Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.

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

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

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

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

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

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

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

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

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 Hayes to explore how unexpected hobbies and activities can make you a more effective data scientist and enhance your career.You'll discover:How dancing skills translate into better stakeholder presentations [02:02]What swimming teaches about working with resistance [06:30]Why coaching swimmers improves communication with non-technical colleagues [08:10]The simple activity anyone can try to expand their data science thinking [11:03]Guest BioColin Priest is an actuary, data scientist and educator who has held several CEO and general management roles where he has championed data-driven initiatives. He now lectures at UNSW, where he specialises in adapting education for the age of AI.LinksConnect with Colin on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 applications that go far beyond code generation, including techniques for processing unstructured data, improving stakeholder communication, and identifying blind spots in your analysis.You'll learn:How to use LLMs to extract structured insights from messy unstructured data [02:50]The role-playing technique that helps you practice difficult stakeholder conversations [14:12]Why using multiple LLMs helps reduce AI hallucinations [20:38]A step-by-step approach for integrating LLMs into your workflow safely [25:52]Guest BioColin Priest is an actuary, data scientist and educator who has held several CEO and general management roles where he has championed data-driven initiatives. He now lectures at UNSW, where he specialises in adapting education for the age of AI.LinksConnect with Colin on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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. Genevieve Hayes to share practical LinkedIn strategies that can transform your data science career.In this episode, you'll discover:How Sarah went from posting twice a year to daily LinkedIn content [01:25]The biggest benefits of consistent LinkedIn posting for data science careers [03:15]How to manage the challenge of daily content creation without burnout [04:31]The one LinkedIn strategy every data scientist should start using tomorrow [08:47]Guest BioSarah Burnett is the co-founder of Dub Dub Data, a consultancy that offers human-centric AI and Tableau solutions. She transitioned into independent consulting after navigating redundancy from a senior role at a major bank. She is also the co-host of the podcast unDubbed.LinksConnect with Sarah on LinkedInDub Dub Data WebsiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 overwhelming experience.In this episode, Sarah Burnett joins Dr. Genevieve Hayes to share how she transformed redundancy from a senior banking role into the launch of her own successful data consultancy, proving that unexpected job loss doesn't have to mean career disaster.In this episode, we explore:Why redundancy is a numbers game, not personal failure [03:54]The power of taking time to process after job loss, instead of rushing back [08:47]How to pivot when your first business idea doesn't work [16:58]Why building side projects and community involvement create career insurance [20:52]Guest BioSarah Burnett is the co-founder of Dub Dub Data, a consultancy that offers human-centric AI and Tableau solutions. She transitioned into independent consulting after navigating redundancy from a senior role at a major bank. She is also the co-host of the podcast unDubbed.LinksConnect with Sarah on LinkedInDub Dub Data WebsiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 what makes you uniquely valuable.In this Value Boost episode, Danny Ruspandini joins Dr. Genevieve Hayes to explore the concept of your "untouchable advantage" - the unique combination of experiences and qualities that make you impossible to replace as a data scientist.You'll discover:Why your untouchable advantage extends far beyond your technical qualifications [02:09]How family influences and personal quirks become professional superpowers [04:14]Why introverts have unique advantages they often don't recognize [10:36]The simple way to uncover your own untouchable advantage starting tomorrow [14:08]Guest BioDanny Ruspandini is a brand strategist, business coach and director of Impact Labs Australia. He is also the creator of One Shiny Object, a program for helping solo creatives package what they do into sellable, fixed-price services.LinksConnect with Danny on LinkedInDownload the One Shiny Object frameworkConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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. The reality is that while bosses usually mean well, they have their own careers to worry about.In this episode, Danny Ruspandini joins Dr. Genevieve Hayes to explore how applying a solo business mindset to your data science career can help you take control of your professional destiny, increase your value within organisations, and create opportunities that others miss.You'll learn:How to become the go-to person for specific problems within your organisation [07:11]The "secondary sale" technique that gets your projects approved even when you're not in the room [14:49]Why focusing on one shiny object at a time accelerates your career faster than juggling multiple priorities [19:06]How to find your signature service that makes you indispensable to your employer [23:00]Guest BioDanny Ruspandini is a brand strategist, business coach and director of Impact Labs Australia. He is also the creator of One Shiny Object, a program for helping solo creatives package what they do into sellable, fixed-price services.LinksConnect with Danny on LinkedInDownload the One Shiny Object frameworkConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 Value Boost episode, Miguel Curiel joins Dr. Genevieve Hayes to share practical strategies for handling the political challenges that come with experimentation and data science work, so you can drive real change without creating enemies.You'll learn:Why running experiments is politically riskier than regular analysis [01:50]The mindset shift that turns experiment "failures" into wins [03:56]How to overcome the "it worked for Netflix" objection [05:07]The simple strategy for reducing political friction around data work [08:24]Guest BioMiguel Curiel is the Product Analytics Manager at Bloomberg, where he works at the intersection of technology, data and human behaviour. He has a background in neuroscience and psychology and is currently writing a book on product analytics.LinksConnect with Miguel on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 businesses to flourish and for data scientists to have a meaningful impact in the work they do. This is where product analytics comes in.In this episode, Miguel Curiel joins Dr. Genevieve Hayes to share his practical checklist for maximising business impact through product analytics, drawing from his own experiences analysing how people actually interact with digital products and his upcoming book on the topic.This episode explores:What product analytics actually involves, beyond just measuring clicks and conversions [03:11]Why behavioural science models are crucial for understanding user motivations [07:25]Miguel's seven-step checklist for building impactful product analytics capabilities [15:49]The most valuable skill for data scientists in product analytics [22:27]Guest BioMiguel Curiel is the Product Analytics Manager at Bloomberg, where he works at the intersection of technology, data and human behaviour. He has a background in neuroscience and psychology and is currently writing a book on product analytics.LinksConnect with Miguel on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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, David Cohen joins Dr. Genevieve Hayes to reveal how gamification can transform stakeholder meetings from painful obligation into collaborative problem-solving sessions that actually produce useful requirements.You'll learn:Why gamification works as a "Trojan horse" for productive business conversations [03:26]How to ensure every voice is heard, not just the loudest or most senior person in the room [06:34]The simple technique that prevents senior executives from dominating and skewing requirements [06:59]The easiest way to add interactive elements to your next stakeholder meeting without complex games [08:20]Guest BioDavid Cohen is a data and AI strategy consultant, with a background in supporting the F500 clients of both Big 4 and boutique consulting firms. He is the founder of Superposition, a consulting firm that builds collaborative workshops focused on data & AI-related use cases.LinksConnect with David on LinkedInSuperposition websiteSuperposition YouTube channelConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 the beginning of projects rather than the end - can dramatically improve stakeholder buy-in and project success rates.This episode reveals:Why the traditional bottom-up data communication approach often misses the mark [02:36]How moving visual storytelling to the start of a project can transform stakeholder engagement [06:40]The gamified workshop framework that turns requirement gathering into collaborative problem-solving [08:50]The counterintuitive first step that immediately improves data project outcomes [20:28]Guest BioDavid Cohen is a data and AI strategy consultant, with a background in supporting the F500 clients of both Big 4 and boutique consulting firms. He is the founder of Superposition, a consulting firm that builds collaborative workshops focused on data & AI-related use cases.LinksConnect with David on LinkedInSuperposition websiteSuperposition YouTube channelConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 Boost episode, Professor Jeff Camm joins Dr. Genevieve Hayes to reveal the specific problem framing framework that decision scientists use to ensure they're solving the right problems from the start, dramatically improving their success rates compared to traditional data science approaches.You'll discover:The medical doctor approach to diagnosing business problems by distinguishing symptoms from root causes [02:09]The critical question that reveals what decisions actually need to be made [04:53]How to turn model "failures" into valuable strategic insights for management [06:24]Why thinking beyond the data prevents you from building technically perfect but business-useless solutions [10:04]Guest BioProf Jeff Camm is a decision scientist and the Inmar Presidential Chair in Analytics at the Wake Forest University School of Business. His research has been featured in top-ranking academic journals and he is the co-author of ten books on business statistics, management science, data visualisation and business analytics.LinksConnect with Jeff on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 difference that dramatically improves success rates.In this episode, Prof Jeff Camm joins Dr. Genevieve Hayes to explore how decision science approaches problems differently from data science, why decision science approaches lead to higher success rates, and how data scientists can integrate these techniques into their own work.This episode reveals:The fundamental difference between modelling data and modelling decisions [04:12]Why decision science projects have historically had higher success rates than current data science efforts [10:42]How to avoid the "ill-defined problem" trap that kills most data science projects [21:12]The medical doctor approach to understanding what business problems really need solving [22:28]Guest BioProf Jeff Camm is a decision scientist and the Inmar Presidential Chair in Analytics at the Wake Forest University School of Business. His research has been featured in top-ranking academic journals and he is the co-author of ten books on business statistics, management science, data visualisation and business analytics.LinksConnect with Jeff on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 joins Dr. Genevieve Hayes to reveal practical strategies for validating data product ideas before investing significant development time, drawing from his experience creating machine learning courses with over 250,000 students and building the Nutrify food education app.This episode uncovers:How to spot genuine market demand before building anything [04:15]The validation strategy that guarantees you win regardless of commercial success [10:16]Why passion projects often create unexpected business opportunities [06:33]The simple approach that turns failed experiments into stepping stones for success [11:50]Guest BioDaniel Bourke is the co-creator of Nutrify, an app described as “Shazam for food”, and teaches machine learning and deep learning at the Zero to Mastery Academy.LinksDaniel's websiteDaniel's YouTube channelConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 successful independent data professional before age 30, revealing the practical steps and mindset shifts needed to transform technical skills into sustainable freedom.In this episode, you'll discover:Why embracing the "permissionless economy" is crucial for independent success [14:59]The power of "starting the job before you have it" [12:17]Why building your own website is the foundation for long-term independent success [24:35]A practical approach to opportunity selection that accelerates career momentum [17:27]Guest BioDaniel Bourke is the co-creator of Nutrify, an app described as “Shazam for food”, and teaches machine learning and deep learning at the Zero to Mastery Academy.LinksDaniel's websiteDaniel's YouTube channelConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 Dr. Genevieve Hayes to reveal how he transformed his entire data team's performance and culture through a simple but powerful approach: starting a BI book club that costs almost nothing but delivers enormous ROI.This episode reveals:How a weekly team book club transformed Kashif's data team [02:26]The "data concierge" concept that transforms dashboard builders into trusted business advisors [04:07]Why Data Insights Delivered by Mo Villagran is a team game-changer [08:28]The critical difference between fulfilling requests and solving underlying business problems [09:05]Guest BioKashif Zahoor is the Vice President of Business Intelligence at Influence Mobile and has extensive experience in data leadership.LinksConnect with Kashif on LinkedInData Insights Delivered (Amazon Australia)(Amazon US)The AI-Driven Leader (Amazon Australia)(Amazon US)Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 this episode, Kashif Zahoor joins Dr. Genevieve Hayes to share his journey from data order taker to strategic business partner, revealing a practical framework that any data professional can use to transform their role and accelerate their career growth.You'll learn:The three-step framework for evolving from order taker to strategic partner: amplify efficiency, deliver measurable value, and partner first, analyze second [06:21]Why understanding your company's financial model is crucial for demonstrating real business impact [10:57]The mindset shift from waiting for requests to proactively identifying and solving business problems [19:33]How building trust through consistent delivery opens doors to bigger strategic conversations [17:04]Guest BioKashif Zahoor is the Vice President of Business Intelligence at Influence Mobile and has extensive experience in data leadership.LinksConnect with Kashif on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 for identifying and overcoming the cognitive biases that sabotage data-driven decision making.This episode reveals:How confirmation bias transforms data analysis into a "numerical Rorschach test" where stakeholders see only what confirms their existing beliefs [02:59]The "verbal jujitsu" technique that acknowledges preconceptions without confrontation, allowing stakeholders to save face while guiding them toward data-driven conclusions [03:47]Why recency bias makes yesterday's angry customer complaint outweigh months of systematic data analysis in executive decision making [05:24]The pre-meeting strategy that helps you anticipate and prepare for stakeholder blind spots before they derail your presentation [07:00]Guest BioDr Russell Walker is the principal consultant at Walker Associates, which specialises in data science education and healthcare analytics, and previously served as a professor at DeVry University, where he co-founded the university’s business intelligence and analytics program. He holds a PhD in business administration with a specialty in computer science.LinksRussell's WebsiteConnect with Russell on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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. Genevieve Hayes to reveal how battle-tested frameworks from competitive debating can bridge this gap, transforming data scientists from skilled communicators into persuasive advocates who drive real organizational change.This conversation reveals:The fundamental difference between ethical persuasion and manipulation [03:13]How to make dry statistics emotionally compelling by connecting data points to human experiences that resonate with decision-makers [08:11]The four-part "stock issues" framework from policy debate that transforms any technical presentation into a persuasive business case [11:22]The executive summary and headline strategies that ensure your persuasive message cuts through information overload [17:44]Guest BioDr Russell Walker is the principal consultant at Walker Associates, which specialises in data science education and healthcare analytics, and previously served as a professor at DeVry University, where he co-founded the university’s business intelligence and analytics program. He holds a PhD in business administration with a specialty in computer science.LinksRussell's WebsiteConnect with Russell on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 practical strategies for identifying and filtering out bot activity from social media datasets to extract trustworthy business insights.This episode uncovers:The telltale patterns in social media data that reveal bot activity [03:10]How machine learning classifiers can identify bot accounts [05:20]Why removing bot activity can increase marketing ROI by 10-20% [06:41]The broader application of these techniques beyond social media for identifying "dodgy" data records in any dataset [07:25]Guest BioTim O’Hearn is a software engineer who spent years gaining millions of followers for clients by circumventing anti-botting measures on social networks. He is also the author of the new book, Framed: A Villain’s Perspective on Social Media.LinksTim's WebsiteConnect with Tim on LinkedInSubscribe to Tim's newsletterConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE