Value Driven Data Science

AI misinformation is a new problem. Misleading data is not. Long before anyone had heard of a hallucination, organisations were making bad decisions based on cherry-picked statistics, misunderstood averages, and numbers that confirmed what decision-makers already wanted to believe.
In this Value Boost episode, Derek Gibson joins Dr Genevieve Hayes to explore how data professionals can help their stakeholders become better data sceptics and avoid being duped by misleading data long before it ever reaches an AI.
In this episode, you'll discover:
1. The timeless data traps that catch even experienced decision makers [01:56]
2. How to arm your stakeholders with the right questions to push back on data [07:57]
3. Why confirmation bias is the most dangerous data vulnerability in any organisation [09:20]
4. What it means when an analytics team is asked to confirm a decision rather than inform one [13:24]
Guest Bio
Derek Gibson is a decision scientist, analytics educator, and has recently wrapped up his long career in financial services at Wells Fargo. He serves on the Wake Forest University MS Business Analytics Advisory Board. He is also a co-author of Data Duped: How to Avoid Being Hoodwinked by Misinformation and author of the upcoming Data, AI, and the Noise: Searching for Truth in Information and Algorithms.
Links
Connect with Derek on LinkedIn
Derek's website
Connect with Genevieve on LinkedIn
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AI doesn't lie - at least, not intentionally. It just sounds completely confident while filling in the gaps with whatever seems most plausible. And in a world where AI outputs are increasingly being used to inform high-stakes decisions, the ability to spot what's wrong, before it reaches a stakeholder, is becoming one of the most important skills a data professional can have.
In this episode, Derek Gibson joins Dr Genevieve Hayes to share practical strategies for identifying unreliable AI outputs and building the defences necessary to keep AI-generated misinformation from reaching your stakeholders.
In this episode, you'll discover:
Why AI is not a truth tool and what that means for how you use it [03:21]
The red flags that signal an AI output shouldn't be trusted [12:21]
A simple prompting habit you can develop to reduce AI mistakes [16:13]
Why the skill of verifying AI outputs is one you need to build yourself [24:25]
Guest Bio
Derek Gibson is a decision scientist, analytics educator, and has recently wrapped up his long career in financial services at Wells Fargo. He serves on the Wake Forest University MS Business Analytics Advisory Board. He is also a co-author of Data Duped: How to Avoid Being Hoodwinked by Misinformation and author of the upcoming Data, AI, and the Noise: Searching for Truth in Information and Algorithms.
Links
Connect with Derek on LinkedIn
Derek's website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

In high-stakes decision-making, waiting for more data is often not an option. Yet many data scientists assume that without a large dataset, meaningful analysis is impossible. The good news is that rigorous, quantitative analysis is possible with far less data than most data scientists realise - in some cases with just a single datapoint.
In this Value Boost episode, Douglas Hubbard joins Dr Genevieve Hayes to share practical techniques from How to Measure Anything that data scientists can start using right now to support high-stakes decisions when observations are scarce and every data point counts.
In this episode, you'll learn:
Why a single observation reveals more than you think [01:58]
How Laplace's Rule of Succession lets you estimate probabilities from tiny samples [08:25]
The Rule of Five and what it reveals about small sample statistics [12:08]
The simplest and most overlooked technique for reducing measurement uncertainty [14:07]
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
Be among the first to hear about the release of each new podcast episode by signing up HERE

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
Be among the first to hear about the release of each new podcast episode by signing up HERE

AI has the potential to dramatically expand what data scientists can do. But used without care, it also has the potential to quietly erode the expertise that makes them valuable in the first place.
In this Value Boost episode, Tim Dietrich joins Dr Genevieve Hayes to explore how to stay on the right side of that line and what mindful AI use actually looks like in practice.
In this episode, you'll discover:
Why looking for problems to solve with AI is a warning sign [02:05]
What happens when you use AI before you have the expertise to direct it [05:51]
Why your AI interactions should be conversations rather than one-way requests [06:54]
How to use AI to become a better thinker not just a faster worker [08:40]
Guest Bio
Tim Dietrich is an independent software developer with over 25 years’ experience building business software for organisations ranging from startups to Fortune 50 companies, including Siemens and the Library of Congress. Recently, he has become known for building a virtual team of AI specialists that allows him to operate with the output and breadth of a small firm, while remaining a team of one.
Links
Connect with Tim on LinkedIn
Tim's website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE