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

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

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. Genevieve Hayes to share insights from his experience manipulating social media platforms, revealing what ethical data scientists can learn from understanding the dark side of algorithmic systems.This conversation reveals:How social media platforms are essentially just sophisticated recommendation engines [08:16]The "canary" technique for detecting when underlying systems have changed [11:36]Why customer accounts often provide better testing data than artificial test accounts [13:56]The importance of time series data collection for identifying suspicious patterns, effectiveness of campaigns, and understanding platform dynamics [18:04]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

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 showcasing your value as a data professional.In this episode, you'll discover:The number one reason why dashboards fail [01:15]The three-bucket framework that transforms dashboard development [04:06]How to salvage an already-built dashboard [07:12]The simple wireframing technique that opens doors to meaningful user conversations [10:08]Guest BioNicholas Kelly is the founder of Delivering Data Analytics, a consultancy focused on helping organisations enable their teams to make smarter, faster, and more confident decisions through data and AI. He is also the author of Delivering Data Analytics and the recently released How to Interpret Data.LinksNicholas's WebsiteConnect with Nicholas on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 technical expertise alone isn't enough and sharing practical frameworks for transforming raw data into actionable business insights that drive real organisational change.This conversation reveals:The four primary challenges that make data interpretation so difficult [02:24]Why ChatGPT and AI tools are changing the data interpretation landscape [06:23]The "Five Whys" technique that ensures you're asking the right questions instead of wasting time on problems everyone already understands [17:32]Why successful data projects don't end with presenting insights and what to do next [20:01]Guest BioNicholas Kelly is the founder of Delivering Data Analytics, a consultancy focused on helping organisations enable their teams to make smarter, faster, and more confident decisions through data and AI. He is also the author of Delivering Data Analytics and the recently released How to Interpret Data.LinksNicholas's WebsiteConnect with Nicholas on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 partners by providing both external credibility and internal direction.This episode reveals:Why a clear purpose statement serves as both an external marketing tool and an internal compass for daily decision-making [02:09]A framework for identifying your stakeholders' true pain points and how your data skills can address them [04:48]A practical first step to develop your own value statement that aligns with organizational strategy while focusing your daily work [06:53]Guest BioDr Peter Prevos is a water engineer and manages the data science function at a water utility in regional Victoria. He runs leading courses in data science for water professionals, holds an MBA and a PhD in business, and is the author of numerous books about data science and magic.LinksConnect with Peter on LinkedInA Brief Guide to Providing Insights as a Service (IaaS)Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 services marketing to transform his water utility's data team from "report writers" to strategic partners by positioning data science as "Insights-as-a-Service."This episode explains:Why treating data science as "Customer Satisfaction Engineering" rather than technical implementation shifts everything about team effectiveness [08:19]How understanding both the financial and psychological "price" users pay for insights leads to dramatically better adoption [14:36]The treasure hunt technique that transformed how stakeholders discover and engage with available data resources [18:17]Why the mantra "99% of business problems don't need machine learning" can paradoxically increase your data science impact [22:29]Guest BioDr Peter Prevos is a water engineer and manages the data science function at a water utility in regional Victoria. He runs leading courses in data science for water professionals, holds an MBA and a PhD in business, and is the author of numerous books about data science and magic.LinksConnect with Peter on LinkedInA Brief Guide to Providing Insights as a Service (IaaS)Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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, Technology second, Opinions third" hierarchy - a framework that will transform how you handle stakeholder pressure without compromising your standards.In this episode, you'll discover:Why this critical hierarchy gets dangerously inverted when deadlines loom and how to prevent it from undermining your credibility [01:05]How to resist the career-limiting trap of cherry-picking facts that merely support executive opinions [04:09]A practical note-taking technique that keeps you anchored to reality when stakeholders push for convenient answers [06:04]The one transformative habit that separates truly valuable data scientists from those who merely validate existing assumptions [07:17]Guest BioDr Brian Godsey is a Data Science Lead at AI platform as a service company DataStax. He is also the author of Think Like a Data Scientist and holds a PhD in Mathematical Statistics and Probability.LinksBrian's websiteConnect with Brian on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 Godsey joins Dr. Genevieve Hayes to discuss why the scientific process behind data science remains more critical than ever, sharing how his original "Think Like a Data Scientist" framework has evolved to harness today's powerful AI capabilities while maintaining the principles that drive real business values.This conversation reveals:Why the seemingly basic question "Where do I start?" continues to derail data scientists' effectiveness and how mastering the right process can transform your impact [01:15]The three stages of the data science process that remain essential for career success even as AI dramatically changes how quickly you can execute them [11:07]How the accessibility revolution of generative AI creates new career opportunities for data scientists in organizations that previously couldn't leverage advanced analytics [18:34]The underrated troubleshooting skill that will make you invaluable as organizations increasingly rely on "black box" AI models for business-critical decisions [20:21]Guest BioDr Brian Godsey is a Data Science Lead at AI platform as a service company DataStax. He is also the author of Think Like a Data Scientist and holds a PhD in Mathematical Statistics and Probability.LinksBrian's websiteConnect with Brian 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 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 technical charts into powerful communication tools - even for those without formal design training.This quick-hit episode reveals:Why default visualisation settings in most software undermine effective communication [02:03]The research-backed "preattentive response" principle that determines whether your visualisation succeeds or fails [05:17]How the counterintuitive "do less" approach creates more impactful data stories [06:18]A simple glance test to immediately evaluate and improve any visualisation you create [11:21]Guest BioBill Shander is the founder of Beehive Media, a data visualisation and information design consultancy. He is also a keynote speaker; teaches workshops on data storytelling, information design, data visualisation and data analytics; and is the author of Stakeholder Whispering.LinksBill's WebsiteConnect with Bill 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 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 the "Stakeholder Whispering" approach from his new book - a methodology that transforms technical experts from order-takers into strategic partners who uncover and address true business needs.This conversation reveals:Why stakeholders struggle to articulate what they truly need (and often don't even know themselves) [06:32]How the "Socratic method" creates breakthrough moments that help stakeholders discover their own requirements [11:00]The six-question framework that strategically alternates between divergent and convergent thinking to reveal hidden needs [14:54]Why approaching stakeholder conversations like a curious investigator rather than a cross-examiner builds trust and uncovers deeper insights [13:28]Guest BioBill Shander is the founder of Beehive Media, a data visualisation and information design consultancy. He is also a keynote speaker; teaches workshops on data storytelling, information design, data visualisation and data analytics; and is the author of Stakeholder Whispering.LinksBill's WebsiteConnect with Bill on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 data science career and for even the most budget-conscious clients.In this episode, you'll uncover:Why Claude consistently outperforms ChatGPT for business applications and how to leverage it as your AI partner for everything from sales coaching to content creation [01:32]How Perplexity delivers real-time research capabilities that save hours of manual work while providing verified sources you can trust [04:02]How Fireflies AI notetaker creates a searchable knowledge base from client conversations that enhances follow-up and project management [07:56]A practical first step to start implementing this maximum-value toolkit in your data science practice tomorrow [09:39]Guest BioHeidi Araya is the CEO and chief AI consultant of BrightLogic, an AI automation agency that specializes in delivering people-first solutions that unlock the potential of small to medium sized businesses. She is also a patented inventor, an international keynote speaker and the author of two upcoming books, one on process improvement for small businesses and the other on career and personal reinvention.LinksConnect with Heidi on LinkedInBrightLogic websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 jaw-dropping ROI. We're talking five to seven-figure returns from solutions you could build in your sleep.In this episode, Heidi Araya joins Dr Genevieve Hayes to reveal exactly how data scientists can escape the soul-crushing enterprise world and build a thriving practice serving clients who actually appreciate your genius.Prepare to discover:Why AI implementations for small businesses can deliver dramatically higher ROI than enterprise solutions [12:16]The three pre-built AI solutions that consistently generate the greatest value for resource-constrained clients [12:16]A practical framework for identifying high-impact opportunities even when clients have minimal data [16:59]The "AI receptionist" solution that generated $30 million in new business from dead leads for one small client [21:19]Guest BioHeidi Araya is the CEO and chief AI consultant of BrightLogic, an AI automation agency that specializes in delivering people-first solutions that unlock the potential of small to medium sized businesses. She is also a patented inventor, an international keynote speaker and the author of two upcoming books, one on process improvement for small businesses and the other on career and personal reinvention.LinksConnect with Heidi on LinkedInBrightLogic websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 Genevieve Hayes to explain his controversial 90-10 rule: that success in AI and data science is 90% about people and only 10% about technology - and shares a surprisingly simple way to put this principle into practice.You'll learn:Why focusing purely on technology creates a dangerous blind spot [01:53]The critical success factor that most data science teams overlook [03:54]The "toasted sandwich strategy" for building crucial relationships [05:54]Guest BioGregory Lewandowski is the Chief AI Strategist and Founder of GLEW, a consultancy focussing on the business side of AI ROI.LinksConnect with Gregory on LinkedInGLEW Services websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 the future belongs to data scientists who understand them.This episode reveals:The two priorities that can unlock budget even mid-cycle (and why cost savings isn't one of them) [07:50]How executive priorities evolve across technology adoption cycles [10:16]Why misaligned compensation metrics doom data science projects [13:03]The "follow the money" framework for understanding what drives business decisions [12:22]Guest BioGregory Lewandowski is the Chief AI Strategist and Founder of GLEW, a consultancy focussing on the business side of AI ROI.LinksConnect with Gregory on LinkedInGLEW Services websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 framework for evaluating AI models [02:05]The key metrics that help you compare different models objectively [02:28]Why understanding speed-cost-accuracy tradeoffs gives you an edge [05:47]How this approach gets you “in the room where it happens” for key AI decisions [07:20]Guest BioAndrei Oprisan is a technology leader with over 15 years of experience in software engineering, specializing in product development, machine learning, and scaling high-performance teams. He is the founding Engineering Lead at Agent.ai and is also currently completing an Executive MBA through MIT’s Sloan School of Management.LinksConnect with Andre on LinkedInAndrei’s websiteAgent.ai websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 critical business questions most technical experts never think to ask [02:21]Why understanding business context makes you better at technical work (not worse) [14:10]How to turn natural curiosity into career opportunities without losing your technical edge [09:19]The simple mindset shift that helps you spot business impact others miss [21:05]Guest BioAndrei Oprisan is a technology leader with over 15 years of experience in software engineering, specializing in product development, machine learning, and scaling high-performance teams. He is the founding Engineering Lead at Agent.ai and is also currently completing an Executive MBA through MIT’s Sloan School of Management.LinksConnect with Andre on LinkedInAndrei’s websiteAgent.ai websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 critical business context most data scientists overlook when presenting their work [02:10]How to ensure your technical content works as hard as you do – whether presented live or shared asynchronously [04:42]The “so what” framework that instantly makes your analysis more compelling to leaders [06:57]Guest BioLauren Lang is the Director of Content for Uplevel and is also a Content Strategy Coach for B2B marketers.Dr Matt Hoffman is a Senior Data Analyst: Strategic Insights at Uplevel and holds a PhD in Physics from the University of Washington.LinksConnect with Lauren on LinkedInConnect 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

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 expertise.Their surprising findings about Gen AI’s impact on developer productivity went viral across 75 global media outlets – not because of complex statistics, but because of how they told the story.Here’s what you’ll learn:Why the “last mile” is killing your data science impact – and how to fix it through strategic collaboration [01:00]The counterintuitive findings about Gen AI that sparked global attention (including a 40% increase in code defects) [13:02]How to transform “disappointing” technical results into compelling business narratives that drive real change [17:15]The exact process for structuring your insights to keep executives engaged (and off their phones) [08:31]Guest BioDr Matt Hoffman is a Senior Data Analyst: Strategic Insights at Uplevel and holds a PhD in Physics from the University of Washington.Lauren Lang is the Director of Content for Uplevel and is also a Content Strategy Coach for B2B marketers.LinksConnect with Matt on LinkedInConnect with Lauren on LinkedInCan Generative AI Improve Developer Productivity? (Report)Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 that transformed his 20-year development career – which you can use to transform your data science productivity right now.Get ready to discover:The Kanban and focus currency techniques that replace notification-driven chaos [02:09]A 90-day planning system that beats imposter syndrome and drives results [03:09]Why two-hour focus blocks outperform constant context switching [04:19]The habit tracking method that helps you consistently “win the day” [06:12]Guest BioBen Johnson is the CEO and Founder of Particle 41, a development firm that helps businesses accelerate their application development, data science and DevOps projects.LinksConnect with Ben on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 multiplier for data science careers, and shares proven strategies to dramatically accelerate your results.This episode reveals:Why lacking clear intention kills productivity — and how to ensure every analysis drives real decisions. [02:11]A powerful “storyboarding” framework for turning vague requests into actionable projects. [09:51]How to deliver results faster using modern data architectures and raw data analysis. [13:19]The game-changing mindset shift that transforms data scientists from order-takers into trusted strategic partners. [17:05]Guest BioBen Johnson is the CEO and Founder of Particle 41, a development firm that helps businesses accelerate their application development, data science and DevOps projects.LinksConnect with Ben on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 Stouse and John Thompson, join Dr Genevieve Hayes to deliver a tough love wake-up call on why data scientists struggle to create business impact, and more importantly, how to fix it.This episode reveals:Why focusing purely on technical metrics like accuracy and precision is sabotaging your success — and what metrics actually matter to business leaders. [04:18]The critical mindset shift needed to transform from a back-room technical specialist into a valued business partner. [30:33]How to present data science insights in ways that drive action — and why your fancy graphs might be hurting rather than helping. [25:08]Why “data driven” isn’t enough, and how to adopt a “data informed” approach that delivers real business outcomes. [54:08]Guest BioBill Schmarzo, also known as “The Dean of Big Data,” is the AI and Data Customer Innovation Strategist for Dell Technologies’ AI SPEAR team, and is the author of six books on blending data science, design thinking, and data economics from a value creation and delivery perspective. He is an avid blogger and is ranked as the #4 influencer worldwide in data science and big data by Onalytica and is also an adjunct professor at Iowa State University, where he teaches the “AI-Driven Innovation” class.Mark Stouse is the CEO of ProofAnalytics.ai, a causal AI company that helps companies understand and optimize their operational investments in light of their targeted objectives, time lag, and external factors. Known for his ability to bridge multiple business disciplines, he has successfully operationalized data science at scale across large enterprises, driven by his belief that data science’s primary purpose is enabling better business decisions.John Thompson is EY’s Global Head of AI and is the author of four books on AI, data and analytics teams. He was named one of dataIQ’s 100 most influential people in data in 2023 and is also an Adjunct Professor at the University of Michigan, where he teaches a course based on his book “Building Analytics Teams”.LinksConnect with Bill on LinkedInConnect with Mark on LinkedInConnect with John on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 machine learning can be used to help others in the organisation perform their jobs more effectively, there’s no reason why they can’t also be used to help data professionals excel in their own jobs. And as experts in applying these techniques, data scientists are perfectly placed to leverage them.In this episode, Prof Barzan Mozafari joins Dr Genevieve Hayes to discuss how AI and machine learning are helping data professionals do their jobs more effectively.Guest BioProf. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and Prof. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and has won several awards for his research at the intersection of machine learning and database systems.Highlights(00:05) Meet Barzan Mozafari(00:50) The role of AI in data engineering(01:36) The birth of Keebo(02:34) Challenges in modern data pipelines(05:41) How Keebo optimizes data warehousing(07:35) AI and ML techniques behind Keebo(08:47) Reinforcement learning in practice(16:23) Guardrails and safeguards in AI systems(26:29) The build vs. buy dilemma(36:03) Future trends in data science and AI(39:36) Final advice for data scientists(40:50) Closing remarks and contact informationLinksKeebo websiteConnect with Barzan on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 was first released. But over 20 years later, we are now at a point where technology has become good enough for this to soon become fact. And its data science that’s making this possible.Or more accurately, it’s the intersection of data science and art.In this episode, Michela Ledwidge joins Dr Genevieve Hayes to discuss how virtual reality and data science can be combined to create interactive data storytelling experiences.Guest BioMichela Ledwidge is the co-founder and CEO of Mod, a studio specialising in real-time and virtual production, and the creator of Grapho, a VR platform that lets non-technical users examine and manipulate graph data. She is also the writer and director of A Clever Label, a world-first interactive documentary.Highlights(00:05) Meet Michela Ledwidge(02:04) Michela’s journey from Commodore 64 to interactive filmmaking(06:40) The birth of Mod and remixable films(14:48) Exploring graph databases and data science techniques(25:33) The future of data science and AI in creative industries(32:27) Grapho: Data science + storytelling in virtual reality(48:29) The future of data science and storytelling(49:37) Conclusion and contact informationLinksGrapho websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 Rumsfeld made his famous “unknown unknowns” speech, he was referring to military intelligence. But the concept of “unknown unknowns” is just as relevant to data and data science. Those data dark spots, or data gaps, can be a real issue when it comes to data-driven decision making.In this episode, Matt O'Mara joins Dr Genevieve Hayes to discuss the challenges and risks data gaps present to businesses and the community, and what data scientists can do to help address this issue.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.Highlights(00:55) Understanding information gaps(02:33) Matt O'Mara's journey and insights(04:58) Real-world examples of information gaps(07:30) The impact of information gaps on society(11:54) Organizational challenges and solutions(25:55) Critical information sources and management(31:33) Developing an information lens(42:47) The role of data scientists in addressing information gaps(45:29) Conclusion and contact informationLinksi3 websiteConnect 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

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 they wanted to share with their target buyers. That is, until recently.In this episode, Hikari Senju joins Dr Genevieve Hayes to discuss how advances in AI technology have made it possible to generate personalised advertising content, optimised to produce the best results, and what that means for content creators.Guest BioHikari Senju is the founder and CEO of Omneky, an AI platform that generates, analyzes and optimizes personalised advertising content at scale. He is a Harvard computer science graduate and also co-founded tutoring app Quickhelp, which he later sold to Yup.com.Highlights(02:06) How OmneKey works(03:29) Personalisation in advertising(06:35) The role of human input in AI-generated content(10:45) Impact of AI on the advertising industry(15:09) Hikari Senju’s journey and insights(19:53) Technical deep dive into OmneKey(25:54) The competitive landscape of AI(32:10) The future of content and AI(40:26) Conclusion and final thoughtsLinksOmnekey websiteConnect with Hikari on LinkedInFollow Hikari on XConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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, one thing has remained the same. Most machine learning projects still fail to deploy.However, it’s not the technical capabilities of data scientists that let them down – those are now better than ever before. Rather, “it’s the lack of a well-established business practice that is almost always to blame.”In this episode, Dr Eric Siegel joins Dr Genevieve Hayes to discuss bizML, the new “gold-standard”, six-step practice he has developed “for ushering machine learning projects from conception to deployment.”Guest BioDr Eric Siegel is a leading machine learning consultant and the CEO and co-founder of Gooder AI. He is also the founder of the long-running Machine Learning Week conference series; author of the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die and the recently released The AI Playbook; and host of The Dr Data Show podcast.Highlights(01:21) Challenges in machine learning deployment(05:00) The importance of business involvement in ML projects(15:39) Defining bizML and its steps(25:32) Understanding predictive analytics(26:52) Challenges in model deployment and MLOps(29:12) BizML for generative and causal AI(31:25) Exploring uplift modeling(35:45) Gooder AI: bridging the gap between data science and business value(45:45) Beta testing and future plans for Gooder AI(47:35) Final advice for data scientistsbLinksBizML websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 rarely taught as part of traditional prediction-centric data science training. Where it is taught, though, is in the social sciences.In this episode, Joanne Rodrigues joins Dr Genevieve Hayes to discuss how techniques drawn from the social sciences, in particular, causal inference, can be combined with data science techniques to give data scientists the ability to understand and change consumer behaviour at scale.Guest BioJoanne Rodrigues is an experienced data scientist with master’s degrees in mathematics, political science and demography. She is the author of Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights and the founder of health technology company ClinicPriceCheck.com.Highlights(00:49) Combining social sciences with data science(02:01) Joanne’s journey from social sciences to data science(04:15) Understanding causal inference(07:40) Real-world applications of causal inference(12:22) Challenges in causal inference(19:41) Correlation vs. causation in data science(26:12) Operationalising randomness in experiments(27:16) Observational experiments vs. medical trials(27:47) Designing experiments with existing data(28:50) Challenges in natural experiments(29:55) Ethical considerations in experimentation(31:50) Qualitative frameworks in causal inference(35:58) Integrating causal inference with machine learning(38:59) Common techniques in causal inference(41:02) Marketing causal inference to management(43:48) Ethical implications of predictive modelling(48:08) Final advice for data scientistsLinksConnect with Joanne on LinkedInJoanne’s websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 Genevieve Hayes to discuss how he has been using generative AI and large language models (LLMs) to enhance people’s access to NYC council meetings through his work on citymeetings.nyc.Guest BioVikram Oberoi is a software engineer, fractional CTO and co-owner of Baxter HQ, a boutique early-stage tech product development firm. He also built and operates citymeetings.nyc, an LLM powered tool to make New York City council meetings accessible.Highlights(00:00) Meet Vikram Oberoi(01:31) Overview of citymeetings.nyc(07:50) Vikram’s journey into local politics(12:05) Technical aspects of citymeetings.nyc(18:41) Dealing with AI hallucinations(25:00) Understanding the different types of AI errors(26:05) Case study: Honeycomb’s query feature(26:59) Reinforcement learning with human feedback(28:32) Choosing between Claude and GPT(31:42) The importance of context windows(40:31) Effective prompt engineering tips(46:11) Final advice for data scientistsLinkscitymeetings.nycVikram’s websiteVikram’s talk at NYC School of Data about citymeetings.nycFollow Vikram on XConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 too hard to spot the patterns and keep up with the constant change. As a result, many people are either investing in markets based on guesswork or not investing at all.This is where AI can help, because there’s nothing that AI does better than finding patterns in large volumes of data. AI has the potential to democratize access to investment insights.In this episode, Andrew Einhorn joins Dr Genevieve Hayes to discuss how AI can help ordinary investors find better investment opportunities than they could ever manage on their own.Guest BioAndrew Einhorn is the CEO and co-founder of Levelfields, an AI-driven fintech application that automates arduous investment research so investors can find opportunities faster and easier. Before moving into finance, Andrew started his career as an epidemiologist and helped build a pandemic monitoring system for Georgetown Hospital. He also previously co-founded tech company Synoptus, has consulted for NASA and served as an advisor to a $65 billion hedge fund.Highlights(00:06) Meet Andrew Einhorn(02:54) Andrew’s journey from public health to data science(07:55) The birth of Levelfields(19:35) Event-driven investment insights explained(26:22) AI and data science behind Levelfields(36:36) User experience and customisation(41:03) Future developments and final adviceLinksLevelfields websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 joins Dr Genevieve Hayes to discuss how you can apply the best techniques from software product management and UI/UX design to create ML and AI products your stakeholders will love.Guest BioBrian T O’Neill is the Founder and Principal of Designing for Analytics, an independent data product UI/UX design consultancy that helps data leaders turn ML & analytics into usable, valuable data products. He also advises on product and UI/UX design for startup founders in MIT’s Sandbox Innovation Fund; hosts the podcast Experiencing Data; founded The Data Product Leadership Community and maintains a career as a professional percussionist performing in Boston and internationally.HighlightsIntroducing Brian T. O’Neill (00:19)Brian’s journey from music to data product design (02:16)Understanding the real needs of stakeholders (06:45)The importance of user-centered design in data products (09:33)Gaining insights through direct user interaction (12:16)Focusing on business and user experience outcomes (17:48)Debunking the myths of self-serve analytics and dashboarding (22:46)Data platforms vs. data products (27:26)Defining a data product: the value exchange principle (29:08)Designing human-centered data products (32:56)The CED framework: conclusions, evidence, data (36:01)Final advice for data scientists (45:06)LinksBrian’s mailing listDesigning for AnalyticsData Product Leadership CommunityCED frameworkConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 front of us or engage with this new technology and shape the future of AI and the world as we know it.In this episode, Dr Eric Daimler joins Dr Genevieve Hayes to discuss his extraordinary work in shaping the future of AI and what that future might look like.Guest BioDr. Eric Daimler is the Chair, CEO and Co-Founder of Conexus AI and has previously co-founded five other companies in the technology space. He served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of President, as the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics. He is also the author of the upcoming book The Future is Formal: The Roadmap for Using Technology to Solve Society’s Biggest Problems.Highlights(00:00) Meet Dr. Eric Daimler(01:46) Eric’s role in the Obama Administration(06:32) Challenges in government data integration(10:31) The importance of technical expertise in policy(16:06) Founding Connexus AI(18:09) Understanding category theory(20:51) Applications of Conexus AI(27:16) The future of AI: safe and symbolic(38:35) Insights from Eric’s upcoming book(47:49) Advice for data scientists and final thoughtsLinksConnect with Eric on LinkedInConexus AI websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 within. And although this may be the right solution for some organisations, building a solution isn’t right for all.In this episode, Collin Graves joins Dr Genevieve Hayes to discuss what a bought solution might look like in the data science space, and whether it is right for you.Guest BioCollin Graves is the CEO of North Labs, a leading fractional cloud data analytics firm that helps growing companies become data-driven. Before founding North Labs, he served with distinction in NATO Special Operations during his tenure with the US Air Force. He is also the author of the upcoming Data Revolution: Leading with Analytics and Winning from Day One.Highlights(01:43) Collin’s journey from the US Air Force to data science (09:53) The birth of North Labs: a fractional data analytics firm (12:02) Scaling a one-man operation to a thriving business (13:58) The challenges of using data in the industrial and manufacturing sector (28:41) The power of outsourcing data science (34:09) The future of data teams and the role of in-house expertise (41:44) Insights from Collin’s upcoming book (46:17) Final thoughts and advice for data scientistsLinksConnect with Collin on LinkedInNorth Labs websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 such way is through leveraging knowledge graphs and retrieval augmented generation (or RAG).In this episode, Kirk Marple joins Dr Genevieve Hayes to discuss how knowledge graphs and RAG can be leveraged to improve the quality of generative AI.Guest BioKirk Marple is the CEO and Technical Founder of Graphlit, serverless, cloud-native platform that streamlines the development of AI apps by automating unstructured data workflows and leveraging retrieval augmented generation.Highlights(00:19) Meet Kirk Marple(01:22) Leveraging knowledge graphs and RAG(06:08) Challenges with named entity extraction(09:16) Cost implications of LLMs(12:17) Deep dive into RAG(16:58) Vector search explained(20:49) Graph databases and RAG(38:58) Future of RAG and AI(43:08) Final thoughtsLinksConnect with Kirk on LinkedInGraphlit websiteFollow Graphlit on XConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 unable to create value from their work. However, this doesn’t necessarily have to be the case.In this episode, Douglas Squirrel joins Dr Genevieve Hayes to discuss systems and techniques data scientists and their managers can use to make data science teams profitable.Guest BioDouglas Squirrel has been coding for forty-five years and has led software teams for twenty-five. He uses the power of conversations to create insane profits in technology organisations of all sizes. His experience includes growing software teams as a CTO in startups; consulting on product improvement; and coaching a wide variety of leaders in improving their conversations, aligning to business goals, and creating productive conflict.HighlightsDouglas Squirrel’s journey: From CTO to profitability guru (00:00)Integrating data science with business goals (10:58)The surprising technological growth in Africa (17:38)Overcoming the Walled Garden: strategies for tech team success (19:14)The Lean Startup approach to data science (26:48)The importance of direct feedback in data science (32:50)Transforming data science with human empathy (33:39)Leveraging action science for effective communication (42:46)Elephant Carpaccio (47:41)Techniques for data scientists to create business value (51:22)Creating productive conflict for business innovation (53:43)Final thoughts and resources (01:00:28)LinksDouglas Squirrel’s websiteSquirrel SquadronConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 the impact of the data scientist on the creation of the next generation of data orchestration tools.Guest BioSandy Ryza is a data scientist turned data engineer who is currently the lead engineer on the Dagster project, an open-source data orchestration platform used in MLOps, data science, IOT and analytics. He is also the co-author of Advanced Analytics with Spark.HighlightsWelcome to Value Driven Data Science (00:00)Introducing Sandy Ryza and his journey from data scientist to data engineer (01:30)Navigating the challenges of creating consistent data definitions within teams (05:11)The birth and development of Dagster (11:32)Dagster: A tool designed for data scientists (20:54)Final thoughts and advice for data scientists (37:29)LinksConnect with Sandy on LinkedInFollow Sandy on XDagsterConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 value, as well as approaches to analysing survey results, to maximise that value.Guest BioKyle Block is Head of Research at Gradient, an analytics agency that combines advanced statistical and machine learning techniques to answer difficult marketing challenges. He holds a Masters in Spatial Analysis from the University of Pennsylvania and has spent his career helping managers use data to make important decisions.Talking PointsWhat good survey design looks like.Advice on how to design effective surveys.How list experiments can be used to uncover true opinions around sensitive topics.How data science techniques can be applied to survey data analysis to maximise its value.What the future might hold for survey data analysis.LinksConnect with Kyle on LinkedInGradient WebsiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 Associate Professor in UNSW’s Faculty of Law and Justice and Deputy Director of the Allens Hub for Technology, Law and Innovation. Her research focuses on competition, data privacy and consumer protection regulation, including their application to digital platforms.Talking PointsWhat types of data are companies collecting about their customers?How companies currently de-identify customer data to ensure consumer privacy is protected.The effectiveness of data de-identification methods at truly protecting the privacy of individuals.The state of current consumer data privacy laws and how they are likely to evolve.The impact of generative AI tools, such as ChatGPT, on consumer data privacy.LinksUNSW Allens Hub for Technology, Law and InnovationKatharine’s Research (SSRN Page)Consumer Policy Research CentreSingled Out ReportConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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-making process can become more complicated, in the form of sequential decision problems.Guest BioWarren Powell is the co-founder and Chief Innovation Officer of Optimal Dynamics and a Professor Emeritus after retiring from Princeton, where he was a faculty member in the Department of Operations Research and Financial Engineering. He is also the author of Sequential Decision Analytics and Modelling and Reinforcement Learning and Stochastic Optimization.Talking PointsWhat is a sequential decision problem?Real-life examples of sequential decision problems and the disciplines in which they occur.The four main classes of techniques for solving sequential decision problems.How Warren’s approach to addressing sequential decision problems differs from the standard approach in this space.The challenges of implementing sequential decision analysis techniques in practice.LinksConnect with Warren on LinkedInWarren’s website (SDA Links)Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 necessary to use a targeted strategy based on data.In this episode, Tom Schwab joins Dr Genevieve Hayes to discuss how Interview Valet uses data to optimise business results in podcast interview marketing.Guest BioTom Schwab is the founder and Chief Evangelist Officer of Interview Valet and the author of Podcast Guest Profits and One Conversation Away. He is also an engineer whose first job out of college involved running nuclear power plants in the US Navy.Talking PointsWhat is podcast interview marketing and how it differs from traditional digital marketing approaches?How Tom uses data to inform podcast guest marketing strategies at Interview Valet.The most important metrics for targeting podcast marketing and optimising return on investment.What makes a top podcast?How Tom’s use of data and analytics has evolved over time.LinksInterview ValetConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 analysis.In this episode, Lauren Pearl joins Dr Genevieve Hayes to discuss her work in helping start-up founders translate their business ideas into maths via financial models.Guest BioLauren Pearl is a CEO-turned-CFO who helps start-up founders work better with financial data. She holds an MBA from NYU’s Stern School of Business and is the resident start-up finance expert at NYU’s Berkley Centre for Entrepreneurship.Talking PointsWhat is meant by financial modelling?The challenges of building financial models with little or no data.Why is it important for founders to understand their financials.The potential consequences of not understanding financial data.How founders can use data and technology more generally to help in running their business.LinksConnect with Lauren on LinkedInLauren Pearl ConsultingConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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 the security of a steady pay check, making the transition from employee to entrepreneur may seem like an impossible leap, regardless of how desirable it may seem.In this episode, David Shriner-Cahn joins Dr Genevieve Hayes to discuss how data scientists can escape the corporate world and make the transition from employee to datapreneur.Guest BioDavid Shriner-Cahn is the podcast host and community builder behind Smashing the Plateau, an online platform offering resources, accountability, and camaraderie to high-performing professionals who are making the leap from the corporate career track to entrepreneurial business ownership.Talking PointsHow entrepreneurship differs from being a regular employee, particularly with regard to mindset.The advantages and disadvantages of each way of making a living.Making the transition from employment to entrepreneurship and how to gauge if entrepreneurship is right for you.Building your network as an entrepreneur.How taking a sabbatical can help ease the transition between being an employee and an entrepreneur.The value of community.LinksSmashing the PlateauConnect with Genevieve on LinkedInValue Driven Data Science has recently featured in Feedspot’s list of the 4 Best Australian Data Science Podcasts. Be among the first to hear about the release of each new podcast episode by signing up HERE

Depending on who you speak to blockchain and cryptocurrency are either the way of the future or the scam of the century. But few would be able to tell you what either of them actually is – including among data scientists for whom data and technology are a way of life. In this episode, Luke Willis joins Dr Genevieve Hayes to demystify blockchains, cryptocurrency and the data behind them.Guest BioLuke Willis is the dApp UX guy. He’s a web3 developer with extensive front end and UX experience. He’s also the founder of the Koin Press where he writes a regular newsletter, hosts the Koin Press podcast and helps others make their dApp ideas a reality.Talking PointsWhat is the blockchain?The different types of blockchains and the differences between them?How the blockchain relates to cryptocurrency.What is a dApp and Luke’s experiences in building them.How data is stored on the blockchain and how it can be accessed.LinksLuke’s WebsiteKoinos BlocksKoinerEtherscanConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

When data science first became the must-have skill of the 21st century, organisations were fighting to recruit the best and brightest data science talent. But the glory of having a data scientist on staff was often short-lived, as many organisations soon found they didn’t know what to do with them.Business leaders had been sold the dream of being able to turn their data into business gold but were unable to maximise the value of the data science expertise they had brought in because they couldn’t communicate effectively with their new data science teams.In this episode, Dr Howard Friedman joins Dr Genevieve Hayes to discuss how adopting a customer mindset can help business leaders capitalise on the hidden value of data.Guest BioDr Howard Steven Friedman is a data scientist, health economist, and writer with decades of experience leading data modelling teams in the private sector, public sector and academia. He is an adjunct professor, teaching data science, statistics, and program evaluation, at Columbia University, and has authored/co-authored over 100 scientific articles and book chapters in areas of applied statistics, health economics and politics. His previous books include Ultimate Price and Measure of a Nation, which Jared Diamond called the best book of 2012.Talking PointsHow Howard’s personal experiences informed the writing of Winning with Data Science.What business leaders should know, in order to be effective customers of data science teams.How important is it for business leaders to be up to date with the latest data science trends and buzzwords?What data scientists should know in order to work more effectively with business leaders.Howard’s previous book, Ultimate Price.How data scientists and economists go about placing a price on human life.LinksConnect with Howard on LinkedInHoward’s websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE