Data scientists are trained to work with large datasets. But the decisions that truly make or break an organisation are rarely the ones with large datasets behind them. They are the high-stakes, one-off decisions made under significant uncertainty - and most data scientists have no framework for handling them.

In this episode, Douglas Hubbard joins Dr Genevieve Hayes to share how combining techniques from statistics, economics and decision theory can help data scientists tackle the problems that matter most.

In this episode, you'll discover:

What Applied Information Economics is and how it works in practice [03:17]
Why organisations are systematically measuring the wrong things [09:23]
How the Lens Model can make expert judgment more reliable than the expert themselves [13:44]
How AI can turbocharge the Applied Information Economics approach [21:10]

Guest Bio

Douglas Hubbard is the founder and president of Hubbard Decision Research and the creator of Applied Information Economics. He has over 35 years’ experience in management consulting focusing on the application of quantitative methods to decision making. He is also the author of How to Measure Anything: Finding the Value of Intangibles in Business and The Failure of Risk Management: Why It’s Broken and How to Fix It.

Links

How to Measure Anything website
Connect with Genevieve on LinkedIn
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