How To Think About Business Intelligence Data (part 1)

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Let’s get into it!

A client came to me with a big set of data and a goal: build a business intelligence layer that she can sell as a SaaS.

The first thing I did was confirm that she actually wanted a BI layer. I work with a lot of non-technical founders who are still learning the tech lingo, so when they come to me with terms like “Business Intelligence” or “Data Infrastructure”, or “Angentic AI“, I always make sure we’re on the same page.

I told her that BI is software that collects, analyzes, and visualizes data so that the end user can do things like make faster decisions, reduce costs, and increase revenue. She agreed.

Next, she gave me my marching orders; she said the first thing we needed to do was clean the data.

I took one look at never-ending rows of Google Sheet before me and disagreed. The first thing we actually needed to do was model the data properly so we could build a data layer that’s easy to work with.

She asked me the difference, and I’d like to share it here. But I’m under NDA. So instead of talking about her IP specifically, we’ll talk about the data within the context of the iced soy milk latte I’m drinking right now.

Specifically, we’ll use some fake supply chain data, from my favorite part: the soy milk. That represents farmers, shipments, processors, manufactures, cafes, inventory, and sales.

See you next week for part 2,

LaToya