I asked three experts about their work and never said the word “ontology.” An LLM turned each conversation into an ontology model anyway, and all three made sense. The hardest part had nothing to do with ontology
Data analysis is becoming a skill, not a job title. With AI, business users can get answers without SQL or an analyst in the loop. But someone still has to make sure the data is worth analyzing: clean, fresh and trusted. Meet the Data Owner
Payback is one of the most underrated metrics every CEO should know. It can be difficult to define the right target values for CAC, ROI, CPA, or ROAS. But every stakeholder knows how long they’re willing to wait to get their money back
Data modeling has spent decades being the industry’s favorite “best practice” that nobody had time for. Ironically, the rise of AI is making it one of the most valuable business assets. Here’s the simple framework I use to bridge business objectives and analytics.
What if you could ask your personal finances any question—and always know exactly where the answer came from? This article shows how I built a governed AI-powered finance system that turns raw transactions into trustworthy insights and better financial decisions
What separates meaningful data from nonsense? Just as only an infinitesimal fraction of all possible Rubik’s Cube color combinations represent a real cube, only a well-defined data model ensures that every query produces results that actually make sense
Your biggest competitive advantage may be one hire away. I believe hiring a single great data analyst is often the highest-ROI investment a growing company can make