Why I built TrueState

I spent my career watching analytics teams fail — and almost never because the people were not good enough.

They had Snowflake or BigQuery. They had BI. They had smart analysts drowning in requests. What they did not have was a system designed to work as one: vendors who each owned a slice of the stack and walked away from integration, strategy, and the hard analytical work that actually moves decisions. Executives saw “analytics” as a category and assumed the team could deliver magic. The team saw a pile of tools and tickets. Everyone was frustrated; almost everyone blamed the wrong layer of the problem.

I watched good analysts take the hit for outcomes they could not control — roadmaps that were never really funded, data that never quite lined up, and predictive work that stayed permanently “next quarter” because nobody had capacity to do real data science on top of keeping the lights on.

TrueState is not a market slide I wanted to pitch. It is the thing I wished had existed for those teams: a platform that does advanced analytics with the reliability bar set for people who are not full-time ML engineers, sitting on the warehouse they already have — and, when the organisation genuinely does not know what to build first, a strategy engagement that maps decisions and data before another generic roadmap gets approved and shelved.

My background is McKinsey — not as a badge, but as context: I care about methodology that survives contact with real executives and real messiness. The rigour in TrueState is the rigour you need when outputs change decisions and you cannot hire a senior data scientist for every question.

If this resonates, the honest next steps are simple: see the platform, apply for a Discovery session if you are not sure what to build yet, or read more from me on the blog.