Writing
Deep-dives on software architecture and the way source code is structured — written to be understood by beginners, yet useful to teams shipping at scale. Diagrams, real examples, no hand-waving.
Microservices can create real team autonomy, but they move cost into operations, data consistency, observability, testing, and coordination. This piece asks when that cost is worth paying and when a modular monolith is the kinder architecture.
AI coding tools can make a team produce code faster than it can understand, review, debug, and safely operate that code. This article explains cognitive debt, how it differs from technical debt and intent debt, why agentic coding makes it more visible, and the practical controls teams can use: smaller batches, better PR rationale, human-owned invariants, disposable prototypes, and AI used to repay understanding instead of only generating more work.
LLMs can feel like they understand us because they handle patterns, context, and language-shaped reasoning well. This explainer separates useful machine understanding from human understanding so teams can verify, constrain, and use AI with better judgment.
A practical introduction to Lean Business Analysis: why BA starts with value, how needs assessment protects teams from solving the wrong problem, and how a business case keeps decisions grounded without turning analysis into ceremony.
A practical guide to Lean BA planning: stakeholder analysis, predictive versus adaptive BA rhythms, elicitation planning, and the difference between collecting stated requests and uncovering the need behind them.
A calm, practical guide to requirements modeling: scope, process, rule, data, and interface models, with examples of how diagrams and structured descriptions help teams reason together before building.
A practical reflection on the later half of Business Analysis: requirement quality, prioritization, traceability, change control, UAT, rollout choices, transition, and evaluating whether the delivered solution actually worked.
A practical reflection on Data: Harness Your Numbers to Go from Uncertain to Unstoppable, focused on how the EOS Data Component helps leaders move from anxious guesswork to clearer operating visibility. The article looks at data as a clean windshield: a way to test intuition, avoid hopeful fiction, connect Visionary and Integrator work, and turn numbers into leadership freedom rather than pressure.
A practical reflection on the Scorecard idea in Data and EOS: why a company does not need dozens of KPIs to understand its operating health, but a small set of measurables that are clear, consistent, predictive, and owned. The article looks at the Scorecard as a check-engine light, the difference between P&L and leading indicators, weekly cadence, reverse-engineering goals, range goals, and the traps of measuring too much or measuring what is easy instead of what matters.
