Value Driven Data Science

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

The question haunting every data scientist right now isn't whether AI will change their work, it's whether there will still be a place for them when it does. The answer, according to Tim Dietrich, isn't to compete with AI but to do something far more interesting with it - in his case, building a virtual team of over 100 AI specialists to dramatically expand what he is able to achieve.
In this episode, Tim joins Dr Genevieve Hayes to share the principles and practicalities behind building a virtual AI team, and what data scientists can learn from his experience.
In this episode, you'll discover:
How Tim went from being the "world's most negative person on AI" to building a virtual team of over 100 specialists [03:08]
What a virtual team of AI specialists can do that a human team can't [06:11]
How to build your first AI agent and what to delegate to it [14:19]
Why the human in the middle is still the most important person on the team [17:11]
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

These days, every organisation wants to describe themselves as "AI-first". But in the rush to find opportunities to use AI, it can be easy to forget that AI isn't always the right answer. 
In this Value Boost episode, Santosh Kaveti joins Dr Genevieve Hayes to explore the situations where AI isn't the answer, how to recognise them, and how to have the conversation with stakeholders who are convinced it is.
In this episode, you'll discover:
The types of problems where AI consistently falls short [01:36]
How to recognise when AI is the wrong tool for the job [04:46]
Why most AI conversations eventually lead back to data, people and processes [06:25]
How to push back on an AI solution without losing stakeholder confidence [09:43]
Guest Bio
Santosh Kaveti is the CEO and Founder of ProArch, a technology consultancy that helps enterprises operationalise AI securely and at scale. His expertise spans critical infrastructure industries, including power generation, manufacturing and healthcare, where he has seen firsthand how AI can drive business transformation in complex regulatory environments.
Links
Connect with Santosh on LinkedIn
ProArch website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE