Daniel Shapiro, PhD
Aug 8, 2017 · 2 min read

Hi Chris,

To avoid problems, we try and map out what the data inputs are, and make sure the data is real. Some clients assume they have data, but in the end it turns out they don’t. So we map that out up front. Next, to identify the outcomes, we write up a really clear statement of work (SOW), that covers all the eventualities. Mathieu Lemay is writing one up right now.

The second part of your question was about how the client knows what they need. Often they don’t, but in this imaginary case, that I know a fair bit about BI needs *cough* NDA *cough*… analysts want to grab and sort data to make decisions. This is called a mixed initiative system. I have written a bunch on this in academia. See my last 3 publications on my LinkedIn. Basically the analyst wants the system to do lots of the thinking and the AI should work WITH the analyst to help sort the data. So imagine a big table or wall with documents like Tom Cruise in Minority report. That’s an example of humans working with AI to organize and sift through data.

This is what BI data analysts at an FI want. They have to write some random report and they pull together a pile of “stuff”. They want to sort through heaps of data really fast. They know they want it. They may not get AI, but they get their needs really well.

Hey, if there are any readers who have seen this exact requirement please chime in.

Anyhow, thanks for your comment Chris Butler. Pricing AI is not so different from pricing plumbing. You know what you can see and what you can’t, and you get the real picture when you rip open some walls.

Cheers!

-Daniel

    Daniel Shapiro, PhD

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