Value Driven Data Science

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.LinksBluebird 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

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

Genevieve Hayes Consulting Episode 59: [Value Boost] How Data Scientists Can Get in the AI Room Where It Happens Everyone’s talking about AI, but the real opportunities for data scientists come from being in the room where key AI decisions are made.In this Value Boost episode, technology leader Andrei Oprisan joins Dr Genevieve Hayes to share a specific, proven strategy for leveraging the current AI boom and becoming your organisation’s go-to AI expert.This episode explains:How to build a systematic 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 Bio Andrei 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. Links Connect with Andre on LinkedInAndrei’s websiteAgent.ai website Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE Read Full Transcript [00:00:00] Dr Genevieve Hayes: Hello, and welcome to your value boost from Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and I’m here again with Andrei Oprisan. Head of engineering at agent.[00:00:21] ai to turbocharge your data science career in less time than it takes to run a simple query. In today’s episode, we’re going to explore how data scientists can leverage the current AI boom to accelerate their career progression. Welcome back, Andre.[00:00:40] Andrei Oprisan: Thank you. Great to be here.[00:00:41] Dr Genevieve Hayes: So as I mentioned at the start of our previous episode together, we are at the dawn of an AI revolution with unprecedented opportunities for data scientists.[00:00:51] Now, through your current role at Agent. ai, and prior roles at AI centric companies, such as OneScreen. ai, you’ve clearly managed to capitalize on this AI boom, and are actively continuing to do so, and have managed to build a very impressive career for yourself, partly as a result. Now, the Internet’s full of career tips, but they’re usually very generic advice from career coaches who’ve never worked in the data science or technology space, and their advice usually doesn’t take into account the specific context of the AI landscape.[00:01:35] What’s one specific strategy that data scientists can use right now to leverage the AI boom for faster career progression?[00:01:44] Andrei Oprisan: I would say first building some expertise and prompt engineering and AI model evaluation. I think that’s a foundation on top of that. I think it’s developing some systematic approaches for comparing different models outputs on domain specific tasks and then creating something maybe like a reliable evaluation framework.[00:02:05] For example, you could create an eval set. Or tasks in a field and developing some quantitative or qualitative metrics to assess how different models perform compared to traditional approaches and that can really position you as someone who can actually properly integrate AI tools into existing workflows while having that element of scientific rigor.[00:02:28] , it’s leveraging the existing trends around prompt engineering around the different models that are coming up every week, every month. Every quarter and figuring out, how we are going to showcase when to maybe use 1 versus another with the scientific approach with again, I would start as simple as.[00:02:47] An eval from the kind of work that you’re doing in your current role or organization, or thinking about adjacent organizations and adjacent kind of strategies to then create some examples of when and when you wouldn’t. Use certain models because of, some numbers where you can show in an email that, this model does really well in this kind of let’s say, classification in this specific domain versus. One that doesn’t . I think from there, you can iterate and do some even more interesting work very repeatedly and looking at some adjacent domains and apply the same sort of technical solutioning to other domains.[00:03:26] Dr Genevieve Hayes: I read an article recently that was written shortly after the launch of the DeepSeek LLM. And there was a group of researchers at a university that were evaluating the model. And they had a series of prompts that could be used to find out, can this model be used to produce offensive or dangerous information?[00:03:49] And they had something like 50 prompts and they randomly chose 10 of them and ran it against that. Is that the same sort of thing that you’re proposing, but obviously specific to the person’s organization?[00:04:03] Andrei Oprisan: That’s exactly it. So I think starting as simple as again this prompt engineering and writing out a few of those prompts and be able to get some kind of repeatable answer, whether it’s a score, whether it’s, selecting from a set of options, just anything that you can then repeat and measure in a Quantitative way[00:04:24] and like, we can say, okay, it is this category, we’re getting with these, let’s say 50 prompts we’re consistently getting, 10 percent of the answers are incorrect, but 90 percent where we’re getting this kind of consistent answer and an answer that can actually be useful.[00:04:40] And then looking at different kinds of models and and then figuring out, how do they form? But also, how might you improve that? And apply some level of scientific method thinking around, ultimately, what can you change to improve? Essentially, what are still these for most folks, black boxes these LLMs that, And go something outcome, something else, and maybe demystifying what that looks like in terms of consistency at the very least in terms of accuracy over time.[00:05:12] And then, it could even take on more advanced topics. Like. How can you improve those results once you have a baseline starting point, you can say, okay, sure. Now, here’s how I improved, or here’s how maybe the prompts were. Incorrect or, they behave differently given a different LLM or, maybe you push different boundaries around context window size on the Google models are not the best.[00:05:38] But they’re the best at dealing with large data sets. there’s a trade off at a certain point in terms of speed and accuracy and cost.[00:05:47] And so then introducing some of these different dimensions, or maybe only looking at those in terms of, you know, yes, if this LLM takes 10 seconds to get me a 98 percent accurate answer, but this other one takes half a second to give me a 95 percent accurate answer, which one would you choose and a business context essentially the faster one that is a little bit cheaper.[00:06:11] Might actually be the right answer. So there’s different kinds of trade offs, I think, given different kinds of context. And I think exploring what that might look like would be a really good way to kind of apply some of those technical skills and looking at some of those other dimensions, around things like pricing and runtime execution time.[00:06:31] Dr Genevieve Hayes: And I can guarantee if you take a strategy like this, you will become the AI expert in your office, and you will be invited to every single AI centric meeting the senior management have forevermore because I did something similar to this it was before LLMs. It was with those cloud cognitive service type APIs.[00:06:50] And anytime one of those came up, I was the person people thought of. I got invited to the meeting. So, this is really good career advice.[00:06:59] Andrei Oprisan: And really, it starts, I think, growth especially think about how do you grow your career as a technical person? Obviously, part of it is being in the right room at the right time to be able to ask the right kinds of questions to be able to present a technical perspective. And again, I think by pushing on some of these boundaries you get exposed to even bigger.[00:07:20] Opportunities and bigger challenges that do need technical solutions that do need someone with a technical mind to say, You know what? Maybe that doesn’t make sense. Or maybe there is a way to leverage a I, for this problem, but not maybe in the way that you’re thinking, and I think being able to at least present that perspective is incredibly valuable.[00:07:39] Dr Genevieve Hayes: And regardless of which industry you’re working in, the secret to success is you’ve got to get in the room where it happens, as the Hamilton song says, and this sounds like a really good strategy for getting there with regard to LLMs.[00:07:53] That’s a wrap for today’s Value Boost, but if you want more insights from Andre, you’re in luck.[00:08:00] We’ve got a longer episode with Andre where we discuss how data scientists can grow into business leadership roles by exploring Andre’s own career evolution from technology specialist to seasoned technology leader. And it’s packed with no nonsense advice for turning your data skills into serious clout, cash and career freedom.[00:08:23] You can find it now, wherever you found this episode, or at your favorite podcast platform. Thanks for joining me again, Andre.[00:08:31] Andrei Oprisan: for having me. This is great.[00:08:33] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been Value Driven Data Science. The post Episode 59: [Value Boost] How Data Scientists Can Get in the AI Room Where It Happens first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

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

Genevieve Hayes Consulting Episode 58: Why Great Data Scientists Ask ‘Why?’ (And How It Can Transform Your Career) Curiosity may have killed the cat, but for data scientists, it can open doors to leadership opportunities.In this episode, technology leader Andrei Oprisan joins Dr Genevieve Hayes to share how his habit of asking deeper questions about the business transformed him from software engineer #30 at Wayfair to a seasoned technology executive and MIT Sloan MBA candidate.You’ll discover:The 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 Bio Andrei 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. Links Connect with Andre on LinkedInAndrei’s websiteAgent.ai website Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE Read Full Transcript [00:00:00] Dr Genevieve Hayes: Hello, and welcome to Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and today I’m joined by Andrei Oprisan. Andrei is a technology leader with over 15 years of experience in software engineering.[00:00:24] 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. In this episode, we’ll be discussing how data scientists can grow into business leadership roles by exploring Andre’s own career evolution from technology specialist to seasoned technology leader.[00:00:55] And more importantly, we’ll be sharing specific steps that you can take to follow his path. So get ready to boost your impact, earn what you’re worth, and rewrite your career algorithm. Andre, welcome to the show.[00:01:09] Andrei Oprisan: Thank you. Great to be here. Great[00:01:11] Dr Genevieve Hayes: We’re at the dawn of the AI revolution with everyone wanting to get in on the act and many organizations terrified of being left behind.[00:01:21] As a result, there are more technical data science and AI centric roles being advertised now than ever before. However, this also brings with it unprecedented opportunities for data scientists to make the leap into business leadership, if they’re willing and if they know how. And those are two very big ifs, because in my experience, Many data scientists either don’t know how to successfully make this transition, or write off the possibility of doing so entirely for fear that it’ll take them too far away from the tools.[00:01:55] Now, Andre you started your career as a software engineer, but have since held a number of technology leadership roles, including VP of Engineering at Liberty Mutual Insurance, Chief Technology Officer at OneScreen. ai, And your current role is head of engineering at agent. ai. What is it that first started you on the path from technical specialist to business leader?[00:02:21] Andrei Oprisan: question. So for me, it was all about asking deeper questions as to the why and that led me to ask them more questions, you know, but why and why again, why are we doing this? Why are we prioritizing this kind of work? What makes us believe this is the right kind of feature, to work on as a developer which inevitably leads to some kind of business questions some questions about. Who the customer is and why we’re serving those customers are those customers, right? Kinds of customers. To serve in the 1st place, or, should we be thinking about different kinds of customer personas?[00:02:56] And what does that mean? All the way to, how do you actually make money as a business? Why are we doing this? Is it to drive efficiency? Is it to serve a new, on top market potentially? And so. As you mentioned, I started as a developer, I started my career at Wayfair back in the early days when they were, I think it was engineer number 30 company of 100 or so people back in the early 2000s.[00:03:20] And we were. Developing big features. I remember I own a big part of baby and wedding registries and checkout and customer reviews. And I was building more and more features and I was sitting and also in more meetings with product managers who are usually the kind of the interface right in a tech world to sort of the business.[00:03:42] And I kept asking more and more questions around it. Hey, but why are we doing this? Why are we solving for baby registries? Why are we solving for wedding registries?[00:03:51] So again. For me, it really started from early days of my career, all the way through later stages, where I was always asking more questions about, is it the right thing?[00:03:59] The highest value thing that we can work on as engineers, as developers, as technical folks, or is there something more valuable that we should be working on that we should be aware of? That we should be asking deeper questions about. And it really started with that kind of inquisitive nature, always asking, why are we doing this?[00:04:16] You know, I’m here as part of this team, and I want to understand why we’re doing these things. So I can be more effective. So I can make sure that, I. Do as much as possible to make a successful[00:04:27] Dr Genevieve Hayes: That approach of asking all those why questions, that’s what they recommend to people in pretty much every management consulting advice book. The three. of Management Consulting. Why this? Why now? Why me? Did you pick that up from reading some sort of Management Consulting book or do you just have an naturally inquisitive nature?[00:04:48] Andrei Oprisan: now for me it was more natural, maybe a bit stubborn, maybe depending on what you ask, maybe a bit , irreverent just to sort of asking the question. So, , why are we doing this? But as a developer, as you’re building out features, you can build a very simple version of an ask or you can build something very complex that needs to scale. That needs to take into account a number of different kinds of factors. And so we really started with. Trying to understand, okay, what is the actual technical requirement and why do we think that is[00:05:16] and that’s usually defined by some kind of either tech lead and a team or a product manager or some combination thereof. And I found that to be very helpful, both for me and those non technical counterparts to ask those why questions because it really revealed a lot of the assumptions that went into the road map that went into even the business thinking there’s obviously some assumption that.[00:05:41] For instance, we’re going to invest in scale from a dev ops standpoint, for example to make sure these servers don’t tip over. We’ll be able to handle more traffic because we expect growth. Okay. But when is that? Why is that?[00:05:53] And it started from me, just not really understanding the business and wanting to learn and more wanting to learn on a deeper level to say, okay. I can understand. I became an expert in baby and wedding registries and all the competitors and I think that that’s part of what’s necessary to be able to build.[00:06:12] Good products that kind of obsession, with the product and , asking questions until you really understand the landscape and what you should and shouldn’t be building. I think those are critical aspects of knowing what to build and not to build to be able to.[00:06:26] And get some better outcomes.[00:06:28] Dr Genevieve Hayes: And so by asking these questions, did senior leadership see that as a sign that you had management or leadership potential and then did you naturally get promoted or did you actively seek out those business leadership roles?[00:06:44] Andrei Oprisan: I think a little bit of both, but more likely in the beginning. It was more the former, so I was asking. More of the questions for the sake of the questions and really wanting. To build a better product, which then led to just more responsibilities. And it was clear to me that I wanted.[00:07:02] Those kinds of questions to be asked and answered. And many times they want, many of those sort of technical conversations they were having, those kinds of questions weren’t really asked by the technical folks. And so I became the kind of person that would always ask those questions and always.[00:07:19] Push us to get good answers to those questions and really test those assumptions over time, as I became more senior in my roles building more complex systems that led to more complex questions that needed answers and increasingly got in front of more senior folks.[00:07:37] So what became conversations Within a team with a product manager or a junior product manager talking to junior engineers became conversations, between senior engineers. And directors of thought up and things like that. And so, I just became part of. In those rooms where those conversations were happening at a higher level that led me to ask more important white questions more around.[00:08:01] The business strategy, why do we think this is the right segment to tackle? Why do we think we’re going to build technology that is really differentiated, that is not just another solution that we could have just bought off the shelf.[00:08:13] And those are very interesting conversations to have. And I think that the kinds of conversations that we don’t get to really have, we’re not really focused on both the technical, but not technical just for the sake of technical sort of solutioning, but technology in the service of the business and the service of a business that is, wanting to grow and stay competitive and and be able to win at whatever the business is trying to do,[00:08:40] Dr Genevieve Hayes: It sounds like your nature made you very well suited to a business leadership role, even though you started off as a technical specialist. But I’ve met a lot of data scientists over the years who are very adamant that they don’t want to move away from purely technical roles and into leadership roles.[00:09:01] For example, I’ve been in teams where the team leader role has It’s been advertised and every single technical person in that team has refused to apply for it because they don’t want to move away from the tools. Is this something that you experienced early in your career?[00:09:19] Andrei Oprisan: definitely, and that’s part of every individuals journey as we’re moving through those individual contributor ranks. There are levels to the individual contributor roles, you can go from junior to very senior, to principal or staff or a member of technical staff and different companies have the sort of laddering that can even go up to the equivalent on the sort of management side, all the way to VP levels Microsoft is famous for, their laddering where you can have Distinguished engineers that are the equivalent of VPs will have hundreds of people who are reporting to them and have similar compensation structures.[00:09:55] So, again, it is possible. Not every organization is set up for that. And so I think part of this has to 1st, start with the right level of research and say, okay. If I’m the kind of person that wants to do only technical work. Will the career progression and this organization really support my objective,[00:10:14] if the most senior level that you can go to might be just a senior engineer level, that might be okay. And that might be the right place for you. But if you want me more responsible and we want to be more of an architect or someone who. Is coordinating, larger, project deployments across multiple divisions,[00:10:37] I would say, figure out if the organization. As those kinds of opportunities, and in many cases, they don’t, because they don’t know that I need, it hasn’t been proven as an actual need. So, part of it is, how comfortable are you? And being that sort of trailblazer and taking some risks and, of crafting your own role versus, working within the existing bounds where you may have a well defined ladder.[00:11:03] And, in other cases, it might be that, no, there is a ceiling and in many organizations, that is the case, especially in a non technology companies, and companies that certainly have a technology or it department and some fashion. But they might not have, the same level that you can go to.[00:11:21] Compared to in a potential business role and that needs to be a decision that is that made to say, okay, is this the right kind of place for me? Can I grow and learn? To the level that I’m looking to grow and learn to and then figure out, if you can sort of.[00:11:36] Move beyond some of those limitations, what are they and what are you comfortable with?[00:11:41] Dr Genevieve Hayes: Early in my career, it was the case that basically in Australia, if you wanted to get beyond a very moderate salary, you had to go into management if you’re a technical person. But. In recent years there are an increasing number of companies and organizations that are building in that technical stream.[00:12:03] I think Deloitte in Australia now does have a technical stream where you can get quite senior. And I know of some government organizations that also do. I’m not quite sure how well that works in practice, but it’s a move in the right direction.[00:12:20] Andrei Oprisan: Right, and I think that’s that’s only increased over time. I’ve only seen companies create more opportunities for those very senior technical folks, not fewer. So, again, I think it is encouraging, but I’d also say, you’re not going to find the same.[00:12:36] Leveling across the board for technical folks as you would, let’s say for management oriented and at a certain point, need to make the decision in terms of. Do you want to stay as an individual and the whole contributor, or are you open to management?[00:12:51] It doesn’t mean from a management standpoint, you’re not technical or, you’re not needing to your technical skills, but it may mean that, yes, you’re no longer coding every day. Right, you are maybe at best reviewing architecture documents and really pressure testing the way the systems are designed and having bigger conversations around, cost optimization and.[00:13:14] Privacy and security implications of the work that is being done and making sure that then those are addressed. Which again, there are different kinds of challenges. They’re still technically challenging. And you’re going to need good advice from additional folks, individual contributors on the teams, but they are different.[00:13:32] Dr Genevieve Hayes: The other thing I’d add to all this is, even if you choose to remain in that individual contributor stream, as you move up the ranks, you are still going to be associating more and more with senior leadership and having to think about things from a business point of view. It doesn’t matter whether you’re managing staff or not.[00:13:51] You need to become more business centric. And that idea that a lot of very technical data scientists have of just being left alone in a room to code all day. That’s not going to happen once you get above a certain level regardless of if you’re technical or a leader.[00:14:10] Andrei Oprisan: That’s right, and I think it’s. Figuring out the right balance of enough technical work, and that can mean different things over time with enough. Organizational impact, which is another way to look at the business elements of. You know, we’re doing a bunch of work, but again, is it making money?[00:14:29] Is it helping our customers get more of what they need? Is it improving some kind of output that the organization is measuring. If we can’t answer any of those questions , to some level of sophistication, then, if we’re working on the right thing or not, would we even know,[00:14:45] and would it even about it may be a very interesting technical problem, of course, but does it matter at all? will anyone even see it when you care? I think by, understanding the business understanding, maybe how many eyeballs. The product is going to get in front of and what the assumptions are and even, coming up with some of those numbers is going to really affect what you’re thinking about what you’re building and why you’re building.[00:15:09] Dr Genevieve Hayes: It sounds like you making that transition from being a technical expert to being a business leader was very organic for you, but was there ever a point in time where you actually consciously thought, okay, I’m actually focusing on this business leadership thing. I’m no longer a technical specialist.[00:15:28] I am a data science or engineering leader.[00:15:32] Andrei Oprisan: Yes, when I transitioned from Wayfair I work for an eCommerce consulting shop. So there is where I learned a lot of my sort of consulting skills and really understand how to talk to. Chief marketing officers and CEO. So understand, what exactly are you trying to accomplish?[00:15:48] But in those conversations, it became very clear to me that I needed to understand more about the business, not less, even as I was very technical, I was a tech lead, I was running the technology team, in charge with the recruiting with defining the staffing plans and also architecting some of the solutions.[00:16:10] And so it became very clear that I needed to understand even more. About what the actual goals were of the organization, because the very first iteration of the project we came in with the wrong assumptions completely, and we came up with some technical solutions that made no sense for where they were trying to go.[00:16:30] 2, 3, 5 years later we came up with something that made sense for a proof of concept and sort to get to an initial contract. But actually, we were setting them up for failure in 4 to 5 years were actually the solution that we were proposing wouldn’t be able to support the kinds of customization as they would need when they moved to 20 different supply chain partners and just having those conversations at a, higher level[00:16:57] It was very eye-opening when I walked out of a few of those meetings. Understanding that 90 percent of our assumptions were just incorrect. It’s like, Oh my God, what are we doing? And why are we having this entire team of engineers building these features for, I think it was Portugal and Spain stores where, we were just expected to lift and shift that for Japan, and that we’re just not going to be possible said, okay,[00:17:22] This made absolutely no sense. Let’s have deeper conversations about. The business what their goals are and how the technology is going to support that both now in the very short term, and we’re applying a very short term kind of mentality. But also long term also in 4 to 5 years, assuming the business is successful and they meet their objectives.[00:17:44] How can we make sure we’re enabling their long term growth?[00:17:48] Dr Genevieve Hayes: So it sounds like if one of our listeners wanted to follow your lead and move from technical specialist into a business leadership role, one of the first steps that they should take is to understand the objectives and goals of their organization and how their work can feed into achieving those goals and objectives.[00:18:09] Andrei Oprisan: Absolutely. I think it’s just having those simple questions answered around. What is the business? What is it doing? Why is it doing it? Why are they in this specific sector now? How has this evolved? And then being able to answer, how are they actually able to do that? Is it people?[00:18:28] Is it process? Is that technology is probably a combination of all of those different factors, but technology can have a multiplying effect, right? And I think it’s asking those questions in terms of where they are now and looking at different ways of expanding different ways of providing. Goods and services and using technology to more efficient.[00:18:49] And , it’s just looking at the business, but I would call it. A common sense approach and asking the kinds of questions. Okay. Someone in on the business side, if they can’t answer things in a simple. Way ask more questions if you can understand them in the terms that.[00:19:08] They’re giving back to you then then ask more clarifying questions. Don’t just assume. Right and it’s okay to not be an expert in those things. The challenge that I had in the beginning was getting frustrated with. My blind spots and my lack of really understanding I think it was.[00:19:24] You know, 1 of the early examples was this around tax treatments and, how obviously. Different territories have different rules for when and how you collect taxes.[00:19:34] It gets into a lot of complexity, but, it was very eyeopening. To ask more of those questions and to understand just how complex of an environment the business operates in, which allowed me to be a better developer, which allowed me to be a better team lead, which allowed me to then be a better partner, frankly, to those business folks who, you know, they have the same goals for the organization that we should have.[00:19:59] The company is going to grow. And if the company grows and it does well, then it means good things for everybody on the team. And if they don’t, that’s going to lead to equally bad things for everybody on the team. And so I think part of it is having that ownership mindset of it’s not someone else’s problem.[00:20:16] If we don’t understand this, it’s my problem. It’s my problem that we don’t understand how we’re going to need to customize this types engine. Because we might get hit with fines and we might need to retroactively as a severity one drop everything now. Anyways, kind of issue later than the line,[00:20:34] Dr Genevieve Hayes: So what is the single most important change our listeners could make tomorrow, regardless of whether their role is purely technical or not, to accelerate their data science impact and results and increase their business exposure?[00:20:47] Andrei Oprisan: I would say, ask, those deeper questions and figure out exactly the kind of work that they’re doing, how it’s having an impact on the bottom line. Whether it does or not, I think, understanding that very well understanding whether or not, the group that you’re in and the division is seen as a cost center or not or revenue center.[00:21:05] I think that’s the biggest sort of eye opening question that you can get answered and figure out, what are the broader objectives? Well, there are technical objectives. That the team has or business objectives that the whole division has and figuring out, okay, am I playing a part in that today or not?[00:21:26] Are we directly or indirectly? And how are my bosses or my bosses, bosses seeing the impact of the work that I’m doing in relation to the business success? And if there is no pathway for that, I think it’s the wrong kind of role in terms of long term growth. So again, if the work that you’re doing doesn’t have a measurable impact on that bottom line or on the growth of the organization, I think it’s worth asking deeper questions as to why that is or why it’s seen that way and how you can get into the kind of role that can help it.[00:22:03] With the growth and resiliency of the business.[00:22:06] Dr Genevieve Hayes: For listeners who want to get in contact with you, Andre, what can they do?[00:22:10] Andrei Oprisan: Sure. Can email me at Andre at agent.ai. Can find me on the web at oprisan.com. My blog is linked there as well. I’m on LinkedIn and x and. All the social networks with the same handles but more importantly, just, find me on agent. ai where I spend most of my time building AI agents helping out in the community giving folks feedback on how to build better agents.[00:22:35] And ultimately aiming to democratize AI and make it more accessible.[00:22:40] Dr Genevieve Hayes: And there you have it, another value packed episode to help turn your data skills into serious clout, cash, and career freedom. If you enjoyed this episode, why not make it a double? Next week, catch Andre’s value boost, a five minute episode where he shares one powerful tip for getting real results real fast.[00:23:01] Make sure you’re subscribed so you don’t miss it. Thank you for joining me today, Andre.[00:23:05] Andrei Oprisan: Thank you. Great to be here.[00:23:07] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been Value Driven Data Science. The post Episode 58: Why Great Data Scientists Ask ‘Why?’ (And How It Can Transform Your Career) first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

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

Genevieve Hayes Consulting Episode 57: [Value Boost] 3 Game-Changing Questions to Save Your Data Science Presentations From Falling Flat Every data scientist knows the sinking feeling: you’ve done brilliant technical work, but your presentation falls flat with stakeholders.In this Value Boost episode, communications expert Lauren Lang and data analyst Dr Matt Hoffman join Dr Genevieve Hayes to share their go-to pre-presentation checklist to ensure that sinking feeling never happens again.You’ll walk away knowing:The 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 Bio Lauren 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. Links Connect with Lauren on LinkedInConnect with Matt on LinkedIn Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE Read Full Transcript [00:00:00] Dr Genevieve Hayes: Hello and welcome to your value boost from value driven data science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy and financial reward. I’m Dr. Genevieve Hayes, and I’m here again with communications expert, Lauren Lang, director of content at Uplevel, and data analyst, Dr.[00:00:24] Matt Hoffman, product manager at Uplevel, to turbo charge your data science career in less time than it takes to run a simple query. In today’s episode, we’re going to be discussing the most important questions data scientists can ask themselves before presenting any models or analysis to maximize the business impact of their work.[00:00:47] So welcome back Lauren and Matt.[00:00:50] Lauren Lang: Thank you. Glad to be back.[00:00:52] Dr Matt Hoffman: Hello again.[00:00:53] Dr Genevieve Hayes: In the longer conversation we had in our previous episode, we did a deep dive into a project the two of you recently collaborated on at Uplevel, where you combined And that’s data insights with Lauren’s communication expertise to produce significant business results.[00:01:11] After hearing your story, it made me wish that I had had the benefit of working with communications experts in the various jobs I’ve had. Unfortunately, many of the organizations I’ve worked for didn’t even have an internal communications team, or if they did, it wasn’t in their job description to assist the data science team in crafting their message.[00:01:34] Because of that, I’ve found myself creating a mental checklist of questions that I’d ask myself before presenting my work to increase my chances of success. So what I’d like to ask each of you today is what is one essential question that data scientists Can ask before delivering a presentation to transform how their analysis lands.[00:02:04] So Lauren, as a communications expert, what would your one essential question be?[00:02:10] Lauren Lang: One essential question would be, what are the business goals or OKRs that the company is currently facing? What is the single biggest Problem or initiative that the company is rallying around right now, and the reason for that is this is the world in which your audience is living in when you are doing a presentation[00:02:37] you have to understand who you’re talking to and understand what their concerns are. And what is keeping them up at night? And what are they thinking about? And what are they prioritizing? And I think sometimes we present data, but we don’t connect it back to the actual business value that our work is bringing.[00:02:56] And I think that that’s a. Negative, not only for the business, but for us as the presenters of information, it’s not showing our value in what we are contributing to the business. So, I think the more that you can contextualize how the work that you’re doing rolls up or connects to these larger business initiatives is just really important.[00:03:18] And not only knowing that yourself, but making that very explicit, to the people that you’re communicating to do they know could be a secondary question. You asked for 1, I’m giving you 2. Do they know how relevant or important? My work is to the goals of the business. Because sometimes they don’t, sometimes when things get a little bit abstract or a little bit technical or out of the general wheelhouse of what executives understand, they may not understand what goal.[00:03:49] You are trying to roll the work up into, so I think it’s really important to just make sure that you are communicating that clearly to them, even if you are very clear about what business value you’re bringing.[00:04:01] Dr Genevieve Hayes: Yeah. From my experience, I’ve seen a lot of data scientists give a presentation, which is, Hey, look at this cool model I’ve built, but they never say, and if you use this cool model I’ve built, this will save you a hundred thousand dollars per year in this expense or whatever the key metric is. They always.[00:04:22] Forget that final step.[00:04:24] Lauren Lang: Right, and that’s the one step. You never want to drop because that’s the one that gets people really excited about what you do and helps to show your value that you’re bringing to the organization.[00:04:35] Dr Genevieve Hayes: So turning to you, Matt, from your experience as a data analyst, what would your one essential question be?[00:04:42] Dr Matt Hoffman: My question would be, what’s the life cycle of whatever artifact I’m using to present on? So I’ll give you an example. If it’s going to be a presentation that I’m going to go do, and I’m standing side by side, I can have slides that are Very minimally supported because I am the voice that’s going to be sharing the information that users need to have to understand the context of the work and all of that.[00:05:11] If that deck is going to get shared out to other people asynchronously, now it’s insufficient. It doesn’t have enough explanation. It can’t be used later. Similarly, if I write a paper. It needs to be concise enough that an executive audience could read it, skim it in a minute, and be able to get some of the takeaways.[00:05:31] So really, what is the life cycle of whatever we’re creating? Who’s going to be using it? What’s their experience? What’s their technical expertise with the subject matter? By really empathizing and understanding the scope of your entire audiences really helps you make much more impactful presentations and artifacts that can support your work.[00:05:54] Setting aside how hard the work it was to do itself. I would also add that understanding that at the very beginning of your project as you’re even building your models, building your data analyst, really understanding the business problem, the business context and your users doing that throughout really helps you make more impactful data science work as well as presented out.[00:06:19] Dr Genevieve Hayes: So basically know your audience.[00:06:21] Dr Matt Hoffman: Know your audience and know the context that matters to them. And I would extremely advise that data scientists get involved in the conversations themselves and are in the rooms where some of these decisions happen to really understand their users and their audience the best.[00:06:39] Dr Genevieve Hayes: For me, the one essential question would have to be so what, which is basically building on what both of you have just said. That’s something a former boss of mine used to always ask about my work. So, I would be the data scientist who went to him with, Hey, look at this cool model that I built.[00:06:57] And before I even got to presenting to the board or the executive, he would always say to me, Hmm, that’s really nice. So what? And so that got me into that habit of, just taking the final step and saying, So. This is a cool model and so it will save you lots and lots of money,[00:07:18] and yeah, big surprise once I started asking myself that the presentations that I was giving ended up being more successful. So, that is my one essential question so, in summary, the three questions we’ve got that every data scientist should ask before , every presentation what are the key metrics that your stakeholders are focused on Who are your stakeholders that will be reading whatever you produce from your work and so what?[00:07:52] How do you connect your work back to those key metrics for those stakeholders that you’ve identified? And sounds like if you do that, then you’re on a path to a winning, successful deliverable.[00:08:08] Lauren Lang: I like it. Yeah.[00:08:11] Dr Genevieve Hayes: Okay, so that’s a wrap for today’s value boost, but if you want more insights from Lauren and Matt, you’re in luck. We’ve got a longer episode with them where we dive deeper into their strategies for transforming complex technical findings into compelling business narratives. And it’s packed with no nonsense advice for turning your data skills into serious cloud cash and career freedom.[00:08:38] You can find it now wherever you found this episode or on your favorite podcast platform. Thanks for joining me again, Lauren and Matt.[00:08:48] Lauren Lang: Thank you so much.[00:08:49] Dr Matt Hoffman: Thanks for having us.[00:08:50] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been Value Driven Data Science. The post Episode 57: [Value Boost] 3 Game-Changing Questions to Save Your Data Science Presentations From Falling Flat first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

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

Genevieve Hayes Consulting Episode 56: How a Data Scientist and a Content Expert Turned Disappointing Results into Viral Research It’s known as the “last mile problem” of data science and you’ve probably already encountered it in your career – the results of your sophisticated analysis mean nothing if you can’t get business adoption.In this episode, data analyst Dr Matt Hoffman and content expert Lauren Lang join Dr Genevieve Hayes to share how they cracked the “last mile problem” by teaming up to pool their 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 Bio Dr 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. Links Connect 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 Read Full Transcript [00:00:00] Dr Genevieve Hayes: Hello, and welcome to Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and today I’m joined by Lauren Lang and Dr. Matt Hoffman. Lauren is the Director of Content for Uplevel and is also a Content Strategy Coach for B2B marketers.[00:00:26] Matt is a Data Analyst and Product Manager at Uplevel and holds a PhD in Physics from the University of Washington. In this episode, we’ll uncover proven strategies for transforming complex technical findings into compelling business narratives that drive real organizational change. So get ready to boost your impact, earn what you’re worth, and rewrite your career algorithm. Lauren, Matt, welcome to the show.[00:00:55] Lauren Lang: Hi Genevieve, thank you so much.[00:00:57] Dr Matt Hoffman: Thanks for having us. Excited to be here.[00:01:00] Dr Genevieve Hayes: In logistics, there’s a concept known as the last mile problem. Which refers to the fact that the last stage of the delivery process of people or goods is typically the most complex and expensive while also being the most essential. For example, it’s typically easier and cheaper to fly a plane full of packages from Australia to the U.[00:01:22] S. than it is to transport those packages by road to their final destinations within the U. S. Yet if you can’t distribute those packages once they arrive in the U. S., they may as well have never left Australia. It’s for this reason that supply chain managers typically focus a disproportionate amount of effort on planning those final miles.[00:01:43] Data scientists also face their own last mile problem. Despite many data science projects requiring sophisticated modelling and analysis techniques, the most difficult part of data science is often communicating the results of those projects to senior management and gaining adoption of the project from the business.[00:02:04] That is the final stage. Yet, unlike in logistics, This is also the stage where data scientists typically focus the least amount of effort, much to the detriment of their work and their careers. Lauren and Matt, the reason why we’ve got both of you as guests in today’s episode is because you’ve recently backed this trend and pooled your combined experience in communications and data science with outstanding results.[00:02:33] And this is actually the first time I’ve come across a data scientist working directly with the communications expert to address the data science last mile problem. Although, it probably should be far more common. So to begin with, Matt, can you give us an overview of the data science project you were working on and how you came to team up with Lauren when delivering the results?[00:02:57] Dr Matt Hoffman: So we work at Uplevel and Uplevel is a company that pulls in data about software engineers and we help tell those data stories to our customers. Senior leaders of engineering, like software engineering firms so that they can make data driven decisions and drive change within their organizations.[00:03:17] One of the things that’s really come up in the past year is this full topic of gen. AI software engineers being able to talk to an AI assistant to help them write code and the thinking was, oh, this is a silver bullet. We’re just going to be able to. Turn on this system. Our developers are going to be more productive.[00:03:36] Instantly. The code is going to get better. There’s going to be nothing but greenfield. If we just turn this on, it’s a no brainer, we heard those questions and we don’t develop our own gen AI tool. But what we do have is data about software engineers and how they spend their time, the effectiveness of their work.[00:03:54] Are they able to deliver more? Are they getting more things done? How’s the bug rate of their code? So it was natural for us to go explore that problem and really try to understand what is the impact of Gen AI on software engineers. That’s the problem that we were facing. So I work with our data science team.[00:04:13] I’m not actually on our data science team, but worked with them to go do this analysis to really try to understand how do people compare to themselves and what changes do we see within this. And then we pulled in Lauren to go start showing off what we found. And that’s where that story kicked off.[00:04:32] Dr Genevieve Hayes: Prior to working with Lauren, what are some of the challenges you encountered in communicating the results of your analysis?[00:04:38] Dr Matt Hoffman: Well, it’s always a tricky one when the answer is complicated. The real fundamental place that we at Uplevel are at is that this is human data. While we may be able to measure timestamps to a millisecond, This is all still predicated that this is people data and people do weird things. And the data is messy and the data is muddy.[00:05:03] So there’s the constant battle of, well, what can we trust? We’re looking for correlations and, you know, you squint to see if like, there’s something there you peel back a layer and then there’s something more, but people data is hard to work with. So that’s really a skill of our data science team to help pull that back.[00:05:20] But we were. Kind of struggling to make heads and tails of what were the real conclusions. And Lauren really helped clarify that story for us and get that communication there.[00:05:30] Dr Genevieve Hayes: People are irrational. I mean that’s the big problem with us. Before you did this, had you ever made some massive mistake because you just assumed people were rational when they worked?[00:05:44] Dr Matt Hoffman: It’s funny stuff so sometime when some work’s becoming delayed and you go ask for the root cause and it’s like, oh, someone’s saying, I thought I did that and I forgot. Like, I never hit the button. That’s the kind of, people data that we see is that, like, yeah, that happened.[00:05:59] It was late, but that was just because you forgot to hit the button. People’s behavior is really funny. So yeah, we just have to kind of take that into account that everybody’s different. That’s okay. And we need to bake that into our analysis, that people work differently and not try to over fit one model that applies to everybody .[00:06:18] Dr Genevieve Hayes: Yeah, I actually wrote a LinkedIn post a while ago saying, people are a problem with data and wouldn’t it be nice to just be dealing with mechanical processes? And I had someone reply to that post who works at a water agency where they don’t deal with people, it’s, water going through pipes, and they said, well actually mechanical processes are just as annoying, they just are annoying in different ways because you have the sensors malfunctioning and all this.[00:06:44] You can dream about not dealing with people but Machines cause problems too .[00:06:48] Dr Matt Hoffman: Yeah, that’s exactly right. So you just have to know that going in and know that it’s going to be messy. And plan for that.[00:06:56] Dr Genevieve Hayes: So Lauren, in your content strategy coaching work you’ve done a lot of work with software as a service companies. And as Matt said, Up Level itself is a company that Works with engineers and probably has a lot of engineers as its employees. So, I’d imagine you’ve worked with a lot of very technical people throughout your career.[00:07:20] Lauren Lang: I have. Yes.[00:07:21] Dr Genevieve Hayes: What are some of the biggest issues you’ve noticed in how technically minded people, especially data scientists and data analysts, present their findings to business stakeholders?[00:07:33] Lauren Lang: It’s very funny because I think that there is a lot of similarities actually between how data scientists might present their findings and how a lot of marketers present their findings. And you would think like, Oh, marketing is so much more. We have our thumb on the pulse of the business.[00:07:48] And, marketers are so much more business driven, but I think, anyone who is looking at data as marketers, we look at data too. We are. Not data scientists, but there’s a fair amount of data science, sometimes in marketing. And there’s a lot of data analysis that happens. And I think there is just this tendency sometimes to.[00:08:07] Get very myopic and get very focused on your own specific context in looking at the data and forgetting that there is probably a larger story that the data existed to tell. I see this a lot. 1 of the. Challenges that I see a lot is, marketers will go into a meeting with a CEO and they will have dashboard after dashboard and chart after chart.[00:08:31] And there is a very sort of distinct look on an executive space when. You’ve shown them three charts in a row or three dashboards and it’s like a completely blank look and you know that they are literally anywhere else. but in the conversation and it’s a little bit of like a death now.[00:08:51] And so I think for anyone who likes to geek out on data, whatever part of the business you’re in, you have to remember that there is this larger value story that you need to be telling, and you need to be showing that data and be mindful of the context in which you’re showing that data.[00:09:08] To what end? Rather than just taking people down the rabbit hole with you. I think sometimes there’s an assumption that everyone should be as interested about all of the nuances and slight, variances in the data as you are, and that’s not always the case.[00:09:24] Dr Genevieve Hayes: Yeah the way you’re describing that death knell face, yeah, I’ve seen that before. And worse than that is when the people you’re presenting to start playing with their phones. Then you definitely know that you’ve failed.[00:09:35] Lauren Lang: Might as well call it right there.[00:09:37] Dr Genevieve Hayes: Yeah, , just pack up and walk out of the room at that point.[00:09:39] Lauren Lang: That’s right. That’s right.[00:09:42] Dr Genevieve Hayes: So, I assume you’ve pointed out these issues to technical people who you’ve worked with. How do they typically respond when you say, hey, not everyone’s as geeky as you?[00:09:53] Lauren Lang: I think there’s a way to couch that in a way, because I have a lot of empathy for it. Geeky people are excited about what we do. I mean, there’s a passion there. And so you don’t want to not communicate that passion.[00:10:05] I think that’s really important. And, there’s some exciting results or, even. Not exciting results that you didn’t think were going to pan out, but there’s always a story to tell, but it’s just, can you tell it maybe at a slightly more abstract level of specificity, maybe? Or can you tell it with an understanding of the context in which your audience exists[00:10:28] I think there’s just a lot of tendency to Just forget that not everyone brings the same experiences and the same understanding and the same depth of knowledge to the table. And so the best way that the stories we tell with data can be impactful is to tell them in context and to be able to pull out the important parts that really can bring the message home.[00:10:50] Dr Genevieve Hayes: So, put yourself in the shoes of your audience,[00:10:53] Lauren Lang: absolutely. You should always have empathy with the person you’re trying to communicate to. I think it was Kim Scott said that communication happens at the listener’s ear and not the speaker’s mouth. That’s where meaning is made. It’s really important to keep that in mind as you are stepping into the shoes.[00:11:09] Of the communicator,[00:11:11] Dr Genevieve Hayes: so, I’d like to now take a deep dive into the project that the two of you collaborated on so Matt, how did you determine which insights from your analysis were most relevant for communicating with management? Are[00:11:24] Dr Matt Hoffman: So we have a set of measures at up level that are kind of part of our standard suite of analysis. So 1st, because if you can’t go explore the data for yourself and understand where your team’s at, then that’s a really unsatisfying experiment. So we knew that we wanted to look at some of these measures.[00:11:43] We’ve also been doing this for a few years now, so we do have a pretty good grasp on. You know, what are appropriate measures to look at for software engineers? And then what is completely inappropriate? That’s like, this is just not a good measure. You shouldn’t use it. It’s problematic for 1 reason or another.[00:12:01] So choosing those measures that we think. Are kind of universally applicable, are good proxies of how this experience may look, and then really trying to see what’s going to move and shift when we look at these. Those were kind of the criteria. We had a few hypotheses that we went in for how we thought things were going to move once you introduced Gen AI to the mix.[00:12:22] And we were surprised by our hypotheses, and we had to reject some of them, which was really fun. And it makes you really challenged that you’re doing it right. And then finding that this actually does go against what we thought would happen.[00:12:36] Dr Genevieve Hayes: you able to share any examples of these?[00:12:39] Dr Matt Hoffman: One of the things that we wrote about and we can share the link to our study was the general thinking was, hey, if you’re going to use Gen AI, you’re going to be able to ask questions and Jenny is going to help you write better code. So one of the things we looked at was. What’s the defect rate of code that gets merged and then it needs to get fixed later?[00:13:02] So how often does that happen? You would think that that would go down if the code is going to be of higher quality because Gen AI is helping you. Now what we found was that actually the defect rate went up. Another organization seemed to find the same thing, saying that the result of Gen AI was that there’s larger changes to code.[00:13:23] And then more things are going to get missed because the batch size is getting larger. So you might find things. four bugs, but there’s five because you’re writing bigger and bigger code changes. So we saw that the defect rate for the cohort that was using Gen AI went up by 40 percent compared to themselves, which is a pretty market change.[00:13:43] So that was one that , we were very surprised to see and are really interested to see what happens next with that as all these tools get better and better and better.[00:13:53] Dr Genevieve Hayes: insight you just described, that doesn’t surprise me because my own personal experience I’ve found with writing code using Gen AI, you can produce the code really, really fast. You’re spending. twice as long or three or four times as long debugging it, because there are all these bugs in it that would not be in there if you’d written it yourself.[00:14:14] And you’re just not used to having that many bugs to fix.[00:14:19] Dr Matt Hoffman: Yeah, and it might be not stylistic, like, the way that you think that you should write your code it might pull some solution that looks reasonable at first pass, but it’s pretty hard to debug if it’s the right thing when it, looks right, smells right, but then under the hood, there’s something wrong with it.[00:14:36] Also, Jenna, I doesn’t understand the context of the problem that you’re trying to go write code for. You have that in your head, you know where you’re at and where the destination is, and it’s going to help you write some code. But you have that.[00:14:49] Dr Genevieve Hayes: Yeah. And I’ve found it creates. Non existent Python packages and non existent Python functions, which is fun, because then you spend half an hour trying to find this package that doesn’t even exist.[00:15:02] Dr Matt Hoffman: It’s tricky. It really is. The other one that I would just briefly say that we looked at is we thought people would write code faster. That’s the statement that you just said. How quickly does it take to get from commit to merge? Does that really pick up? Because you’re using Gen AI.[00:15:16] And we found that it didn’t make much of a tangible impact. That there’s still a lot of time that’s spent when you’re trying to understand the problem of what you’re trying to solve, how you might approach it, the architecture of it. None of those things are going to go away.[00:15:31] Bottlenecks of having another human review your code, that doesn’t change whether they both have Gen AI or not. You’re still working with other people. So those structural factors do tend to be very important in this problem. And those are ones that you need to pursue and kind of conventional means of understanding how your teams work and doing better.[00:15:51] So that one didn’t move at all. And we thought that that would speed up. That was our hypothesis.[00:15:56] Dr Genevieve Hayes: Yeah, doesn’t surprise me. So, Lauren, how did you take these insights and structure them into a narrative that maximized their impact?[00:16:04] Lauren Lang: well, it was funny because even before we had done the research, we knew we wanted to do this research and we wanted to publish it. And looking from a content marketing perspective, I think original research right now is one of the most, potentially impactful formats for creating content.[00:16:23] And some of that is that, there is so much out there. That is just really bland. And I is not helping. Jenna is not helping with that. There’s a lot of content. That is just not special. It’s not differentiated. It’s not helping to educate or inform anybody or share anything new. And so when you have the opportunity to sort of lend something new to the conversation, that’s an important opportunity.[00:16:46] So we knew going in that we were going to do it. What we were not expecting were the results that we got. And I laughed a little bit when we got these results. I had a meeting with our data science team and with Matt, and., we all are sitting down and I’m like, lay it on me tell me what the results were and they were a little bit disappointed and they said, it’s kind of we’re not seeing, a big thing from Impact perspective or a data perspective, like, it’s just not that exciting.[00:17:15] And I said, oh, no, actually, this is very exciting because there were a number of factors. I think that really made this a really impactful report. 1st was just having some new original research on this topic. That is maybe the hot topic of the decade.[00:17:31] I think was really exciting. So it was like, listen, we know that people are very interested in this. We know that this is the question that they are asking, especially engineers and engineering leaders, the people who we serve from a business standpoint. They want to know is gen AI actually helping my developers be more productive.[00:17:48] And we have like some. Things that we can show around that. And then also the fact that we were able to then bring a little bit of a spiky and contrarian point of view about this because a lot of the research that’s been published already is either survey based. So, a lot of developers reporting whether or not they feel more productive.[00:18:11] Which is data as well, but, this is we’re bringing some quantitative data to bear or some of the other data was published by the. AI tools themselves, so you have to take that with a grain of salt. So, we came in[00:18:27] with this sort of interesting and different point of view. And that really, really took off for folks. And we found that some people were surprised. We found a lot of developers and engineers like you, Genevieve, who are not who said, I have been saying this all along. And this feels very validating because I think there is some anxiety among engineers that, Hey, like leadership just thinks that can be replaced.[00:18:50] But it really kicked off a really big conversation in the industry where we just said, Hey, you know, there’s a little bit of a hype cycle right now. We don’t know for sure. , we have results from one sample. There’s no big claims that we can make about the efficacy in the long run.[00:19:06] And things change very quickly. Gen AI is improving all the time, but. We do have some data points that we think are interesting to share and it really took off and it was great for us from a business perspective. It really helped take the work that we do into that last mile. And it helped make the work that we do feel very tangible and accessible for folks.[00:19:29] Dr Genevieve Hayes: So it sounds like, rather than taking a whole bunch of statistics and graphs, which would have been the output of Matt’s work. You translated those statistics and graphs into a narrative that could be understood by a person who wasn’t a data scientist or wasn’t a data analyst. Is that right?[00:19:49] Lauren Lang: Yes, we did. And our audience is primarily engineering leaders, engineering leaders are not data scientists, but they’re technical. So we identified three main takeaways. And we presented that we shared a little bit about our methodology.[00:20:03] And we shared essentially Some thoughts about what does this mean, what is the larger significance of what we found? What does this mean for you as an engineering leader does this mean that we think that you should stop adopting AI?[00:20:17] Does it mean that, right?, you should be more controlling of how your engineers are experimenting with AI. And, we don’t believe that’s the case at all. But it allowed us to sort of share some of our perspective about, how you build effective engineering organizations and what role we think I may have to play in that.[00:20:35] And, that is the larger story where data becomes very interesting because there’s sharing the data and then they’re sharing the so what around the data. So, what does this mean for me as an engineering leader? And so we really tried to bring those 2 elements together in the report.[00:20:51] Dr Genevieve Hayes: How was this report ultimately received by the audience?[00:20:55] Lauren Lang: Very well. We issued a press release around it. And I think we were picked up globally by somewhere between 50 and 75 media outlets, which. For a small engineering analytics platform, I’m pretty happy about that. It was in some engineering forums, it really became a big topic of discussion. We went sort of medium level viral. And it felt really good. It’s like, this is a really interesting topic. We accept that it’s an interesting topic.[00:21:22] We think that we have something that is very interesting to add to the conversation. So, yeah, it was good and some folks to it was great, you know, because engineering leaders are naturally skeptical. This is 1 of the most fun parts about marketing to engineering leaders that engineering leaders hate marketing.[00:21:38] So we got a few emails of folks who are like, tell us more about your methodology. And they really sort of wanted to, see behind the scenes and really, really dig in. And, that is par for the course. And we would expect nothing less[00:21:51] It was a really positive impact. I’m really glad we did it.[00:21:53] Dr Genevieve Hayes: So with all that in mind, I’d like to ask this of each of you. What is the single most important change our listeners could make tomorrow to accelerate their data science impact and results?[00:22:05] Dr Matt Hoffman: I. am very fortunate to have Lauren as an editor even when we collaborate on writing, an article I think having someone who can help you clarify and simplify your story is so important. You really do want to edit and bounce back and forth and try to distill down the most important bits of what you’re doing.[00:22:28] I tend to want to share, like, Everything, all of the details, all the gritty stuff, the exact perfect chart and it’s like, let’s simplify, simplify, simplify. And part of that conversation is also, who’s going to be receiving this? And what’s their persona? At what level are we going to explain this work?[00:22:47] Are they going to be familiar with, the methodology that we’re using? Or do we need to explain that too? So, how do we write everything at the most appropriate level and understand the life cycle of? This report that we’re doing. So having an editor would be my big one and understanding your audience would be the other.[00:23:06] Lauren Lang: I absolutely agree with everything Matt said. I think that the more that you make Sharing the results of your research, a team effort and a team sport, the more you’re likely going to succeed at it. But I think probably, and I’ll just come at it from, more of a technical perspective.[00:23:23] When you are presenting information, 1 of the things that could be very helpful is to present it at various levels of detail. So, making sure that you are presenting key takeaways or abstracts at 1 level and then. People can always double click into things and dive deeper and, you can include appendices or include links to , more of the detailed research.[00:23:47] But I think sort of having these executive summaries and really sort of being able to come at things from a very high level Can help sort of get that initial interest so that people understand quickly. what did the research find? What is the impact? And what is the context that this research was performed in?[00:24:06] Where is the business value, so, being able to connect the dots for your audience in terms of not only did we find this, but here’s what it means. And that thing that it means is actually very impactful to you and the job that you are trying to accomplish .[00:24:19] Dr Genevieve Hayes: So for listeners who want to get in contact with each of you, what can they do?[00:24:23] Lauren Lang: I live on LinkedIn. So they can look me up on LinkedIn. I think my little handle there is ask Lauren Lang.[00:24:31] Dr Matt Hoffman: Likewise, I don’t know what my LinkedIn handle is, but I’m on there. That would be the easiest way to get a hold of me on that.[00:24:39] Lauren Lang: You obviously need to spend more time on LinkedIn than Matt.[00:24:42] Dr Genevieve Hayes: Yes. And there you have it. Another value packed episode to help turn your data skills into serious clout, cash, and career freedom. And if you enjoyed this episode, why not make it a double? Next week, catch Lauren and Matt’s Value Boost, a five minute episode where they share one powerful tip for getting real results real fast.[00:25:08] Make sure you’re subscribed so you don’t miss it. Thanks for joining me today, Lauren and Matt.[00:25:12] Lauren Lang: Thank you so much for having us.[00:25:14] Dr Matt Hoffman: Thank you. It was really lovely.[00:25:16] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been value driven data science. The post Episode 56: How a Data Scientist and a Content Expert Turned Disappointing Results into Viral Research first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

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

Genevieve Hayes Consulting Episode 55: [Value Boost] Why Data Scientists are Focus-Poor (and the Software Developer’s Solution to Fix It) Have you ever noticed that software developers are frequently more productive than data scientists? The reason has nothing to do with coding ability.Software developers have known for decades that the real key to productivity lies somewhere else.In this quick Value Boost episode, software developer turned CEO Ben Johnson joins Dr Genevieve Hayes to discuss the focus management techniques 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 Bio Ben Johnson is the CEO and Founder of Particle 41, a development firm that helps businesses accelerate their application development, data science and DevOps projects. Links Connect with Ben on LinkedIn Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE Read Full Transcript [00:00:00] Dr Genevieve Hayes: Hello and welcome to your value boost from value driven data science. The podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and I’m here with Ben Johnson, CEO and founder of Particle 41 to turbocharge your data science career in less time than it takes to run a simple query.[00:00:29] In today’s episode, we’re going to be discussing techniques from software development that data scientists can use to increase their productivity and efficiency. Welcome back, Ben.[00:00:42] Ben Johnson: Hey, nice to be here.[00:00:44] Dr Genevieve Hayes: As long time listeners of this show are probably already aware, before becoming a data scientist, my background was as an actuary and statistician.[00:00:53] And then when I decided to make the move to data science, I did a master’s in computer science to upskill on machine learning and AI. And one of the things I loved most about my master’s was that my classmates were predominantly software developers and engineers. And I found that Just by being in the same classes as them and associating with them on the class online forums, I learned just as much, if not more, about what it takes to be an effective data scientist as I did from the lectures themselves.[00:01:32] And this is because the software engineers had a very different perspective on data problem solving from what I’d developed as a statistician and actuary. Ben, in addition to being a serial entrepreneur, you yourself are a software developer with over 20 years of experience. In that time, you must have come across a whole range of techniques for boosting your productivity and efficiency as a developer.[00:02:02] Are there any techniques among those that, you’re surprised, data scientists don’t also use?[00:02:09] Ben Johnson: It kind of swirls together. So focus is a currency as kind of the tagline here. So the book, the one thing has been really inspirational for me. And I’m a bullet journaler. And so I kind of take my 90 day goals and break them down into months and then the weeks, you know,[00:02:26] what’s the one thing or the finer sets of things? I find a lot of digital professionals, including data scientists are kind of multitasking and we’ve kind of even created This kind of interruption culture in the way that we work. So I find it interesting when data scientists don’t have like the Kanban board or the flow of work and they’re just kind of operating by slack messages and emails.[00:02:50] And I think then you have Low currency of focus like you’re poor in focus. And so the overarching thing here is to be rich in focus. And that means creating systems and work environment and a personal organization strategy. That makes you richer in focus.[00:03:07] Dr Genevieve Hayes: And how would you go about doing that?[00:03:09] Ben Johnson: So I think it starts with like some level of personal ceremony.[00:03:14] And some adherence to routine. So it may seem confining, but I actually find it gives me a lot of freedom. So spend a lot of time around the quarter. Thinking like, what do I want to accomplish in the next 90 days and documenting that and then breaking that out in a month and not just doing it professionally, but doing it personally as well.[00:03:34] So that then when I go to my week, I’ve kind of planned my week. I know what my focuses are for at least some of the time. I don’t like knock it all down in stone. I leave some flex time in there for. Emails and slack messages, but I definitely know what needs to be true by the end of the week for me to feel accomplished and confident.[00:03:57] And in the end, the biggest enemy is the imposter syndrome, right? So I have to have to put challenges in front of me that I’m accomplishing. Because the last thing I want anybody on my team to feel is that imposter syndrome. And the only way we were get through that is by. Proving to ourselves that we can accomplish the goals that we put in front of ourselves.[00:04:19] Dr Genevieve Hayes: What you’ve described there is very similar to the approach that I take in my work. I read Cal Newport’s deep work about, three years ago. Yeah, and one of the things I find, you know, as a data scientist, often I do have multiple projects on the go. But I try and work in deep work blocks, so I schedule three two hour blocks per day, and I actually have a kitchen timer, and for that two hour block, I will only work on one particular task, and even if I’m working on multiple topics within a day.[00:04:55] I try and only have one task per day, but just having those two hour focus blocks really helps me to accomplish a lot.[00:05:03] Ben Johnson: Yeah, I think so. And what you’re talking about there is this time compression and I think time compression is very, very powerful. And I would say most people don’t. Incorporate an element of time compression, like your timer is time compression and incorporate environment. We kind of used to be.[00:05:23] We planned the year and we give very little cadence to the quarter and the month. And then we kind of realized. You know, Q3 we’re falling behind and then that would make for these awful Q4 experiences, right? People working right up into the last day of the year kind of thing. I think we’re seeing that improve and I think time compression, EOS is really big on the quarterly planning, the monthly planning.[00:05:50] And then you mentioned like the Pomodoro technique. These things are getting really popular, but those things are awarded by an increase. Like when you’re rich in focus, those things happen, right? Or you do those things to become more rich in focus.[00:06:06] Dr Genevieve Hayes: And my experience is the days when I do manage to have those focus blocks, I’m happier at the end of the day.[00:06:12] Ben Johnson: Yep. Yeah, because you created a scoreboard and you won the day, right? You know, you won the day. Yeah. In my bullet journal, I have a habit tracker and I put so many habits on there that if I do about half of them, like I’m good, and that works for me, you know, kind of always be solving.[00:06:28] You know salespeople, they always be closing and I’m kind of like always be doing something to make my life better, even if it’s just like drinking water, right? Remembering to drink water that’s a thing on my tracker.[00:06:42] Dr Genevieve Hayes: And that’s a wrap for today’s Value Boost. But if you want more insights from Ben, you’re in luck. We’ve got a longer episode with Ben where we discuss strategies for accelerating your data science impact and results. And it’s packed with no nonsense advice for turning your data skills into serious clout, cash, and career freedom.[00:07:04] You can find it now, wherever you found this episode, or at your favorite podcast platform. Well, thank you for joining me again, Ben.[00:07:12] Ben Johnson: Oh, my pleasure.[00:07:14] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been Value Driven Data Science. The post Episode 55: [Value Boost] Why Data Scientists are Focus-Poor (and the Software Developer’s Solution to Fix It) first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

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

Genevieve Hayes Consulting Episode 54: The Hidden Productivity Killer Most Data Scientists Miss Why do some data scientists produce results at a rate 10X that of their peers?Many data scientists believe that better technologies and faster tools are the key to accelerating their impact. But the highest-performing data scientists often succeed through a different approach entirely.In this episode, Ben Johnson joins Dr Genevieve Hayes to discuss how productivity acts as a hidden 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 Bio Ben Johnson is the CEO and Founder of Particle 41, a development firm that helps businesses accelerate their application development, data science and DevOps projects. Links Connect with Ben on LinkedIn Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE Read Full Transcript [00:00:00] Dr Genevieve Hayes: Hello and welcome to Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and today I’m joined by Ben Johnson, CEO and founder of Particle 41, a development firm that helps businesses accelerate their application development, data science, and DevOps projects.[00:00:30] In this episode, we’ll discuss strategies for accelerating your data science impact and results without sacrificing technical robustness. So get ready to boost your impact. Earn what you’re worth and rewrite your career algorithm. Ben, welcome to the show.[00:00:48] Ben Johnson: Yeah, thank you for having me.[00:00:50] Dr Genevieve Hayes: One of the most common misconceptions I see about data scientists is the mistaken belief that their worth within a business is directly linked to the technical complexity of the solutions they can produce.[00:01:04] And to a certain extent, this is true. I mean, if you can’t program, fit a model, or perform even the most basic statistical analysis, realistically, your days as a data scientist are probably numbered. However, while technical skills are certainly necessary to land a data science job, The data scientists I see making the biggest impact are the ones who are not necessarily producing the most complex solutions, but who can produce solutions to the most pressing business problems in the shortest possible time.[00:01:41] So in that sense, productivity can be seen as a hidden multiplier for data science careers. Ben, as the founder of a company that helps businesses accelerate their data science initiatives, it’s unsurprising that one of your areas of interest is personal productivity. Based on your experience, What are some of the biggest productivity killers holding data scientists back?[00:02:11] Ben Johnson: I don’t know for others. I know for myself that what kills my productivity is not having an intention or a goal or a direct target that I’m trying to go for. So when we solve the science problems, we’re really trying to figure out, like, what is that hunt statement or that question that key answer you know, the question that will bring the answer.[00:02:33] And also, what is the right level of information that would handle that at the asker’s level? So the ask is coming from a context or a person. And so we can know a lot. If that person is a fellow data scientist, then obviously we want to give them data. We want to answer them with data. But if that’s a results oriented business leader, then we need to make sure that we’re giving them information.[00:02:57] And we. Are the managers of the data, but to answer your question, I think that the biggest killer to productivity is not being clear on what question are we trying to answer?[00:03:08] Dr Genevieve Hayes: That, resonates with my own experience. One of the things I encountered early in my data science career was well, to take a step back. I originally trained as an actuary and worked as an actuary, and I was used to the situation where your boss would effectively tell you what to do. So, go calculate, calculate.[00:03:28] premiums for a particular product. So when I moved into data science, I think I expected the same from my managers. And so I would ask my boss, okay, what do you want me to do? And his answer would be something like, Oh here’s some data, go do something with it. And you can probably imagine the sorts of solutions that we got myself and my team would come up with something that was a model that looks like a fun fit[00:03:59] and those solutions tended to go down like a lead balloon. And it was only after several failures along those lines that it occurred to me, , maybe we should look at these problems from a different, point of view and figure out what is it that the senior management actually want to do with this data before starting to build a particular model from it.[00:04:24] Ben Johnson: Yeah. What decision are you trying to make? Just kind of starting with like the end in mind or the result in mind, I find in any kind of digital execution there are people who speak results language and there are people who speak solutions language. And when we intermix those two conversations,[00:04:41] it’s frustrating, it’s frustrating for the solution people to be like, okay, great. When are you going to give it to me? And it’s frustrating for the business folks, like hey, when am I going to get that answer when we want to talk about the solution? So I found like bifurcating like, okay, let’s have a results or planning discussion separate from a solution and asking for that right to proceed.[00:05:02] In the way that we communicate is super helpful., what your share reminds me of is some of the playbooks that we have around data QA, because in those playbooks, we’re doing analysis just for analysis sake. I feel like we’re looking for the outliers.[00:05:18] Okay. So if we look at this metric, these are the outliers. And really what we’re doing is we’re going back to the, originators of the data and say, like, sanity, check this for us. We want to run through a whole set of sanity checks to make sure that the pipeline that we’re about to analyze makes sense.[00:05:34] Are there any other exterior references that we can compare this to? And I do know that the first time we were participating in this concept of data QA, not having that playbook Was a problem, right? Like, well, okay. Yeah, the data is there. It’s good. It’s coming in, but you know, to really grind on that and make sure that it was reflective of the real world was an important step.[00:05:57] Dr Genevieve Hayes: So QA, I take your meaning quality assurance here? Is that right?[00:06:02] Ben Johnson: Yes. That’s the acronym quality assurance, but testing and doing QA around your data pipelines.[00:06:09] Dr Genevieve Hayes: Okay, so I get it. So actually making sure the pipelines work. And if you don’t understand what is it that you’re looking for with regard to performance, then you can end up going off in the wrong direction. Is that correct?[00:06:23] Ben Johnson: So if you were analyzing sales data, you would want to make sure that your totals reflected the financial reports. You just want to make sure that what you’ve. Accumulated in your analysis environment is reflective of the real world. There’s nothing missing. It generally makes sense. We just haven’t introduced any problem in just the organizing and collection of the data.[00:06:45] Dr Genevieve Hayes: Yeah, yeah. From my background in the insurance industry, those were all the sorts of checks that we used to have to do with the data as well.[00:06:52] Ben Johnson: Well, and oftentimes the folks that are asking these hard questions, they’re not asking the questions because they have any idea how clean the data they’ve collected. They just think there might be a chance. It’s like the dumb and dumber, you know, okay, so we think we have a chance, you know anyways awful movie reference, but they think that there might be a possibility that the answer to all of their questions or this hard decision that they need to make regularly is somewhere in that pile of stuff.[00:07:21] What we call a QA analysis Also is checking the data’s integrity if it’s even capable to solve the problem. So I think that’s a great first step and that sometimes that’s just kind of analysis for analysis sake or feels that way.[00:07:37] Dr Genevieve Hayes: One of the things you’ve touched on several times is the idea of the results oriented people and the solutions oriented people and I take it with the solutions oriented people, you’re talking about people like the data scientists. When the data scientists are talking to those results oriented people, Is there a framework that they can follow for identifying what sorts of results those results oriented people are looking for?[00:08:08] Ben Johnson: It’s very similar in the way that you approach like a UI UX design. We’ve taken kind of a storyboard approach, storyboard approach to what they want to see. Like, okay, What is the question? What are you expecting the answer to be? Like, what do you think would happen?[00:08:25] And then what kind of decisions are you going to do as a result of that? And you had some of those things as well. But kind of storyboarded out what’s the journey that they’re going to take, even if it’s just a logical journey through this data to go affect some change.[00:08:41] Dr Genevieve Hayes: So do you actually map this out on a whiteboard or with post it notes or something? So literally building a storyboard?[00:08:48] Ben Johnson: Most of the time , it’s bullets. It’s more of like written requirements. But when we think of it, we think of it , in a storyboard and often it’ll turn into like a PowerPoint deck or something because we’re also helping them with their understanding of the funding of the data science project, like connecting ROI and what they’re trying to do.[00:09:10] So yeah. Yeah, our firm isn’t just staff augmentation. We want to take a larger holistic ownership approach of the mission that we’re being attached to. So this is critical to like, okay, well, we’re going to be in a data science project together. Let’s make sure that we know what we’re trying to accomplish and what it’s for.[00:09:29] Because, you know, if you’re working on a complex project and six months in everybody forgets Why they’ve done this, like why they’re spending this money oftentimes you need to remind them and, show them where you are in the roadmap to solving those problems.[00:09:44] Dr Genevieve Hayes: With the storyboard approach, can you give me an example of that? Cause I’m still having a bit of trouble visualizing it.[00:09:51] Ben Johnson: Yeah, it’s really just a set of questions. What are you trying to accomplish? What do you expect to have happen? Where are you getting this data? It’s , just a discovery survey that we are thinking about when we’re establishing the ground rules of the particular initiative.[00:10:08] Dr Genevieve Hayes: And how do you go from that storyboard to the solution?[00:10:12] Ben Johnson: That’s a great question. So the solution will end up resolving in whatever kind of framework we’re using data bricks or whatever it’ll talk about the collection, the organization and the analysis. So we’ll break down how are we going to get this data is the data already in a place where we can start messing with it.[00:10:32] What we’re seeing is that a lot of. And I kind of going deep on the collection piece because that’s I feel like that’s like 60 percent of the work. We prefer a kind of a lake house type of environment where we’ll just leave a good portion of the data in its raw original format, analyze it.[00:10:52] Bring it into the analysis. And then, of course, we’re usually comparing that to some relational data. But all that collection, making sure we have access to all of that. And it’s in a in a methodology and pipelines that we can start to analyze it is kind of the critical first step. So we want to get our hands around that.[00:11:10] And then the organization. So is there, you know, anything we need to organize or is a little bit messy? And then what are those analysis? Like, what are those reports that are going to be needed or the visibility, the visualizations that would then be needed on top of that? And then what kind of decisions are trying to be made?[00:11:28] So that’s where the ML and the predictive analytics could come in to try to help assist with the decisions. And we find that most data projects. Follow those, centralized steps that we need to have answers for those.[00:11:43] Dr Genevieve Hayes: So a question that might need to be answered is, how much inventory should we have in a particular shop at a particular time? So that you can satisfy Christmas demand. And then you’d go and get the data about[00:11:59] Ben Johnson: Yeah. The purchase orders or yeah. Where’s the data for your purchase orders? Do you need to collect that from all your stores or do you already have that sitting in some place? Oh, yeah. It’s in all these, you know, disparate CSVs all over the place. We just did a. project for a leading hearing aid manufacturer.[00:12:18] And most of the data that they wanted to use was on a PC in the clinics. So we had to devise a collection mechanism in the software that the clinics were using to go collect all that and regularly import that into a place where We could analyze it, see if it was standardized enough to go into a warehouse or a lake.[00:12:39] And there were a lot of standardization problems, oddly, some of the clinics had kind of taken matters into their own hands and started to add custom fields and whatnot. So to rationalize all of that. So collection, I feel like is a 60 percent of the problem.[00:12:54] Dr Genevieve Hayes: So, we’ve got a framework for increasing productivity by identifying the right problem to solve, but the other half of this equation is how do you actually deliver results in a rapid fashion. because, as you know, A result today is worth far more than a result next year. What’s your advice around getting to those final results faster?[00:13:19] Ben Johnson: So That’s why I like the lake house architecture. We’re also finding new mechanisms and methodology. Some, I can’t talk about where they’re rather than taking this time to take some of the raw data and kind of continuously summarize it. So maybe you’re summarizing it and data warehousing it, but we like the raw data to stay there and just ask it the questions, but it takes more time and more processing power.[00:13:47] So what I’m seeing is we’re often taking that and organizing it into like a vector database or something that’s kind of right for the analysis. We’re also using vector databases in conjunction with AI solutions. So we’re asking the, we’re putting, we’re designing the vector database around the taxonomy, assuming that the user queries are going to match up with that taxonomy, and then using the LLM to help us make queries out of the vector database, and then passing that back to the LLM to test.[00:14:15] Talk about it to make rational sense about the story that’s being told from the data. So one way that we’re accelerating the answer is just to ask questions of the raw data and pay for the processing cost. That’s fast, and that also allows us to say, okay, do we have it?[00:14:32] Like, are we getting closer to having something that looks like the answer to your question? So we can be iterative that way, but at some point we’re starting to get some wins. In that process. And now we need to make those things more performant. And I think there’s a lot of innovation still happening in the middle of the problem.[00:14:51] Dr Genevieve Hayes: Okay, so you’re starting by questioning the raw data. Once you realize that you’re asking the right question and getting something that the results oriented people are looking for, would you then productionize this and start creating pipelines and asking questions of process data? Yeah.[00:15:11] Ben Johnson: Yeah. And we’d start figuring out how to summarize it so that the end user wasn’t waiting forever for an answer.[00:15:17] Dr Genevieve Hayes: Okay, so by starting with the raw data, you’re getting them answers sooner, but then you can make it more robust.[00:15:26] Ben Johnson: That’s right. Yes. More robust. More performant and then, of course, you could then have a wider group of users on the other side consuming that it wouldn’t just be a spreadsheet. It would be a working tool.[00:15:37] Dr Genevieve Hayes: Yeah, it’s one of the things that I was thinking about. I used to have a boss who would always say fast, cheap and good, pick two. Meaning that, you can have a solution now and it can be cheap, but it’s going to come at the cost of And it sounds like you focus on Fast and cheap first, with some sacrifice of quality because you are dealing with raw data.[00:16:00] But then, once you’ve got something locked in, you improve the quality of it, so then technical robustness doesn’t take a hit.[00:16:09] Ben Johnson: Yeah, for sure. I would actually say in the early stage, you’re probably sacrificing the cheap for good and fast because you’re trying to get data right off the logs, right off your raw data, whatever it is. And to get an answer really quickly on that without having to set up a whole lot of pipeline is fast.[00:16:28] And it’s it can be very good. It can be very powerful. We’ve seen many times where it like answers the question. You know, the question of, is that data worth? Mining further and summarizing and keeping around for a long time. So in that way, I think we addressed the ROI of it on the failures, right.[00:16:46] Being able to fail faster. Oh yeah. That data is not going to answer the question that we have. So we don’t waste all the time of what it would have been to process that.[00:16:55] Dr Genevieve Hayes: And what’s been the impact of taking this approach for the businesses and for the data scientists within your organisation who are taking this approach?[00:17:05] Ben Johnson: I think it’s the feeling of like. of partnership with us around their data where we’re taking ownership of the question and they’re giving us access to whatever they have. And there’s a feeling of partnership and the kind of like immediate value. So we’re just as curious about their business as they are.[00:17:27] And then we’re working shoulder to shoulder to help them determine the best way to answer those questions.[00:17:32] Dr Genevieve Hayes: And what’s been the change in those businesses between, before you came on board and after you came on board?[00:17:39] Ben Johnson: Well, I appreciate that question. So with many of the clients, they see that, oh, this is the value of the data. It has unlocked this realization that I, in the case of the hearing aid manufacturer that we work with, they really started finding that they could convert more clients and have a better brand relationship by having a better understanding of their data.[00:18:03] And they were really happy that they kept it. You know, 10 years worth of hearing test data around to be able to understand, their audience better and then turn that into. So they’ve seen a tremendous growth in brand awareness and that’s resulted in making a significant dent in maintaining and continuing to grow their market share.[00:18:26] Dr Genevieve Hayes: So they actually realize the true value of their data.[00:18:30] Ben Johnson: That’s right. And then they saw when they would take action on their data they were able to increase market share because they were able to affect people that truly needed to know about their brand. And like we’re seeing after a couple of years, their brand is like, you don’t think hearing aids unless you think of this brand.[00:18:48] So it’s really cool that they’ve been able to turn that data by really, Talking to the right people and sending their brand message to the right people.[00:18:56] Dr Genevieve Hayes: Yeah, because what this made me think of was one of the things I kept encountering in the early days of data science was a lot of Senior decision makers would bring in data scientists and see data science as a magic bullet. And then because the data scientists didn’t know what questions to answer, they would not be able to create the value that had been promised in the organization.[00:19:25] And the consequence after a year or two of this would be the senior decision makers would come to the conclusion that data science is just a scam. But it seems like by doing it right, you’ve managed to demonstrate to organizations such as this hearing aid manufacturer, that data science isn’t a scam and it can actually create value.[00:19:48] Ben Johnson: Absolutely. I see data sciences anytime that that loop works, right? Where you have questions. So even I have a small client, small business, he owns a glass manufacturing shop. And. The software vendor he uses doesn’t give him a inexpensive way to mark refer like who his salespeople are,[00:20:09] so he needs a kind of a salesperson dashboard. What’s really cool is that his software gives them, they get full access to a read only database. So putting a dashboard on top of. His data to answer this salesperson activities and commissions and just something like that. That’s data science.[00:20:28] And now he can monitor his business. He’s able to scale using his data. He’s able to make decisions on how many salespeople should I hire, which ones are performing, which ones are not performing. How should I pay them? That’s a lot of value to us as data scientists. It just seems like we just put a dashboard together.[00:20:46] But for that business, that’s a significant capability that they wouldn’t have otherwise had.[00:20:52] Dr Genevieve Hayes: So with all that in mind, what is the single most important change our listeners could make tomorrow? to accelerate their data science impact and results.[00:21:02] Ben Johnson: I would just say, be asking that question, Like what question am I trying to answer? What do you expect the outcome to be? Or what do you think the outcome is going to be? So that I’m not biased by that, but I’m sanity checking around that. And then what decisions are you going to make as a result?[00:21:19] I think always having that like in the front of your mind would help you be more consultative and help you work according to an intention. And I think that’s super helpful. Like don’t let the client Or the customer in your case, whether that be an internal person give you that assignment, like, just tell me what’s there.[00:21:38] Right. I just want insights. I think the have to push our leaders to give us a little more than that.[00:21:46] Dr Genevieve Hayes: the way I look at it is, don’t treat your job as though you’re someone in a restaurant who’s just taking an order from someone.[00:21:53] Ben Johnson: Sure.[00:21:54] Dr Genevieve Hayes: Look at it as though you’re a doctor who’s diagnosing a problem.[00:21:58] Ben Johnson: Yeah. And the data scientists that I worked with that have that like in their DNA, like they just can’t move forward unless they understand why they’re doing what they’re doing have been really impactful. In the organization, they just ask great questions and they quickly become an essential part of the team.[00:22:14] Dr Genevieve Hayes: So for listeners who want to get in contact with you, Ben, or to learn more about Particle 41, what can they do?[00:22:21] Ben Johnson: Yeah, I’m on LinkedIn. In fact I love talking to people about data science and DevOps and software development. And so I have a book appointment link on my LinkedIn profile itself. So I’m really easy to get into a call with, and we can discuss whatever is on your mind. I also offer fractional CTO services.[00:22:42] And I would love to help you with a digital problem.[00:22:45] Dr Genevieve Hayes: And there you have it. Another value packed episode to help turn your data science skills into serious clout, cash, and career freedom. If you enjoyed this episode, why not make it a double? Next week, catch Ben’s value boost, a quick five minute episode where he shares one powerful tip for getting real results real fast.[00:23:10] Make sure you’re subscribed so you don’t miss it. Thanks for joining me today, Ben.[00:23:16] Ben Johnson: Thank you. It was great being here. I enjoyed it[00:23:19] Dr Genevieve Hayes: And for those in the audience, thank you for listening. I’m Dr. Genevieve Hayes, and this has been value driven data science. The post Episode 54: The Hidden Productivity Killer Most Data Scientists Miss first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

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