Value Driven Data Science
Value Driven Data Science is a masterclass where data professionals learn how to become strategic experts. Each week, Dr Genevieve Hayes speaks with world-class data practitioners who have mastered strategic positioning, built genuine authority, and transformed their expertise into organisational influence. You'll learn how they create value by helping stakeholders make better decisions and solve real business problems with data - not just by running analyses. If you're a data professional ready to stop being a technical executor and become a strategic expert, this masterclass is for you.
Episodes
Episode 112: [Value Boost] Lies, Damned Lies and Stakeholders
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
Episode 111: Building Your Defences Against AI Misinformation
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 episo
Episode 110: [Value Boost] Why You Need Less Data Than You Think
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 D
Episode 109: How to Measure Anything and Make Better Decisions
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
Episode 108: [Value Boost] How to Use AI Without Losing Your Edge
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,
Episode 107: Building a Virtual Empire of AI Specialists
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 t
Episode 106: [Value Boost] When AI Isn't the Answer
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
Episode 105: From AI Idea to Production Reality
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 co
Episode 104: [Value Boost] The Four Zones of AI Productivity for Data Scientists
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
Episode 103: The Art of the Actionable Insight
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
Episode 102: [Value Boost] How Giving Away Your Work for Free Can Build Your Authority as a Data Scientist
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
Episode 101: Why Traditional Statistics Still Matters in the Age of AI
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 st
Episode 100: What Data Science Value Really Means
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 thi
Episode 99: [Value Boost] Preventing ML Bias Before it Becomes a Problem
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 t
Episode 98: Building Trust in AI Through Model Interpretability
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 join
Episode 97: [Value Boost] Mathematical Modelling as a Gateway to ML Success
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 Var
Episode 96: Making Better Decisions with ML and Optimisation
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
Episode 95: [Value Boost] Building Models That Work While Millions Are Watching
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 practica
Episode 94: Creating Global Impact with Data Science
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
Episode 93: [Value Boost] What Industry Data Scientists Can Learn from Academic Training
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 a
Episode 92: Making the Academia to Industry Leap in Data Science
Making the leap from academia to industry isn't just another career change - it involves a complete shift in the way you work. Data scientists transitioning from academia face a brutal learning curve that can leave them feeling unprepared despite years of advanced training.In this episode, Dr. Sayli Javadekar joins Dr. Genevieve Hayes to share her recent journey from a tenure-track academ
Episode 91: [Value Boost] How Your Hobbies Can Supercharge Your Data Science Career
Activities outside of data science can strengthen the very skills data scientists need for their careers in surprising ways. From improving stakeholder communication to learning how to work with resistance rather than against it, hobbies and interests often teach lessons that directly translate to professional effectiveness.In this Value Boost episode, Colin Priest joins Dr. Genevieve Hay
Episode 90: Using LLMs to Become a More Effective Data Scientist
When most data scientists think about using LLMs and generative AI, the first thing that springs to mind is writing code faster. While that's certainly useful, if it's the only application you're exploring, you're missing some of the most powerful opportunities to enhance your effectiveness as a data scientist.In this episode, Colin Priest joins Dr. Genevieve Hayes to explore advanced LLM
Episode 89: [Value Boost] LinkedIn Strategies for Boosting Your Data Science Career
LinkedIn has become a powerful career tool for data scientists willing to invest the time. Regular posting can lead to unexpected work opportunities, reconnections with former colleagues, and valuable networking with professionals worldwide. But making the leap from occasional posting to consistent content creation can feel overwhelming.In this Value Boost episode, Sarah Burnett joins Dr.
Episode 88: Building a Data Science Career After Unexpected Job Loss
There was once a time, when data science was still in its infancy, when demonstrating any attempt to learn Python or machine learning was enough to secure a job interview. The demand for data scientists massively outweighed supply. Ten years later, however, the job market has dramatically shifted - and many data scientists who unexpectedly find themselves out of work face a truly overwhel
Episode 87: [Value Boost] How Your Weirdness Could Be Your Data Science Superpower
When most data scientists think about their competitive edge, they focus solely on what goes on their resume - education, work experience, and technical skills. But what if the things that truly make you irreplaceable go far deeper than your LinkedIn profile? Your family background, cultural influences, communication quirks, and even the hobbies that make you nerd out all contribute to wh
Episode 86: Why Every Data Scientist Is Already Running a Business
Every data scientist is running their own business - it's just that most of those businesses are solo operations with one client: their employer. Unfortunately, most data scientists don't realise this and too many fall into the trap of believing their employer will magically take care of their career development, putting them on the right projects and ensuring they get proper training. Th
Episode 85: [Value Boost] The Office Politics Survival Guide for Data Science Experiments
Here's something that data science courses don't prepare you for: even your most brilliant analysis can fail if you can't navigate the human side of your organisation. And office politics becomes especially tricky when you're running experiments. You're essentially asking people to place bets on their ideas - and then potentially delivering the news that their bet didn't "win".In this Val
Episode 84: The 7-Step Checklist for Creating Business Impact Through Product Analytics
When working with data, it can be easy to fall into the trap of believing that your dataset represents nothing more than numbers on a page. However, behind every data point is a human story - people clicking through websites, abandoning shopping carts, or binge-watching Netflix shows. And in our app-driven world, understanding these human behaviours has become absolutely critical - for bu
Episode 83: [Value Boost] How to Gamify Data Science Requirements Gathering for Better Results
Stakeholder requirement gathering is often one of the most dreaded parts of data science projects - dry, tedious sessions where conflicting voices talk past each other and senior executives dominate the conversation. Yet without proper requirements, data science projects are doomed to fail due to solving the wrong problems or missing critical business needs.In this Value Boost episode, Da
Episode 82: Why You Should Start Your Data Projects with Pictures Not Data
Most data scientists follow the same predictable process: gather requirements, collect data, build models, and only at the very end create visualisations to communicate results. This traditional approach seems logical, but what if it's actually working against us? In this episode, David Cohen joins Dr. Genevieve Hayes to reveal how flipping the script on data visualisation - moving it to
Episode 81: [Value Boost] How to Frame Data Problems Like a Decision Scientist
Data science training programs often jump straight into technical methods without teaching one of the most critical skills for project success - problem framing. Without proper framing, data science projects are doomed to fail, right from the start, as data scientists find themselves solving the wrong problems or building models that don't address real business decisions.In this Value Boo
Episode 80: Why Decision Scientists Succeed Where Data Scientists Fail
Most data scientists have never heard of decision science, yet this discipline - which dates back to WWII - may hold the key to solving one of data science's biggest problems: the 87% project failure rate. While data scientists excel at building models that predict outcomes, decision scientists focus on modelling the actual business decisions that need to be made - a subtle but crucial di
Episode 79: [Value Boost] The Win Win Data Product Validation Strategy
One of the biggest risks for independent data professionals is spending months or years developing a product or service that nobody wants to buy. The graveyard of failed data science projects is filled with technically brilliant solutions that solved problems no one actually had, leaving their creators with empty bank accounts and bruised egos.In this Value Boost episode, Daniel Bourke jo
Episode 78: From Machine Learning Engineer to Independent Data Professional Before 30
The traditional career path of climbing the corporate ladder no longer appeals to many data scientists - who crave freedom and ownership of their work. Yet the leap from employment to independence can feel risky and uncertain, especially without a clear roadmap for success.In this episode, Daniel Bourke joins Dr. Genevieve Hayes to share his journey from machine learning engineer to succe
Episode 77: [Value Boost] Why Your Data Team Needs a Book Club
The right book at the right time can completely transform your career trajectory, but many data professionals struggle to find resources that directly address their unique challenges of bridging technical expertise with business impact. While technical skills courses are abundant, guidance on becoming a strategic data leader remains scarce.In this Value Boost episode, Kashif Zahoor joins
Episode 76: The 3 Step Framework That Transforms Data Order-Takers to Strategic Business Partners
Many data scientists begin their careers expecting to influence strategic decisions, only to find themselves trapped as "data order takers" - endlessly running reports and responding to requests without understanding their business impact. This reactive approach limits career growth and earning potential, keeping even experienced professionals from reaching their strategic potential.In th
Episode 75: [Value Boost] The Psychology Hack That Gets Your Data Insights Heard
Even the most compelling data presentation can fail if it runs headfirst into your stakeholders' cognitive blind spots. Decision makers who claim to be "data-driven" often unconsciously filter information through their existing beliefs, leaving brilliant insights ignored or dismissed.In this Value Boost episode, Dr. Russell Walker joins Dr. Genevieve Hayes to reveal practical techniques f
Episode 74: How Competitive Debating Frameworks Can Revolutionise Your Data Science Career
Data storytelling might make your findings memorable, but persuasion is what gets your recommendations implemented. Many data scientists have mastered communication and storytelling, yet still watch their brilliant insights gather dust because they haven't learned the crucial difference between informing stakeholders and persuading them to act.In this episode, Dr. Russell Walker joins Dr.
Episode 73: [Value Boost] How to Trust Social Media Data When You Can't Trust Social Media
Social media data drives countless business decisions, but up to 40% of social media engagement may be artificial or manipulated by bots. For data scientists accustomed to cleaning messy data, deliberately manipulated data presents an entirely different challenge that requires specialized detection techniques.In this Value Boost episode, Tim O'Hearn joins Dr. Genevieve Hayes to reveal pra
Episode 72: The Social Media Hacker's Guide to Better Data Science
Social media algorithms silently shape what billions of people see and how they interact online. While most data scientists work to optimize business value within platform rules, there's valuable knowledge to be gained from understanding how these systems can be exploited - knowledge that can make ethical data scientists better at their jobs.In this episode, Tim O'Hearn joins Dr. Geneviev
Episode 71: [Value Boost] Why Most Dashboards Fail and How to Fix Yours
Most dashboards and reports get ignored despite all the technical expertise that goes into creating them. The reason isn't technical limitations or poor data quality - it's that they fail to deliver value to the people who are supposed to use them.In this Value Boost episode, Nicholas Kelly joins Dr. Genevieve Hayes to reveal proven strategies for increasing dashboard adoption and showcas
Episode 70: How to Interpret Data Like a Pro in the Age of AI
Despite unprecedented data abundance and widespread data science education, even experienced data professionals still struggle to interpret data effectively. They draw wrong conclusions, miss critical insights, or fail to communicate findings in actionable ways.In this episode, Nicholas Kelly joins Dr. Genevieve Hayes to tackle the critical challenge of data interpretation - revealing why
Episode 69: [Value Boost] The Value Proposition Framework Every Data Scientist Needs to Master
Can you clearly articulate what makes your data science work valuable - both to yourself and to your key stakeholders? Without this clarity, you'll struggle to stay focused and convince others of your worth.In this Value Boost episode, Dr. Peter Prevos joins Dr. Genevieve Hayes to share how creating a compelling value proposition transformed his data team from report writers to strategic
Episode 68: How to Market Your Data Science Skills Internally with the Insights-as-a-Service Approach
Internal data science teams face a unique challenge - they're providing an invisible service that only gets noticed when something goes wrong. This puts data scientists in the awkward position of having to market themselves within their own organization, without any marketing training.In this episode, Dr. Peter Prevos joins Dr. Genevieve Hayes to share how he applied his PhD research in s
Episode 67: [Value Boost] The 3 Level Hierarchy That Protects Your Data Science Credibility
When deadlines loom, it's easy for data scientists to fall into the trap of cutting corners and bending analyses to deliver what stakeholders want. But what if a simple framework could help you maintain quality under pressure while preserving your professional integrity?In this Value Boost episode, Dr. Brian Godsey joins Dr. Genevieve Hayes to reveal his powerful "Knowledge first, Technol
Episode 66: How to Think Like a Data Scientist (Even While AI Does All the Work)
The data science world has always been obsessed with tools and techniques - a fixation that's only intensified in the era of generative AI. Yet even as ChatGPT and similar technologies transform the landscape, the fundamental challenge remains the same - turning technical capabilities into business results requires a process most data scientists never learned.In this episode, Dr. Brian Go
Episode 65: [Value Boost] How to Upgrade Your Data Visuals Without Design Training
Even the most brilliant data analysis can fall flat when presented with poor visualisations. Many data scientists simply use default charts from their analysis software, missing the opportunity to create compelling visuals that drive understanding and decision-making.In this Value Boost episode, Bill Shander joins Dr. Genevieve Hayes to share the design principles that can transform techn
Episode 64: Stop Being a Data Waiter and Start Stakeholder Whispering
Data scientists can often find themselves in a frustrating cycle - meticulously executing stakeholder requests only to discover what they delivered isn't what was actually needed. The disconnect between what stakeholders ask for and what truly solves their problems can derail projects and limit advancement of your career.In this episode, Bill Shander joins Dr. Genevieve Hayes to reveal th
Episode 63: [Value Boost] 3 Affordable AI Tools Every Data Scientist Needs
Looking for powerful AI tools that can dramatically boost your impact, regardless of the size of the businesses you serve? You don't need an enterprise-size budget to transform your work and create massive value for your stakeholders.In this Value Boost episode, Heidi Araya joins Dr Genevieve Hayes to reveal three high-impact, low-cost AI tools that deliver exceptional ROI for both your d
Episode 62: The Data Science Gold Mine Hidden in Small Business AI Solutions
While most data scientists chase after scraps at the big business table, a hidden gold mine sits completely ignored. Small businesses are desperate for AI solutions but can't get help because everyone thinks they're "too small."The truth? These overlooked clients - representing a staggering 99.8% of all businesses - are willing to pay real money for simple AI implementations that deliver
Episode 61: [Value Boost] The 90-10 Rule for Transforming Data Science Impact
Would you believe that sharing a conversation in the lunch room could be more valuable to your data science career than spending countless hours behind a computer, perfecting algorithms and models? It's a radical idea, but it's exactly the kind of thinking that transforms good data scientists into exceptional ones.In this Value Boost episode, AI strategist Gregory Lewandowski joins Dr Gen
Episode 60: 5 Executive Priorities That Transform Data Science Results into Business Value
If you want to succeed in data science, you need to create business value. But what does business value actually mean to the executives with the power to make or break your data science initiative?In this episode, AI strategist Gregory Lewandowski joins Dr Genevieve Hayes to share the five executive priorities he discovered while leading analytics for major enterprises - and explain why t
Episode 59: [Value Boost] How Data Scientists Can Get in the AI Room Where It Happens
Everyone’s talking about AI, but the real opportunities for data scientists come from being in the room where key AI decisions are made.In this Value Boost episode, technology leader Andrei Oprisan joins Dr Genevieve Hayes to share a specific, proven strategy for leveraging the current AI boom and becoming your organisation’s go-to AI expert.This episode explains:How to build a systematic
Episode 58: Why Great Data Scientists Ask ‘Why?’ (And How It Can Transform Your Career)
Curiosity may have killed the cat, but for data scientists, it can open doors to leadership opportunities.In this episode, technology leader Andrei Oprisan joins Dr Genevieve Hayes to share how his habit of asking deeper questions about the business transformed him from software engineer #30 at Wayfair to a seasoned technology executive and MIT Sloan MBA candidate.You’ll discover:The crit
Episode 57: [Value Boost] 3 Game-Changing Questions to Save Your Data Science Presentations From Falling Flat
Every data scientist knows the sinking feeling: you’ve done brilliant technical work, but your presentation falls flat with stakeholders.In this Value Boost episode, communications expert Lauren Lang and data analyst Dr Matt Hoffman join Dr Genevieve Hayes to share their go-to pre-presentation checklist to ensure that sinking feeling never happens again.You’ll walk away knowing:The critic
Episode 56: How a Data Scientist and a Content Expert Turned Disappointing Results into Viral Research
It’s known as the “last mile problem” of data science and you’ve probably already encountered it in your career – the results of your sophisticated analysis mean nothing if you can’t get business adoption.In this episode, data analyst Dr Matt Hoffman and content expert Lauren Lang join Dr Genevieve Hayes to share how they cracked the “last mile problem” by teaming up to pool their experti
Episode 55: [Value Boost] Why Data Scientists are Focus-Poor (and the Software Developer’s Solution to Fix It)
Have you ever noticed that software developers are frequently more productive than data scientists? The reason has nothing to do with coding ability.Software developers have known for decades that the real key to productivity lies somewhere else.In this quick Value Boost episode, software developer turned CEO Ben Johnson joins Dr Genevieve Hayes to discuss the focus management techniques
Episode 54: The Hidden Productivity Killer Most Data Scientists Miss
Why do some data scientists produce results at a rate 10X that of their peers?Many data scientists believe that better technologies and faster tools are the key to accelerating their impact. But the highest-performing data scientists often succeed through a different approach entirely.In this episode, Ben Johnson joins Dr Genevieve Hayes to discuss how productivity acts as a hidden multip
Episode 53: A Wake-Up Call from 3 Tech Leaders on Why You’re Failing as a Data Scientist
Are your data science projects failing to deliver real business value?What if the problem isn’t the technology or the organization, but your approach as a data scientist?With only 11% of data science models making it to deployment and close to 85% of big data projects failing, something clearly isn’t working.In this episode, three globally recognised analytics leaders, Bill Schmarzo, Mark
Episode 52: Automating the Automators – How AI and ML are Transforming Data Teams
In many organisations, data scientists and data engineers exist as support staff. Data engineers are there to make data accessible to data scientists and data analysts, and data scientists are there to make use of that data to support the rest of the business.But in helping everyone else in the business, data professionals can often forget to help themselves.However, just as AI and machin
Episode 51: Data Storytelling in Virtual Reality
In the 2002 movie, Minority Report, the future of data interaction is depicted as Tom Cruise standing in front of a computer monitor and literally grabbing data points with his hands. Data interaction is shown to be as easy as interacting with physical objects in the real world.This vision of a world where data is accessible to all was considered to be science fiction when Minority Report
Episode 50: Addressing the Unknown Unknowns in Data-Driven Decision Making
When it comes to awareness and understanding, what we know and don’t know can be split into four categories: known knowns; unknown knowns; known unknowns; and unknown unknowns. And to quote former US Secretary of Defence Donald Rumsfeld: “If one looks throughout the history of our country and other free countries, it is the latter category that tends to be the difficult ones.”When Rumsfel
Episode 49: AI-Generated Advertising and the Future of Content Creation
The idea of targeted marketing is nothing new. Even before the advent of computers and data science, businesses have always tried to optimise their advertising campaigns by tailoring their advertisements to their ideal buyers.Data science allowed businesses to become more effective at this targeting. However, it was still necessary for businesses to manually create the advertising content
Episode 48: Overcoming the Machine Learning Deployment Challenge
It’s been 12 years since Thomas H Davenport and DJ Patil first declared data science to be “the sexiest job of the 21st century” and in that time a lot has changed. Universities have started offering data science degrees; the number of data scientists has grown exponentially; and generative AI technologies, such as Chat-GPT and Dall-E have transformed the world.Yet, throughout that time,
Episode 47: Leveraging Causal Inference to Drive Business Value in Data Science
For most people, data science is synonymous with machine learning, and many see the role of the data scientist as simply being to build predictive models. Yet, predictive analytics can only get you so far. Predicting what will happen next is great, but what good is knowing the future if you don’t know how to change it?That’s where causal analytics can help. However, causal inference is ra
Episode 46: Empowering Democracy with LLMs
With all the reports about the spread of misinformation and disinformation on social media, sometimes it feels like one of the biggest threats to democracy is technology. But no technology is inherently good or bad. It’s how you use it that matters. And just as technology has the potential to harm democracy, it also has the potential to enhance it.In this episode, Vikram Oberoi joins Dr G
Episode 45: AI-Powered Investment Insights
Succeeding in stock market investing is all about timing – buying low, selling high and being able to read the signs to determine when things are going to change. But as anyone who’s ever tried to get rich through stock trading can tell you, this is easier said than done.Given the massive amounts of financial data published each day, for people who aren’t experts in the field, it can be t
Episode 44: Designing Data Products People Actually Want to Use
As a data scientist, there’s nothing worse than devoting months of your time to building a data product that appears to meet your stakeholders’ every need, only to find it never gets used. It’s depressing, demotivating and can be devastating for your career.But as the old saying goes, “You can lead a horse to water, but you can’t make it drink”. Or can you?In this episode, Brian T O’Neill
Episode 43: Shaping the Future of AI
Two years ago, no one could imagine the impact generative AI would have on our world, and most of us can’t even begin to imagine the impact the next generation of AI will have on our world two years from now. The only thing that is certain is uncertainty.But that uncertainty brings with it great opportunities and choices. We can choose to sit back and let the future of AI play out in fron
Episode 42: Should You Outsource Your Data Team?
Chances are, you’re reading this summary on a device you didn’t build yourself. Why would you? Tech companies can build you a far better device for a much lower cost than you could ever manage alone. As with many other cases in life, this is an example of where it is better to buy than to build.Yet, in building a data team, many organisations assume the only solution is to build from with
Episode 41: Building Better AI Apps with Knowledge Graphs and RAG
When ChatGPT was first released, there was talk it would lead to traditional search engines, like Google, soon becoming obsolete. That was until users discovered generative AI’s one major drawback – it makes stuff up.Because of the stochastic nature of ChatGPT, it is never going to be possible to completely eliminate hallucinations. However, there are ways to work around this issue. One s
Episode 40: Making Data Science Teams Profitable
For many people, data science is synonymous with machine learning and many data science courses are little more than overviews of the most used machine learning algorithms and techniques.Where the majority of data science courses fall short is they neglect to bridge the gap between data science theory and business reality, resulting in many data scientists who are technically strong but u
Episode 39: The Impact of Data Science on Data Orchestration
One of the big promises of data science is its ability to combine multiple disparate datasets to produce value-creating insights. But this is only possible if you can get all those disparate datasets together, in the one location, to begin with. The has led to the rise of the data engineer and the data orchestration platform.In this episode, Sandy Ryza joins Dr Genevieve Hayes to discuss
Episode 38 – The Art and Science of Survey Design
From BuzzFeed Quizzes to the national census, it’s impossible to get through life without encountering surveys. However, not all surveys are created equal. As with everything else in data science, garbage going in will inevitably lead to garbage coming out.In this episode, Kyle Block joins Dr Genevieve Hayes to look at practical techniques for designing surveys to ensure they deliver valu
Episode 37: Data Privacy in the Age of AI
Most people have come to accept that the price of living in a technological world, and its associated convenience, is some loss of data privacy. However, few realise just how much privacy they are giving up.In this episode, Dr Katharine Kemp joins Dr Genevieve Hayes to discuss data privacy challenges for consumers and data scientists in the age of AI.Guest BioDr Katharine Kemp is an Assoc
Episode 36: Sequential Decision Problems
Decision-making is an essential part of everyday life and one of the main applications of data science is making the decision-making process easier.However, mostly when data scientists build models, it’s to make a single decision. But in real life, decision-making is rarely that simple.In this episode, Prof Warren Powell joins Dr Genevieve Hayes to discuss one way in which the decision-ma
Episode 35: Data-Driven Podcasting
According to the Interview Valet 2023 State of Podcast Guesting Annual Report, there are over 380,000 active podcasts in the world right now, with the average podcast episode receiving just 150 downloads within 30 days of its release.So, for individuals and organisations looking to use podcast marketing to grow their business, just booking podcast guest appearances isn’t enough. It’s nece
Episode 34: Financial Modelling for Start-Up Founders
Start-ups and data science go hand in hand, but usually when people think about how data science can help start-ups, it’s with regard to product development and enhancement. However, it doesn’t matter how great a start-up’s product is, if the financials are a mess, the business is going to struggle.This is where data science can also help start-ups, in the form of financial modelling and
Episode 33: Making the Shift from Data Scientist to Datapreneur
Data science is among the most in-demand skills of the 21st century, with opportunities existing for data scientists to make a difference and earn good money as an employee in a range of industries. Yet there has also never been a better time to be a data science entrepreneur (or datapreneur).But for data scientists who have never experienced the entrepreneurial life and who are used to t
