If your stakeholders could take two rocks, bang them together three times, spin around, and make a better decision, they would.

That’s not a criticism. It’s just the truth about what stakeholders actually want.

Your stakeholders do not wake up in the morning hoping that a new predictive model will be waiting in their inbox when they arrive at work. And they do not lie awake at night, wishing that the following day their data scientists will present them with more accurate forecasts.

What they do want is to make better decisions, and they don’t particularly care how they get there.

The problem is that the data science profession has pretty much spent the past decade optimising for better predictions, as if a more accurate forecast is an end in itself.

It isn’t.

A forecast is just information. What happens to that information is where the real value either gets created or quietly disappears.

This is the gap that decision science was built to close. And it’s the gap that Adam DeJans Jr, co-author of The Decision Factory, has spent his career working in.

In the latest episode of Value Driven Data Science, Adam joins me to share what it actually looks like when an organisation stops optimising for better predictions and starts building systems that enable better decisions.

You’ll discover:

1. What separates a decision factory from a machine learning pipeline [02:11]
2. Why uncertainty is an input into good decision making [10:51]
3. How to test a decision policy before it costs you real money [12:11]
4. Why giving stakeholders options beats giving them the optimal solution [18:48]

Stakeholders don’t want better models. They want to make better decisions. This episode is about how you can bridge that gap.

Listen now on Apple Podcasts or Spotify, or click the link below:

​Episode 123: How to Build a Decision Factory Inside Your Organisation​

Talk again soon,

Dr Genevieve Hayes