Too many good decisions get made on bad numbers.
You pull up a number in a meeting and someone else quietly doesn't believe it. You ask your AI to run an analysis and it hands back a confident, well-formatted, completely wrong answer. That sucks.
Data only matters if you trust it.
I spent a decade leading and building data teams from all different angles — leading the data science work at CompassRed, advocating for open data at Open Data Delaware, and building the team at Tech Impact's Data Innovation Lab. I had the chance to help dozens of organizations, for profit and nonprofit alike, figure out how to use data and AI in their work.
Every one of those experiences taught me the same things.
Data only matters if you trust it. You should feel confident in the quality of the data. Your team should have a shared understanding of your metrics. You should understand the analytics that you're making decisions on.
This was true a decade ago, but then AI made it urgent. A model pointed at untrustworthy numbers doesn't hesitate. It answers fluently with a pretty graph, a paragraph of reasoning, and all kinds of certainty that it shouldn't have. Suddenly you're making mission critical decisions on a terrible data point.
Your work is too important to have untrustworthy data. Let's fix it together.
We'll get your data to be trustworthy, so we can build AI grounded in reality on top of it.