What a "Data Culture" means, Data Modeling best practices

Small Data is having a big moment!!!! I covered Small Data SF by MotherDuck in person and sat down with Brittany Bafandeh, CEO at Data Culture. We talked about the real blockers to impact and how teams can move faster with the data they already have.

Here is what we got into:

When it is not a data problem -

Brittany walked through a case where dashboards, pipelines, and new tools were not the fix. The real issue was slow decisions and unclear ownership. Once they set decision rights and clear KPIs, outcomes changed without buying more tech.

Do you have a data culture or just tools -

As a consultant, she looks for simple signals. Are decisions tied to metrics. Do teams review outcomes every week. Are definitions shared. If the answer is no, that is an infrastructure shell without culture inside it.

Consultant vs in house -

Consultants can push for focus and bring patterns from many teams. In house leaders win by staying close to the business and building habits that last. The best results happen when both mindsets meet.

One modeling habit that breaks things -

Teams jump to complex models too soon. Brittany’s fix is to model around decisions first. Keep names, metrics, and grain simple. Let complexity come only when the use case proves it.

Why this matters

Most teams do not need more tools to get value. They need faster decisions, shared language, and simple models that match the business. Small data, used well, beats big stacks used poorly!!!!

I am publishing the full interview next. If you care about real outcomes with the stack you already have, you will like this one.

#data #ai #motherduck #smalldatasf #theravitshow