Most data stacks fall short in a specific way: they’re built for tasks, not operations. The tools are excellent individually — but operators need end-to-end visibility, lineage, and clear failure paths. What’s missing is a control tower for data ops.Read

LeadershipMar 30, 2026Toxic Work Environments - Part 2 of 3: The SignalsHow toxic work environments first surface — in communication. Vague phrases, polished alignment, and the slow shift from ownership and execution to permission and narrative.Read

LeadershipMar 27, 2026Toxic Work Environments - Part 1 of 3Toxic work environments rarely start with bad intent — they emerge from a gap between responsibility and capability that compounds when left unaddressed. The recurring pattern, and why self-awareness has to scale with the role.Read

AI & Enterprise StrategyFeb 15, 2026SaaS Is (Mostly) DeadFor 15 years the playbook was buy → configure → integrate → renew. Agentic AI just flipped it: 20,000 lines of a data-ops framework rebuilt in a week. When the marginal cost of software approaches zero, the scarce asset becomes domain judgment — not engineering capacity.Read

Field notes from the work

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