Episode 83: [Value Boost] How to Gamify Data Science Requirements Gathering for Better Results
Stakeholder requirement gathering is often one of the most dreaded parts of data science projects - dry, tedious sessions where conflicting voices talk past each other and senior executives dominate the conversation. Yet without proper requirements, data science projects are doomed to fail due to solving the wrong problems or missing critical business needs.In this Value Boost episode, 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
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Episode 82: Why You Should Start Your Data Projects with Pictures Not Data
Most data scientists follow the same predictable process: gather requirements, collect data, build models, and only at the very end create visualisations to communicate results. This traditional approach seems logical, but what if it's actually working against us? In this episode, David Cohen joins Dr. Genevieve Hayes to reveal how flipping the script on data visualisation - moving it to 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
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Episode 81: [Value Boost] How to Frame Data Problems Like a Decision Scientist
Data science training programs often jump straight into technical methods without teaching one of the most critical skills for project success - problem framing. Without proper framing, data science projects are doomed to fail, right from the start, as data scientists find themselves solving the wrong problems or building models that don't address real business decisions.In this Value 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
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Episode 80: Why Decision Scientists Succeed Where Data Scientists Fail
Most data scientists have never heard of decision science, yet this discipline - which dates back to WWII - may hold the key to solving one of data science's biggest problems: the 87% project failure rate. While data scientists excel at building models that predict outcomes, decision scientists focus on modelling the actual business decisions that need to be made - a subtle but crucial 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
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Episode 79: [Value Boost] The Win Win Data Product Validation Strategy
One of the biggest risks for independent data professionals is spending months or years developing a product or service that nobody wants to buy. The graveyard of failed data science projects is filled with technically brilliant solutions that solved problems no one actually had, leaving their creators with empty bank accounts and bruised egos.In this Value Boost episode, Daniel Bourke 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
About Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
Are you tired of spending hours mastering the latest data science techniques, only to struggle translating your brilliant models into brilliant paychecks?
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• Creating data solutions that bosses can’t ignore;
• Bridging the gap between data geeks and decision-makers;
• Charting your own course in the data science world;
• Becoming the go-to data expert everyone wants to work with; and
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From algorithms to autonomy - it's time to drive your value in data science.
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