Value Driven Data Science

Osad

• Episode 112: [Value Boost] Lies, Damned Lies and Stakeholders 02.07.2026 16min

  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 BioDerek 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.LinksConnect with Derek on LinkedInDerek's websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 111: Building Your Defences Against AI Misinformation 25.06.2026 26min

  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 BioDerek 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. LinksConnect with Derek on LinkedInDerek's websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 110: [Value Boost] Why You Need Less Data Than You Think 18.06.2026 16min

  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 BioDouglas 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.LinksHow to Measure Anything websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 109: How to Measure Anything and Make Better Decisions 11.06.2026 29min

  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 BioDouglas 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.LinksHow to Measure Anything websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 108: [Value Boost] How to Use AI Without Losing Your Edge 04.06.2026 10min

  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 BioTim 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.LinksConnect with Tim on LinkedInTim's websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 107: Building a Virtual Empire of AI Specialists 28.05.2026 28min

  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 BioTim 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.LinksConnect with Tim on LinkedInTim's websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 106: [Value Boost] When AI Isn't the Answer 21.05.2026 11min

  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 BioSantosh 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.LinksConnect with Santosh on LinkedInProArch websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 105: From AI Idea to Production Reality 14.05.2026 29min

  Organisations today have no shortage of AI ideas. What they lack is the ability to turn those ideas into production-ready systems that deliver real business value.For data scientists trying to get AI projects off the ground, understanding why that gap exists is as important as the technical work itself.In this episode, Santosh Kaveti joins Dr Genevieve Hayes to share what organisations consistently get wrong when embarking on AI initiatives, and what data scientists can do to help get it right.In this episode, you'll discover:Why organisations with great AI ideas still fail to deploy them [02:16]What history tells us about where the current AI wave is heading [09:48]The real cost of bolting AI onto systems that weren't designed for it [13:42]How to forge the cross-functional partnerships that get AI projects off the ground [22:21]Guest BioSantosh 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.LinksConnect with Santosh on LinkedInProArch websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 104: [Value Boost] The Four Zones of AI Productivity for Data Scientists 07.05.2026 13min

  AI can get you to 60% of a finished output in minutes. But getting from 60% to 100% - the part where real insight lives - is where human expertise becomes the deciding factor. And the more expertise you bring, the further AI can take you.In this Value Boost episode, Brent Dykes joins Dr Genevieve Hayes to apply his Four Zones of AI Productivity framework to the insight generation process and explore what it means for data professionals who want to position themselves as strategic advisors.In this episode, you'll discover:The Four Zones of AI Productivity and how they apply to insight generation [01:28]Why AI can help you find an insight but can't generate an actionable one [06:39]Why better AI tools will widen the gap between experts and novices [09:46]How to use AI effectively in your insight generation process [11:44]Guest BioBrent Dykes is the author of Effective Data Storytelling and the founder of AnalyticsHero. He has consulted with some of the world’s most recognised brands, including Microsoft, Sony, Nike and Amazon, and is a regular contributor to Forbes.LinksConnect with Brent on LinkedInEffective Data Storytelling websiteForbes article about the Four Zones of AI ProductivityConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 103: The Art of the Actionable Insight 30.04.2026 30min

  Most data scientists have been in this situation: you spend hours analysing a dataset, return to your stakeholder with your findings, and are met with a polite "that's interesting" - before your work disappears into a drawer, never to be seen again.The problem usually isn't the analysis. It's that interesting observations and genuine insights are not the same thing.In this episode, Brent Dykes joins Dr Genevieve Hayes to share the frameworks behind identifying and communicating insights that actually move organisations to act.In this episode, you'll discover:What makes an insight an insight and why only 5% of findings qualify [03:42]The four dimensions that focus your analysis before you touch the data [11:25]The six criteria for a truly actionable insight [15:06]Why narrative outperforms an executive summary every time [19:14]Guest BioBrent Dykes is the author of Effective Data Storytelling and the founder of AnalyticsHero. He has consulted with some of the world’s most recognised brands, including Microsoft, Sony, Nike and Amazon, and is a regular contributor to Forbes.LinksConnect with Brent on LinkedInEffective Data Storytelling websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 102: [Value Boost] How Giving Away Your Work for Free Can Build Your Authority as a Data Scientist 23.04.2026 12min

  Building authority as a data professional doesn't require a large budget, a publisher, or even a large audience. But it does require a deliberate decision to share your thinking with the world and the patience to let that compound over time.In this Value Boost episode, Prof. Rob Hyndman joins Dr. Genevieve Hayes to share how selectively giving away his work for free helped him become one of the most cited and influential statisticians in the world, and what data professionals at any stage of their career can learn from that approach.In this episode, you'll discover:Why Rob decided to give away his work for free from the start of his career [01:42]How open source software multiplied the impact of his research [05:58]Why authority building is a virtuous cycle and how to start it [09:47]Why starting small is the right move [10:35]Guest BioProf. Rob Hyndman is one of the world’s most influential applied statisticians and a Professor in the Department of Econometrics and Business Statistics at Monash University. He has maintained an active statistical consulting practice for over 40 years, published over 200 research papers, co-authored more than 65 R packages and written five books on time series forecasting. He is also a Fellow of both the Australian Academy of Science and the Academy of Social Sciences in Australia.LinksRob's websiteOtexts' websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 101: Why Traditional Statistics Still Matters in the Age of AI 16.04.2026 28min

  Data scientists today are under pressure to adopt the latest tools - machine learning, LLMs, generative AI. But in the rush to embrace what's new, many are leaving some of the most powerful analytical tools sitting on the shelf. Tools that handle something modern AI largely can't: uncertainty.In this episode, Prof. Rob Hyndman joins Dr. Genevieve Hayes to make the case for why rigorous statistical thinking remains indispensable in the age of AI, and what data scientists are giving up when they abandon it.In this episode, you'll discover:Why throwing data at an LLM is no substitute for building a model that understands the problem [04:27]How combining classical statistics and machine learning can produce better forecasting results than either approach alone [08:22]What data scientists lose when they stop thinking probabilistically - and why it matters for decision making [12:38]Where to start if you want to strengthen your statistical foundations [25:10]Guest BioProf. Rob Hyndman is one of the world’s most influential applied statisticians and a Professor in the Department of Econometrics and Business Statistics at Monash University. He has maintained an active statistical consulting practice for over 40 years, published over 200 research papers, co-authored more than 65 R packages and written five books on time series forecasting. He is also a Fellow of both the Australian Academy of Science and the Academy of Social Sciences in Australia.LinksRob's websiteOtexts' websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 100: What Data Science Value Really Means 09.04.2026 38min

  Over 100 episodes of conversations with world-class practitioners, a few ideas keep surfacing. Technical skill is necessary but never sufficient. The most valuable data professionals aren't the ones who build the best models - they're the ones who know which problems are worth solving. And the gap between those two things is where most data scientists are leaving value on the table.In this milestone episode, Dr. Genevieve Hayes reflects on her career journey and the conversations that helped her arrive at these conclusions, with Matt O'Mara turning the tables to put her in the hot seat.In this episode, you'll discover:From statistician to machine learning advocate and back again - and what that journey revealed [09:49]The crack in the data science skills market where significant value is hiding [18:59]Why knowing which problems to solve matters more than knowing how to solve them [24:53]The top three lessons from 100 conversations on what data science value actually means [33:49]Guest BioMatt O'Mara is the Managing Director of information and insights company Analysis Paralysis and is the founder and Director of i3, which helps organisations use an information lens to realise significant value, increase productivity and achieve business outcomes. He is also an international speaker, facilitator and strategist and is the first and only New Zealander to attain Records and Information Management Practitioners Alliance (RIMPA) Global certified Fellow status.LinksConnect with Matt on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 99: [Value Boost] Preventing ML Bias Before it Becomes a Problem 26.03.2026 10min

  Biased machine learning models don't just produce poor predictions. They can damage reputations, derail projects, and in high-stakes fields like healthcare, potentially cause real harm. Yet many data scientists don't check for bias until it's too late, missing the opportunity to address it at its source.In this Value Boost episode, Serg Masis joins Dr. Genevieve Hayes to share practical techniques for detecting and mitigating bias in machine learning models before they become major problems for you and your stakeholders.You'll discover:The most common bias patterns to watch for [01:32]How to diagnose whether bias exists in your model [04:44]The three levels where bias can be addressed  [07:13]Where to intervene for maximum impact [08:17]Guest BioSerg Masis is the Principal AI Scientist at Syngenta, a leading agricultural company with a mission to improve global food security. He is also the author of Interpretable Machine Learning with Python and co-author of the upcoming DIY AI and Building Responsible AI with Python.LinksSerg's WebsiteConnect with Serg on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 98: Building Trust in AI Through Model Interpretability 19.03.2026 24min

  When your machine learning model makes a decision that affects someone's medical treatment, financial security, or legal rights, "the algorithm said so" isn't good enough. Stakeholders need to understand why models make the decisions they do, and in high-stakes environments, model interpretability becomes the difference between AI adoption and AI rejection.In this episode, Serg Masis joins Dr. Genevieve Hayes to share practical strategies for building interpretable machine learning models that earn stakeholder trust and accelerate AI adoption within your organisation.You'll learn:The crucial distinction between interpretable and explainable models [07:06]Why feature engineering matters more than algorithm choice [14:56]How to use models to improve your data quality [17:59]The underrated technique that builds stakeholder trust  [21:20]Guest BioSerg Masis is the Principal AI Scientist at Syngenta, a leading agricultural company with a mission to improve global food security. He is also the author of Interpretable Machine Learning with Python and co-author of the upcoming DIY AI and Building Responsible AI with Python.LinksSerg's WebsiteConnect with Serg on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 97: [Value Boost] Mathematical Modelling as a Gateway to ML Success 12.03.2026 10min

  Data scientists often jump straight to machine learning when tackling a new problem. But there's a foundational step that can dramatically increase your chances of project success and create more reliable business value. Mathematical modelling from first principles provides a low-cost scaffolding that can make your machine learning work more robust.In this Value Boost episode, Dr. Tim Varelmann joins Dr. Genevieve Hayes to explain how building models from physics principles, like mass and energy conservation, creates a modular foundation that reduces computational costs and makes your work easier to understand.In this episode, we explore:1. What mathematical modelling from first principles actually means [01:20]2. How to build modular models with different resolution levels [04:39]3. When to add machine learning to first principles models [08:18]4. The practical first step to incorporate this approach into your work [09:23]Guest BioDr Tim Varelmann is the founder of Bluebird Optimization and holds a PhD in Mathematical Optimisation. He is also the creator of Effortless Modeling in Python with GAMSPy, the world’s first GAMSPy course.LinksBluebird Optimization WebsiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 96: Making Better Decisions with ML and Optimisation 05.03.2026 26min

  Data scientists use optimisation every day when training machine learning models, without even thinking about it. But there's another type of optimisation - that many data scientists are unaware of - that can be used to dramatically boost the business value of your ML outputs. This second layer transforms predictions into optimal decisions, and it's where the real impact often happens.In this episode, Dr. Tim Varelmann joins Dr. Genevieve Hayes to explain how combining machine learning with decision optimisation creates solutions that go far beyond prediction, helping stakeholders make better decisions in uncertain environments.You'll discover:How decision optimisation differs from ML parameter tuning [02:19]Why combining predictions with optimisation multiplies value [13:36]The mindset shift needed to think in optimisation terms [22:59]How to spot immediate optimisation opportunities in your work [23:42]Guest BioDr Tim Varelmann is the founder of Bluebird Optimization and holds a PhD in Mathematical Optimisation. He is also the creator of Effortless Modeling in Python with GAMSPy, the world’s first GAMSPy course.LinksGet Tim's 3 Step Guide to Add Optimisation to Your Data Science SkillsBluebird Optimization WebsiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 95: [Value Boost] Building Models That Work While Millions Are Watching 26.02.2026 11min

  Building a model for an academic paper is one thing. Building a model that has to work perfectly during the Cricket World Cup with millions watching is something else entirely. There's no room for the kind of errors that might be acceptable in research settings or even standard business applications.In this Value Boost episode, Prof. Steve Stern joins Dr. Genevieve Hayes to share practical lessons from deploying the Duckworth-Lewis-Stern method in high-pressure, real-time environments where mistakes have global consequences.You'll learn:Why model simplicity matters more than you think [02:04]The two types of errors you need to understand [03:21]How to test models for extreme situations [05:50]The balance between confidence and humility [07:37]Guest BioProf. Steve Stern is a Professor of Data Science at Bond University, and is the official custodian of the Duckworth-Lewis-Stern (DLS) cricket scoring system.LinksContact Steve at Bond UniversityConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 94: Creating Global Impact with Data Science 19.02.2026 35min

  For most data scientists, the idea of impacting the world through your work seems impossible. You may be developing technically brilliant solutions within your organisation, but seeing them become industry standards or influence global decisions feels completely out of reach.In this episode, Prof. Steve Stern joins Dr Genevieve Hayes to share how he transformed a mathematical critique of a cricket scoring system into becoming the custodian of the globally adopted Duckworth-Lewis-Stern method - all from an office in Canberra, Australia.This episode reveals:How a single email response changed everything [05:24]Why principles build trust where mathematics can't [13:19]The "error whack-a-mole" problem that destroys credibility [16:00]The real secret to creating work with impact [30:29]Guest BioProf. Steve Stern is a Professor of Data Science at Bond University, and is the official custodian of the Duckworth-Lewis-Stern (DLS) cricket scoring system.LinksContact Steve at Bond UniversityConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
• Episode 93: [Value Boost] What Industry Data Scientists Can Learn from Academic Training 18.12.2025 9min

  While the transition from academia to industry can be brutal for data scientists, academics don't show up in industry empty-handed. They bring powerful transferable skills that many industry-trained data scientists never develop.In this Value Boost episode, Dr. Sayli Javadekar joins Dr. Genevieve Hayes to flip the script on their previous conversation, exploring the valuable skills that academic-trained data scientists bring to industry and how any data scientist can develop these same strengths.You'll learn:The most valuable skills academics bring to industry [01:30]Why the experimental mindset matters so much [03:43]The hidden benefit of extended research projects [04:54]How mentorship can work both ways for mutual benefit [07:06]Guest BioDr Sayli Javadekar is a data scientist at Thoughtworks, with experience at the World Bank and UNAIDS. Before this, she was an Assistant Professor at the University of Bath and holds a PhD in Econometrics from the University of Geneva.LinksConnect with Sayli on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE