Value Driven Data Science

Episode 104: [Value Boost] The Four Zones of AI Productivity for Data Scientists

Dr Genevieve Hayes — Thu, 07 May 2026 07:00:00 +1000

AI can get you to 60% of a finished output in minutes. But getting from 60% to 100% - the part where real insight lives - is where human expertise becomes the deciding factor. And the more expertise you bring, the further AI can take you.

In this Value Boost episode, Brent Dykes joins Dr Genevieve Hayes to apply his Four Zones of AI Productivity framework to the insight generation process and explore what it means for data professionals who want to position themselves as strategic advisors.

In this episode, you'll discover:

The Four Zones of AI Productivity and how they apply to insight generation [01:28]
Why AI can help you find an insight but can't generate an actionable one [06:39]
Why better AI tools will widen the gap between experts and novices [09:46]
How to use AI effectively in your insight generation process [11:44]

Guest Bio

Brent Dykes is the author of Effective Data Storytelling and the founder of AnalyticsHero. He has consulted with some of the world’s most recognised brands, including Microsoft, Sony, Nike and Amazon, and is a regular contributor to Forbes.

Links

Connect with Brent on LinkedIn
Effective Data Storytelling website
Forbes article about the Four Zones of AI Productivity
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 103: The Art of the Actionable Insight

Dr Genevieve Hayes — Thu, 30 Apr 2026 07:00:00 +1000

Most data scientists have been in this situation: you spend hours analysing a dataset, return to your stakeholder with your findings, and are met with a polite "that's interesting" - before your work disappears into a drawer, never to be seen again.

The problem usually isn't the analysis. It's that interesting observations and genuine insights are not the same thing.

In this episode, Brent Dykes joins Dr Genevieve Hayes to share the frameworks behind identifying and communicating insights that actually move organisations to act.

In this episode, you'll discover:

What makes an insight an insight and why only 5% of findings qualify [03:42]
The four dimensions that focus your analysis before you touch the data [11:25]
The six criteria for a truly actionable insight [15:06]
Why narrative outperforms an executive summary every time [19:14]

Guest Bio

Links

Connect with Brent on LinkedIn
Effective Data Storytelling website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 102: [Value Boost] How Giving Away Your Work for Free Can Build Your Authority as a Data Scientist

Dr Genevieve Hayes — Thu, 23 Apr 2026 07:00:00 +1000

Building authority as a data professional doesn't require a large budget, a publisher, or even a large audience. But it does require a deliberate decision to share your thinking with the world and the patience to let that compound over time.

In this Value Boost episode, Prof. Rob Hyndman joins Dr. Genevieve Hayes to share how selectively giving away his work for free helped him become one of the most cited and influential statisticians in the world, and what data professionals at any stage of their career can learn from that approach.

In this episode, you'll discover:

Why Rob decided to give away his work for free from the start of his career [01:42]
How open source software multiplied the impact of his research [05:58]
Why authority building is a virtuous cycle and how to start it [09:47]
Why starting small is the right move [10:35]

Guest Bio

Prof. Rob Hyndman is one of the world’s most influential applied statisticians and a Professor in the Department of Econometrics and Business Statistics at Monash University. He has maintained an active statistical consulting practice for over 40 years, published over 200 research papers, co-authored more than 65 R packages and written five books on time series forecasting. He is also a Fellow of both the Australian Academy of Science and the Academy of Social Sciences in Australia.

Links

Rob's website
Otexts' website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 101: Why Traditional Statistics Still Matters in the Age of AI

Dr Genevieve Hayes — Thu, 16 Apr 2026 07:00:00 +1000

Data scientists today are under pressure to adopt the latest tools - machine learning, LLMs, generative AI. But in the rush to embrace what's new, many are leaving some of the most powerful analytical tools sitting on the shelf. Tools that handle something modern AI largely can't: uncertainty.

In this episode, Prof. Rob Hyndman joins Dr. Genevieve Hayes to make the case for why rigorous statistical thinking remains indispensable in the age of AI, and what data scientists are giving up when they abandon it.

In this episode, you'll discover:

Why throwing data at an LLM is no substitute for building a model that understands the problem [04:27]
How combining classical statistics and machine learning can produce better forecasting results than either approach alone [08:22]
What data scientists lose when they stop thinking probabilistically - and why it matters for decision making [12:38]
Where to start if you want to strengthen your statistical foundations [25:10]

Guest Bio

Links

Rob's website
Otexts' website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 100: What Data Science Value Really Means

Dr Genevieve Hayes — Thu, 09 Apr 2026 07:00:00 +1000

Over 100 episodes of conversations with world-class practitioners, a few ideas keep surfacing. Technical skill is necessary but never sufficient. The most valuable data professionals aren't the ones who build the best models - they're the ones who know which problems are worth solving. And the gap between those two things is where most data scientists are leaving value on the table.

In this milestone episode, Dr. Genevieve Hayes reflects on her career journey and the conversations that helped her arrive at these conclusions, with Matt O'Mara turning the tables to put her in the hot seat.

In this episode, you'll discover:

From statistician to machine learning advocate and back again - and what that journey revealed [09:49]
The crack in the data science skills market where significant value is hiding [18:59]
Why knowing which problems to solve matters more than knowing how to solve them [24:53]
The top three lessons from 100 conversations on what data science value actually means [33:49]

Guest Bio

Matt O'Mara is the Managing Director of information and insights company Analysis Paralysis and is the founder and Director of i3, which helps organisations use an information lens to realise significant value, increase productivity and achieve business outcomes. He is also an international speaker, facilitator and strategist and is the first and only New Zealander to attain Records and Information Management Practitioners Alliance (RIMPA) Global certified Fellow status.

Links

Connect with Matt on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 99: [Value Boost] Preventing ML Bias Before it Becomes a Problem

Dr Genevieve Hayes — Thu, 26 Mar 2026 07:00:00 +1100

Biased machine learning models don't just produce poor predictions. They can damage reputations, derail projects, and in high-stakes fields like healthcare, potentially cause real harm. Yet many data scientists don't check for bias until it's too late, missing the opportunity to address it at its source.

In this Value Boost episode, Serg Masis joins Dr. Genevieve Hayes to share practical techniques for detecting and mitigating bias in machine learning models before they become major problems for you and your stakeholders.

You'll discover:

The most common bias patterns to watch for [01:32]
How to diagnose whether bias exists in your model [04:44]
The three levels where bias can be addressed [07:13]
Where to intervene for maximum impact [08:17]

Guest Bio

Serg 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.

Links

Serg's Website
Connect with Serg on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 98: Building Trust in AI Through Model Interpretability

Dr Genevieve Hayes — Thu, 19 Mar 2026 07:00:00 +1100

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 Bio

Links

Serg's Website
Connect with Serg on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 97: [Value Boost] Mathematical Modelling as a Gateway to ML Success

Dr Genevieve Hayes — Thu, 12 Mar 2026 07:00:00 +1100

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 Bio

Dr 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.

Links

Bluebird Optimization Website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 96: Making Better Decisions with ML and Optimisation

Dr Genevieve Hayes — Thu, 05 Mar 2026 07:00:00 +1100

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 Bio

Links

Get Tim's 3 Step Guide to Add Optimisation to Your Data Science Skills
Bluebird Optimization Website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 95: [Value Boost] Building Models That Work While Millions Are Watching

Dr Genevieve Hayes — Thu, 26 Feb 2026 07:00:00 +1100

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 Bio

Prof. 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.

Links

Contact Steve at Bond University
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 94: Creating Global Impact with Data Science

Dr Genevieve Hayes — Thu, 19 Feb 2026 07:00:00 +1100

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 Bio

Prof. 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.

Links

Contact Steve at Bond University
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 93: [Value Boost] What Industry Data Scientists Can Learn from Academic Training

Dr Genevieve Hayes — Thu, 18 Dec 2025 07:00:00 +1100

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 Bio

Dr 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.

Links

Connect with Sayli on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 92: Making the Academia to Industry Leap in Data Science

Dr Genevieve Hayes — Thu, 11 Dec 2025 07:00:00 +1100

Making the leap from academia to industry isn't just another career change - it involves a complete shift in the way you work. Data scientists transitioning from academia face a brutal learning curve that can leave them feeling unprepared despite years of advanced training.

In this episode, Dr. Sayli Javadekar joins Dr. Genevieve Hayes to share her recent journey from a tenure-track academic position to working as a data scientist in industry, revealing the challenges she faced and the strategies that helped her navigate this difficult transition.

You'll discover:

Why academic training can leave you unprepared for industry expectations [10:49]
The mindset shifts required when moving from research to business [07:50]
Strategies to help bridge the gap between academic and business work [15:23]
The one thing academics should do before leaving for industry [22:11]

Guest Bio

Links

Connect with Sayli on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 91: [Value Boost] How Your Hobbies Can Supercharge Your Data Science Career

Dr Genevieve Hayes — Thu, 04 Dec 2025 07:00:00 +1100

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 Bio

Colin 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.

Links

Connect with Colin on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 90: Using LLMs to Become a More Effective Data Scientist

Dr Genevieve Hayes — Thu, 27 Nov 2025 07:00:00 +1100

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 Bio

Links

Connect with Colin on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 89: [Value Boost] LinkedIn Strategies for Boosting Your Data Science Career

Dr Genevieve Hayes — Thu, 20 Nov 2025 07:00:00 +1100

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 Bio

Sarah 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.

Links

Connect with Sarah on LinkedIn
Dub Dub Data Website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 88: Building a Data Science Career After Unexpected Job Loss

Dr Genevieve Hayes — Thu, 13 Nov 2025 07:00:00 +1100

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 Bio

Links

Connect with Sarah on LinkedIn
Dub Dub Data Website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 87: [Value Boost] How Your Weirdness Could Be Your Data Science Superpower

Dr Genevieve Hayes — Thu, 06 Nov 2025 07:00:00 +1100

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 Bio

Danny 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.

Links

Connect with Danny on LinkedIn
Download the One Shiny Object framework
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 86: Why Every Data Scientist Is Already Running a Business

Dr Genevieve Hayes — Thu, 30 Oct 2025 07:00:00 +1100

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 Bio

Links

Connect with Danny on LinkedIn
Download the One Shiny Object framework
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 85: [Value Boost] The Office Politics Survival Guide for Data Science Experiments

Dr Genevieve Hayes — Thu, 23 Oct 2025 07:00:00 +1100

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 Bio

Miguel 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.

Links

Connect with Miguel on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 84: The 7-Step Checklist for Creating Business Impact Through Product Analytics

Dr Genevieve Hayes — Thu, 16 Oct 2025 07:00:00 +1100

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 Bio

Links

Connect with Miguel on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 83: [Value Boost] How to Gamify Data Science Requirements Gathering for Better Results

Dr Genevieve Hayes — Thu, 09 Oct 2025 07:00:00 +1100

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 Bio

David 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.

Links

Connect with David on LinkedIn
Superposition website
Superposition YouTube channel
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 82: Why You Should Start Your Data Projects with Pictures Not Data

Dr Genevieve Hayes — Thu, 02 Oct 2025 07:00:00 +1000

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 Bio

Links

Connect with David on LinkedIn
Superposition website
Superposition YouTube channel
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 81: [Value Boost] How to Frame Data Problems Like a Decision Scientist

Dr Genevieve Hayes — Thu, 25 Sep 2025 07:00:00 +1000

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 Bio

Prof 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.

Links

Connect with Jeff on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 80: Why Decision Scientists Succeed Where Data Scientists Fail

Dr Genevieve Hayes — Thu, 18 Sep 2025 07:00:00 +1000

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 Bio

Links

Connect with Jeff on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 79: [Value Boost] The Win Win Data Product Validation Strategy

Dr Genevieve Hayes — Thu, 04 Sep 2025 07:00:00 +1000

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 Bio

Daniel 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.

Links

Daniel's website
Daniel's YouTube channel
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 78: From Machine Learning Engineer to Independent Data Professional Before 30

Dr Genevieve Hayes — Thu, 28 Aug 2025 07:00:00 +1000

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 Bio

Daniel 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.

Links

Daniel's website
Daniel's YouTube channel
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 77: [Value Boost] Why Your Data Team Needs a Book Club

Dr Genevieve Hayes — Thu, 21 Aug 2025 07:00:00 +1000

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 Bio

Kashif Zahoor is the Vice President of Business Intelligence at Influence Mobile and has extensive experience in data leadership.

Links

Connect with Kashif on LinkedIn
Data Insights Delivered (Amazon Australia)(Amazon US)
The AI-Driven Leader (Amazon Australia)(Amazon US)
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 76: The 3 Step Framework That Transforms Data Order-Takers to Strategic Business Partners

Dr Genevieve Hayes — Thu, 14 Aug 2025 07:00:00 +1000

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 Bio

Kashif Zahoor is the Vice President of Business Intelligence at Influence Mobile and has extensive experience in data leadership.

Links

Connect with Kashif on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 75: [Value Boost] The Psychology Hack That Gets Your Data Insights Heard

Dr Genevieve Hayes — Thu, 07 Aug 2025 07:00:00 +1000

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 Bio

Dr 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.

Links

Russell's Website
Connect with Russell on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 74: How Competitive Debating Frameworks Can Revolutionise Your Data Science Career

Dr Genevieve Hayes — Thu, 31 Jul 2025 07:00:00 +1000

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 Bio

Links

Russell's Website
Connect with Russell on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 73: [Value Boost] How to Trust Social Media Data When You Can't Trust Social Media

Dr Genevieve Hayes — Thu, 24 Jul 2025 07:00:00 +1000

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 Bio

Tim 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.

Links

Tim's Website
Connect with Tim on LinkedIn
Subscribe to Tim's newsletter
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 72: The Social Media Hacker's Guide to Better Data Science

Dr Genevieve Hayes — Thu, 17 Jul 2025 07:00:00 +1000

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 Bio

Links

Tim's Website
Connect with Tim on LinkedIn
Subscribe to Tim's newsletter
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 71: [Value Boost] Why Most Dashboards Fail and How to Fix Yours

Dr Genevieve Hayes — Thu, 10 Jul 2025 07:00:00 +1000

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 Bio

Nicholas 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.

Links

Nicholas's Website
Connect with Nicholas on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 70: How to Interpret Data Like a Pro in the Age of AI

Dr Genevieve Hayes — Thu, 03 Jul 2025 07:00:00 +1000

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 Bio

Links

Nicholas's Website
Connect with Nicholas on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 69: [Value Boost] The Value Proposition Framework Every Data Scientist Needs to Master

Dr Genevieve Hayes — Thu, 26 Jun 2025 07:00:00 +1000

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 Bio

Dr 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.

Links

Connect with Peter on LinkedIn
A Brief Guide to Providing Insights as a Service (IaaS)
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 68: How to Market Your Data Science Skills Internally with the Insights-as-a-Service Approach

Dr Genevieve Hayes — Thu, 19 Jun 2025 07:00:00 +1000

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 Bio

Links

Connect with Peter on LinkedIn
A Brief Guide to Providing Insights as a Service (IaaS)
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 67: [Value Boost] The 3 Level Hierarchy That Protects Your Data Science Credibility

Dr Genevieve Hayes — Thu, 12 Jun 2025 07:00:00 +1000

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 Bio

Dr 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.

Links

Brian's website
Connect with Brian on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 66: How to Think Like a Data Scientist (Even While AI Does All the Work)

Dr Genevieve Hayes — Thu, 05 Jun 2025 07:00:00 +1000

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 Bio

Dr 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.

Links

Brian's website
Connect with Brian on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 65: [Value Boost] How to Upgrade Your Data Visuals Without Design Training

Dr Genevieve Hayes — Thu, 29 May 2025 07:00:00 +1000

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 Bio

Bill 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.

Links

Bill's Website
Connect with Bill on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 64: Stop Being a Data Waiter and Start Stakeholder Whispering

Dr Genevieve Hayes — Thu, 22 May 2025 07:00:00 +1000

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 Bio

Links

Bill's Website
Connect with Bill on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 63: [Value Boost] 3 Affordable AI Tools Every Data Scientist Needs

Dr Genevieve Hayes — Thu, 15 May 2025 07:00:00 +1000

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 Bio

Heidi 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.

Links

Connect with Heidi on LinkedIn
BrightLogic website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 62: The Data Science Gold Mine Hidden in Small Business AI Solutions

Dr Genevieve Hayes — Thu, 08 May 2025 07:00:00 +1000

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 Bio

Links

Connect with Heidi on LinkedIn
BrightLogic website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 61: [Value Boost] The 90-10 Rule for Transforming Data Science Impact

Dr Genevieve Hayes — Thu, 01 May 2025 07:00:00 +1000

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 Bio

Gregory Lewandowski is the Chief AI Strategist and Founder of GLEW, a consultancy focussing on the business side of AI ROI.

Links

Connect with Gregory on LinkedIn
GLEW Services website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 60: 5 Executive Priorities That Transform Data Science Results into Business Value

Dr Genevieve Hayes — Thu, 24 Apr 2025 07:00:00 +1000

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 Bio

Gregory Lewandowski is the Chief AI Strategist and Founder of GLEW, a consultancy focussing on the business side of AI ROI.

Links

Connect with Gregory on LinkedIn
GLEW Services website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 59: [Value Boost] How Data Scientists Can Get in the AI Room Where It Happens

Dr Genevieve Hayes — Thu, 10 Apr 2025 06:58:38 +1000

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 LinkedIn
Andrei’s website
Agent.ai website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 58: Why Great Data Scientists Ask ‘Why?’ (And How It Can Transform Your Career)

Dr Genevieve Hayes — Thu, 03 Apr 2025 07:16:26 +1100

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

Links

Connect with Andre on LinkedIn
Andrei’s website
Agent.ai website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 57: [Value Boost] 3 Game-Changing Questions to Save Your Data Science Presentations From Falling Flat

Dr Genevieve Hayes — Thu, 27 Mar 2025 07:29:32 +1100

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 LinkedIn
Connect with Matt on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 56: How a Data Scientist and a Content Expert Turned Disappointing Results into Viral Research

Dr Genevieve Hayes — Thu, 20 Mar 2025 07:27:17 +1100

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 LinkedIn
Connect with Lauren on LinkedIn
Can Generative AI Improve Developer Productivity? (Report)
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 55: [Value Boost] Why Data Scientists are Focus-Poor (and the Software Developer’s Solution to Fix It)

Dr Genevieve Hayes — Thu, 13 Mar 2025 06:49:56 +1100

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 LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 54: The Hidden Productivity Killer Most Data Scientists Miss

Dr Genevieve Hayes — Thu, 06 Mar 2025 07:23:37 +1100

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 LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 53: A Wake-Up Call from 3 Tech Leaders on Why You’re Failing as a Data Scientist

Dr Genevieve Hayes — Thu, 27 Feb 2025 07:53:50 +1100

Are your data science projects failing to deliver real business value?

What if the problem isn’t the technology or the organization, but your approach as a data scientist?

With only 11% of data science models making it to deployment and close to 85% of big data projects failing, something clearly isn’t working.

In this episode, three globally recognised analytics leaders, Bill Schmarzo, Mark Stouse and John Thompson, join Dr Genevieve Hayes to deliver a tough love wake-up call on why data scientists struggle to create business impact, and more importantly, how to fix it.

This episode reveals:

Why focusing purely on technical metrics like accuracy and precision is sabotaging your success — and what metrics actually matter to business leaders. [04:18]
The critical mindset shift needed to transform from a back-room technical specialist into a valued business partner. [30:33]
How to present data science insights in ways that drive action — and why your fancy graphs might be hurting rather than helping. [25:08]
Why “data driven” isn’t enough, and how to adopt a “data informed” approach that delivers real business outcomes. [54:08]

Guest Bio

Bill Schmarzo, also known as “The Dean of Big Data,” is the AI and Data Customer Innovation Strategist for Dell Technologies’ AI SPEAR team, and is the author of six books on blending data science, design thinking, and data economics from a value creation and delivery perspective. He is an avid blogger and is ranked as the #4 influencer worldwide in data science and big data by Onalytica and is also an adjunct professor at Iowa State University, where he teaches the “AI-Driven Innovation” class.

Mark Stouse is the CEO of ProofAnalytics.ai, a causal AI company that helps companies understand and optimize their operational investments in light of their targeted objectives, time lag, and external factors. Known for his ability to bridge multiple business disciplines, he has successfully operationalized data science at scale across large enterprises, driven by his belief that data science’s primary purpose is enabling better business decisions.

John Thompson is EY’s Global Head of AI and is the author of four books on AI, data and analytics teams. He was named one of dataIQ’s 100 most influential people in data in 2023 and is also an Adjunct Professor at the University of Michigan, where he teaches a course based on his book “Building Analytics Teams”.

Links

Connect with Bill on LinkedIn
Connect with Mark on LinkedIn
Connect with John on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 52: Automating the Automators – How AI and ML are Transforming Data Teams

Dr Genevieve Hayes — Thu, 19 Dec 2024 08:44:55 +1100

In many organisations, data scientists and data engineers exist as support staff. Data engineers are there to make data accessible to data scientists and data analysts, and data scientists are there to make use of that data to support the rest of the business.

But in helping everyone else in the business, data professionals can often forget to help themselves.

However, just as AI and machine learning can be used to help others in the organisation perform their jobs more effectively, there’s no reason why they can’t also be used to help data professionals excel in their own jobs. And as experts in applying these techniques, data scientists are perfectly placed to leverage them.

In this episode, Prof Barzan Mozafari joins Dr Genevieve Hayes to discuss how AI and machine learning are helping data professionals do their jobs more effectively.

Guest Bio

Prof. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and Prof. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and has won several awards for his research at the intersection of machine learning and database systems.

Highlights

(00:05) Meet Barzan Mozafari
(00:50) The role of AI in data engineering
(01:36) The birth of Keebo
(02:34) Challenges in modern data pipelines
(05:41) How Keebo optimizes data warehousing
(07:35) AI and ML techniques behind Keebo
(08:47) Reinforcement learning in practice
(16:23) Guardrails and safeguards in AI systems
(26:29) The build vs. buy dilemma
(36:03) Future trends in data science and AI
(39:36) Final advice for data scientists
(40:50) Closing remarks and contact information

Links

Keebo website
Connect with Barzan on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 51: Data Storytelling in Virtual Reality

Dr Genevieve Hayes — Thu, 05 Dec 2024 07:53:50 +1100

In the 2002 movie, Minority Report, the future of data interaction is depicted as Tom Cruise standing in front of a computer monitor and literally grabbing data points with his hands. Data interaction is shown to be as easy as interacting with physical objects in the real world.

This vision of a world where data is accessible to all was considered to be science fiction when Minority Report was first released. But over 20 years later, we are now at a point where technology has become good enough for this to soon become fact. And its data science that’s making this possible.

Or more accurately, it’s the intersection of data science and art.

In this episode, Michela Ledwidge joins Dr Genevieve Hayes to discuss how virtual reality and data science can be combined to create interactive data storytelling experiences.

Guest Bio

Michela Ledwidge is the co-founder and CEO of Mod, a studio specialising in real-time and virtual production, and the creator of Grapho, a VR platform that lets non-technical users examine and manipulate graph data. She is also the writer and director of A Clever Label, a world-first interactive documentary.

Highlights

(00:05) Meet Michela Ledwidge
(02:04) Michela’s journey from Commodore 64 to interactive filmmaking
(06:40) The birth of Mod and remixable films
(14:48) Exploring graph databases and data science techniques
(25:33) The future of data science and AI in creative industries
(32:27) Grapho: Data science + storytelling in virtual reality
(48:29) The future of data science and storytelling
(49:37) Conclusion and contact information

Links

Grapho website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 50: Addressing the Unknown Unknowns in Data-Driven Decision Making

Dr Genevieve Hayes — Thu, 21 Nov 2024 06:20:48 +1100

When it comes to awareness and understanding, what we know and don’t know can be split into four categories: known knowns; unknown knowns; known unknowns; and unknown unknowns. And to quote former US Secretary of Defence Donald Rumsfeld: “If one looks throughout the history of our country and other free countries, it is the latter category that tends to be the difficult ones.”

When Rumsfeld made his famous “unknown unknowns” speech, he was referring to military intelligence. But the concept of “unknown unknowns” is just as relevant to data and data science. Those data dark spots, or data gaps, can be a real issue when it comes to data-driven decision making.

In this episode, Matt O'Mara joins Dr Genevieve Hayes to discuss the challenges and risks data gaps present to businesses and the community, and what data scientists can do to help address this issue.

Guest Bio

Highlights

(00:55) Understanding information gaps
(02:33) Matt O'Mara's journey and insights
(04:58) Real-world examples of information gaps
(07:30) The impact of information gaps on society
(11:54) Organizational challenges and solutions
(25:55) Critical information sources and management
(31:33) Developing an information lens
(42:47) The role of data scientists in addressing information gaps
(45:29) Conclusion and contact information

Links

i3 website
Connect with Matt on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 49: AI-Generated Advertising and the Future of Content Creation

Dr Genevieve Hayes — Thu, 07 Nov 2024 07:51:24 +1100

The idea of targeted marketing is nothing new. Even before the advent of computers and data science, businesses have always tried to optimise their advertising campaigns by tailoring their advertisements to their ideal buyers.

Data science allowed businesses to become more effective at this targeting. However, it was still necessary for businesses to manually create the advertising content they wanted to share with their target buyers. That is, until recently.

In this episode, Hikari Senju joins Dr Genevieve Hayes to discuss how advances in AI technology have made it possible to generate personalised advertising content, optimised to produce the best results, and what that means for content creators.

Guest Bio

Hikari Senju is the founder and CEO of Omneky, an AI platform that generates, analyzes and optimizes personalised advertising content at scale. He is a Harvard computer science graduate and also co-founded tutoring app Quickhelp, which he later sold to Yup.com.

Highlights

(02:06) How OmneKey works
(03:29) Personalisation in advertising
(06:35) The role of human input in AI-generated content
(10:45) Impact of AI on the advertising industry
(15:09) Hikari Senju’s journey and insights
(19:53) Technical deep dive into OmneKey
(25:54) The competitive landscape of AI
(32:10) The future of content and AI
(40:26) Conclusion and final thoughts

Links

Omnekey website
Connect with Hikari on LinkedIn
Follow Hikari on X
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 48: Overcoming the Machine Learning Deployment Challenge

Dr Genevieve Hayes — Thu, 24 Oct 2024 07:48:22 +1100

It’s been 12 years since Thomas H Davenport and DJ Patil first declared data science to be “the sexiest job of the 21st century” and in that time a lot has changed. Universities have started offering data science degrees; the number of data scientists has grown exponentially; and generative AI technologies, such as Chat-GPT and Dall-E have transformed the world.

Yet, throughout that time, one thing has remained the same. Most machine learning projects still fail to deploy.

However, it’s not the technical capabilities of data scientists that let them down – those are now better than ever before. Rather, “it’s the lack of a well-established business practice that is almost always to blame.”

In this episode, Dr Eric Siegel joins Dr Genevieve Hayes to discuss bizML, the new “gold-standard”, six-step practice he has developed “for ushering machine learning projects from conception to deployment.”

Guest Bio

Dr Eric Siegel is a leading machine learning consultant and the CEO and co-founder of Gooder AI. He is also the founder of the long-running Machine Learning Week conference series; author of the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die and the recently released The AI Playbook; and host of The Dr Data Show podcast.

Highlights

(01:21) Challenges in machine learning deployment
(05:00) The importance of business involvement in ML projects
(15:39) Defining bizML and its steps
(25:32) Understanding predictive analytics
(26:52) Challenges in model deployment and MLOps
(29:12) BizML for generative and causal AI
(31:25) Exploring uplift modeling
(35:45) Gooder AI: bridging the gap between data science and business value
(45:45) Beta testing and future plans for Gooder AI
(47:35) Final advice for data scientistsb

Links

BizML website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 47: Leveraging Causal Inference to Drive Business Value in Data Science

Dr Genevieve Hayes — Thu, 10 Oct 2024 07:26:44 +1100

For most people, data science is synonymous with machine learning, and many see the role of the data scientist as simply being to build predictive models. Yet, predictive analytics can only get you so far. Predicting what will happen next is great, but what good is knowing the future if you don’t know how to change it?

That’s where causal analytics can help. However, causal inference is rarely taught as part of traditional prediction-centric data science training. Where it is taught, though, is in the social sciences.

In this episode, Joanne Rodrigues joins Dr Genevieve Hayes to discuss how techniques drawn from the social sciences, in particular, causal inference, can be combined with data science techniques to give data scientists the ability to understand and change consumer behaviour at scale.

Guest Bio

Joanne Rodrigues is an experienced data scientist with master’s degrees in mathematics, political science and demography. She is the author of Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights and the founder of health technology company ClinicPriceCheck.com.

Highlights

(00:49) Combining social sciences with data science
(02:01) Joanne’s journey from social sciences to data science
(04:15) Understanding causal inference
(07:40) Real-world applications of causal inference
(12:22) Challenges in causal inference
(19:41) Correlation vs. causation in data science
(26:12) Operationalising randomness in experiments
(27:16) Observational experiments vs. medical trials
(27:47) Designing experiments with existing data
(28:50) Challenges in natural experiments
(29:55) Ethical considerations in experimentation
(31:50) Qualitative frameworks in causal inference
(35:58) Integrating causal inference with machine learning
(38:59) Common techniques in causal inference
(41:02) Marketing causal inference to management
(43:48) Ethical implications of predictive modelling
(48:08) Final advice for data scientists

Links

Connect with Joanne on LinkedIn
Joanne’s website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 46: Empowering Democracy with LLMs

Dr Genevieve Hayes — Thu, 26 Sep 2024 09:31:56 +1000

With all the reports about the spread of misinformation and disinformation on social media, sometimes it feels like one of the biggest threats to democracy is technology. But no technology is inherently good or bad. It’s how you use it that matters. And just as technology has the potential to harm democracy, it also has the potential to enhance it.

In this episode, Vikram Oberoi joins Dr Genevieve Hayes to discuss how he has been using generative AI and large language models (LLMs) to enhance people’s access to NYC council meetings through his work on citymeetings.nyc.

Guest Bio

Vikram Oberoi is a software engineer, fractional CTO and co-owner of Baxter HQ, a boutique early-stage tech product development firm. He also built and operates citymeetings.nyc, an LLM powered tool to make New York City council meetings accessible.

Highlights

(00:00) Meet Vikram Oberoi
(01:31) Overview of citymeetings.nyc
(07:50) Vikram’s journey into local politics
(12:05) Technical aspects of citymeetings.nyc
(18:41) Dealing with AI hallucinations
(25:00) Understanding the different types of AI errors
(26:05) Case study: Honeycomb’s query feature
(26:59) Reinforcement learning with human feedback
(28:32) Choosing between Claude and GPT
(31:42) The importance of context windows
(40:31) Effective prompt engineering tips
(46:11) Final advice for data scientists

Links

citymeetings.nyc
Vikram’s website
Vikram’s talk at NYC School of Data about citymeetings.nyc
Follow Vikram on X
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 45: AI-Powered Investment Insights

Dr Genevieve Hayes — Thu, 12 Sep 2024 09:04:37 +1000

Succeeding in stock market investing is all about timing – buying low, selling high and being able to read the signs to determine when things are going to change. But as anyone who’s ever tried to get rich through stock trading can tell you, this is easier said than done.

Given the massive amounts of financial data published each day, for people who aren’t experts in the field, it can be too hard to spot the patterns and keep up with the constant change. As a result, many people are either investing in markets based on guesswork or not investing at all.

This is where AI can help, because there’s nothing that AI does better than finding patterns in large volumes of data. AI has the potential to democratize access to investment insights.

In this episode, Andrew Einhorn joins Dr Genevieve Hayes to discuss how AI can help ordinary investors find better investment opportunities than they could ever manage on their own.

Guest Bio

Andrew Einhorn is the CEO and co-founder of Levelfields, an AI-driven fintech application that automates arduous investment research so investors can find opportunities faster and easier. Before moving into finance, Andrew started his career as an epidemiologist and helped build a pandemic monitoring system for Georgetown Hospital. He also previously co-founded tech company Synoptus, has consulted for NASA and served as an advisor to a $65 billion hedge fund.

Highlights

(00:06) Meet Andrew Einhorn
(02:54) Andrew’s journey from public health to data science
(07:55) The birth of Levelfields
(19:35) Event-driven investment insights explained
(26:22) AI and data science behind Levelfields
(36:36) User experience and customisation
(41:03) Future developments and final advice

Links

Levelfields website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 44: Designing Data Products People Actually Want to Use

Dr Genevieve Hayes — Thu, 29 Aug 2024 07:27:37 +1000

As a data scientist, there’s nothing worse than devoting months of your time to building a data product that appears to meet your stakeholders’ every need, only to find it never gets used. It’s depressing, demotivating and can be devastating for your career.

But as the old saying goes, “You can lead a horse to water, but you can’t make it drink”. Or can you?

In this episode, Brian T O’Neill joins Dr Genevieve Hayes to discuss how you can apply the best techniques from software product management and UI/UX design to create ML and AI products your stakeholders will love.

Guest Bio

Brian T O’Neill is the Founder and Principal of Designing for Analytics, an independent data product UI/UX design consultancy that helps data leaders turn ML & analytics into usable, valuable data products. He also advises on product and UI/UX design for startup founders in MIT’s Sandbox Innovation Fund; hosts the podcast Experiencing Data; founded The Data Product Leadership Community and maintains a career as a professional percussionist performing in Boston and internationally.

Highlights

Introducing Brian T. O’Neill (00:19)
Brian’s journey from music to data product design (02:16)
Understanding the real needs of stakeholders (06:45)
The importance of user-centered design in data products (09:33)
Gaining insights through direct user interaction (12:16)
Focusing on business and user experience outcomes (17:48)
Debunking the myths of self-serve analytics and dashboarding (22:46)
Data platforms vs. data products (27:26)
Defining a data product: the value exchange principle (29:08)
Designing human-centered data products (32:56)
The CED framework: conclusions, evidence, data (36:01)
Final advice for data scientists (45:06)

Links

Brian’s mailing list
Designing for Analytics
Data Product Leadership Community
CED framework
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 43: Shaping the Future of AI

Dr Genevieve Hayes — Thu, 15 Aug 2024 07:59:43 +1000

Two years ago, no one could imagine the impact generative AI would have on our world, and most of us can’t even begin to imagine the impact the next generation of AI will have on our world two years from now. The only thing that is certain is uncertainty.

But that uncertainty brings with it great opportunities and choices. We can choose to sit back and let the future of AI play out in front of us or engage with this new technology and shape the future of AI and the world as we know it.

In this episode, Dr Eric Daimler joins Dr Genevieve Hayes to discuss his extraordinary work in shaping the future of AI and what that future might look like.

Guest Bio

Dr. Eric Daimler is the Chair, CEO and Co-Founder of Conexus AI and has previously co-founded five other companies in the technology space. He served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of President, as the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics. He is also the author of the upcoming book The Future is Formal: The Roadmap for Using Technology to Solve Society’s Biggest Problems.

Highlights

(00:00) Meet Dr. Eric Daimler
(01:46) Eric’s role in the Obama Administration
(06:32) Challenges in government data integration
(10:31) The importance of technical expertise in policy
(16:06) Founding Connexus AI
(18:09) Understanding category theory
(20:51) Applications of Conexus AI
(27:16) The future of AI: safe and symbolic
(38:35) Insights from Eric’s upcoming book
(47:49) Advice for data scientists and final thoughts

Links

Connect with Eric on LinkedIn
Conexus AI website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 42: Should You Outsource Your Data Team?

Dr Genevieve Hayes — Thu, 01 Aug 2024 07:37:12 +1000

Chances are, you’re reading this summary on a device you didn’t build yourself. Why would you? Tech companies can build you a far better device for a much lower cost than you could ever manage alone. As with many other cases in life, this is an example of where it is better to buy than to build.

Yet, in building a data team, many organisations assume the only solution is to build from within. And although this may be the right solution for some organisations, building a solution isn’t right for all.

In this episode, Collin Graves joins Dr Genevieve Hayes to discuss what a bought solution might look like in the data science space, and whether it is right for you.

Guest Bio

Collin Graves is the CEO of North Labs, a leading fractional cloud data analytics firm that helps growing companies become data-driven. Before founding North Labs, he served with distinction in NATO Special Operations during his tenure with the US Air Force. He is also the author of the upcoming Data Revolution: Leading with Analytics and Winning from Day One.

Highlights

(01:43) Collin’s journey from the US Air Force to data science
(09:53) The birth of North Labs: a fractional data analytics firm
(12:02) Scaling a one-man operation to a thriving business
(13:58) The challenges of using data in the industrial and manufacturing sector
(28:41) The power of outsourcing data science
(34:09) The future of data teams and the role of in-house expertise
(41:44) Insights from Collin’s upcoming book
(46:17) Final thoughts and advice for data scientists

Links

Connect with Collin on LinkedIn
North Labs website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 41: Building Better AI Apps with Knowledge Graphs and RAG

Dr Genevieve Hayes — Thu, 18 Jul 2024 07:15:43 +1000

When ChatGPT was first released, there was talk it would lead to traditional search engines, like Google, soon becoming obsolete. That was until users discovered generative AI’s one major drawback – it makes stuff up.

Because of the stochastic nature of ChatGPT, it is never going to be possible to completely eliminate hallucinations. However, there are ways to work around this issue. One such way is through leveraging knowledge graphs and retrieval augmented generation (or RAG).

In this episode, Kirk Marple joins Dr Genevieve Hayes to discuss how knowledge graphs and RAG can be leveraged to improve the quality of generative AI.

Guest Bio

Kirk Marple is the CEO and Technical Founder of Graphlit, serverless, cloud-native platform that streamlines the development of AI apps by automating unstructured data workflows and leveraging retrieval augmented generation.

Highlights

(00:19) Meet Kirk Marple
(01:22) Leveraging knowledge graphs and RAG
(06:08) Challenges with named entity extraction
(09:16) Cost implications of LLMs
(12:17) Deep dive into RAG
(16:58) Vector search explained
(20:49) Graph databases and RAG
(38:58) Future of RAG and AI
(43:08) Final thoughts

Links

Connect with Kirk on LinkedIn
Graphlit website
Follow Graphlit on X
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 40: Making Data Science Teams Profitable

Dr Genevieve Hayes — Thu, 04 Jul 2024 07:32:39 +1000

For many people, data science is synonymous with machine learning and many data science courses are little more than overviews of the most used machine learning algorithms and techniques.

Where the majority of data science courses fall short is they neglect to bridge the gap between data science theory and business reality, resulting in many data scientists who are technically strong but unable to create value from their work. However, this doesn’t necessarily have to be the case.

In this episode, Douglas Squirrel joins Dr Genevieve Hayes to discuss systems and techniques data scientists and their managers can use to make data science teams profitable.

Guest Bio

Douglas Squirrel has been coding for forty-five years and has led software teams for twenty-five. He uses the power of conversations to create insane profits in technology organisations of all sizes. His experience includes growing software teams as a CTO in startups; consulting on product improvement; and coaching a wide variety of leaders in improving their conversations, aligning to business goals, and creating productive conflict.

Highlights

Douglas Squirrel’s journey: From CTO to profitability guru (00:00)
Integrating data science with business goals (10:58)
The surprising technological growth in Africa (17:38)
Overcoming the Walled Garden: strategies for tech team success (19:14)
The Lean Startup approach to data science (26:48)
The importance of direct feedback in data science (32:50)
Transforming data science with human empathy (33:39)
Leveraging action science for effective communication (42:46)
Elephant Carpaccio (47:41)
Techniques for data scientists to create business value (51:22)
Creating productive conflict for business innovation (53:43)
Final thoughts and resources (01:00:28)

Links

Douglas Squirrel’s website
Squirrel Squadron
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 39: The Impact of Data Science on Data Orchestration

Dr Genevieve Hayes — Thu, 20 Jun 2024 07:25:18 +1000

One of the big promises of data science is its ability to combine multiple disparate datasets to produce value-creating insights. But this is only possible if you can get all those disparate datasets together, in the one location, to begin with. The has led to the rise of the data engineer and the data orchestration platform.

In this episode, Sandy Ryza joins Dr Genevieve Hayes to discuss the impact of the data scientist on the creation of the next generation of data orchestration tools.

Guest Bio

Sandy Ryza is a data scientist turned data engineer who is currently the lead engineer on the Dagster project, an open-source data orchestration platform used in MLOps, data science, IOT and analytics. He is also the co-author of Advanced Analytics with Spark.

Highlights

Welcome to Value Driven Data Science (00:00)
Introducing Sandy Ryza and his journey from data scientist to data engineer (01:30)
Navigating the challenges of creating consistent data definitions within teams (05:11)
The birth and development of Dagster (11:32)
Dagster: A tool designed for data scientists (20:54)
Final thoughts and advice for data scientists (37:29)

Links

Connect with Sandy on LinkedIn
Follow Sandy on X
Dagster
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 38 – The Art and Science of Survey Design

Dr Genevieve Hayes — Thu, 06 Jun 2024 07:36:19 +1000

From BuzzFeed Quizzes to the national census, it’s impossible to get through life without encountering surveys. However, not all surveys are created equal. As with everything else in data science, garbage going in will inevitably lead to garbage coming out.

In this episode, Kyle Block joins Dr Genevieve Hayes to look at practical techniques for designing surveys to ensure they deliver value, as well as approaches to analysing survey results, to maximise that value.

Guest Bio

Kyle Block is Head of Research at Gradient, an analytics agency that combines advanced statistical and machine learning techniques to answer difficult marketing challenges. He holds a Masters in Spatial Analysis from the University of Pennsylvania and has spent his career helping managers use data to make important decisions.

Talking Points

What good survey design looks like.
Advice on how to design effective surveys.
How list experiments can be used to uncover true opinions around sensitive topics.
How data science techniques can be applied to survey data analysis to maximise its value.
What the future might hold for survey data analysis.

Links

Connect with Kyle on LinkedIn
Gradient Website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 37: Data Privacy in the Age of AI

Dr Genevieve Hayes — Thu, 23 May 2024 07:13:52 +1000

Most people have come to accept that the price of living in a technological world, and its associated convenience, is some loss of data privacy. However, few realise just how much privacy they are giving up.

In this episode, Dr Katharine Kemp joins Dr Genevieve Hayes to discuss data privacy challenges for consumers and data scientists in the age of AI.

Guest Bio

Dr Katharine Kemp is an Associate Professor in UNSW’s Faculty of Law and Justice and Deputy Director of the Allens Hub for Technology, Law and Innovation. Her research focuses on competition, data privacy and consumer protection regulation, including their application to digital platforms.

Talking Points

What types of data are companies collecting about their customers?
How companies currently de-identify customer data to ensure consumer privacy is protected.
The effectiveness of data de-identification methods at truly protecting the privacy of individuals.
The state of current consumer data privacy laws and how they are likely to evolve.
The impact of generative AI tools, such as ChatGPT, on consumer data privacy.

Links

UNSW Allens Hub for Technology, Law and Innovation
Katharine’s Research (SSRN Page)
Consumer Policy Research Centre
Singled Out Report
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 36: Sequential Decision Problems

Dr Genevieve Hayes — Thu, 09 May 2024 08:51:25 +1000

Decision-making is an essential part of everyday life and one of the main applications of data science is making the decision-making process easier.

However, mostly when data scientists build models, it’s to make a single decision. But in real life, decision-making is rarely that simple.

In this episode, Prof Warren Powell joins Dr Genevieve Hayes to discuss one way in which the decision-making process can become more complicated, in the form of sequential decision problems.

Guest Bio

Warren Powell is the co-founder and Chief Innovation Officer of Optimal Dynamics and a Professor Emeritus after retiring from Princeton, where he was a faculty member in the Department of Operations Research and Financial Engineering. He is also the author of Sequential Decision Analytics and Modelling and Reinforcement Learning and Stochastic Optimization.

Talking Points

What is a sequential decision problem?
Real-life examples of sequential decision problems and the disciplines in which they occur.
The four main classes of techniques for solving sequential decision problems.
How Warren’s approach to addressing sequential decision problems differs from the standard approach in this space.
The challenges of implementing sequential decision analysis techniques in practice.

Links

Connect with Warren on LinkedIn
Warren’s website (SDA Links)
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 35: Data-Driven Podcasting

Dr Genevieve Hayes — Thu, 25 Apr 2024 06:20:52 +1000

According to the Interview Valet 2023 State of Podcast Guesting Annual Report, there are over 380,000 active podcasts in the world right now, with the average podcast episode receiving just 150 downloads within 30 days of its release.

So, for individuals and organisations looking to use podcast marketing to grow their business, just booking podcast guest appearances isn’t enough. It’s necessary to use a targeted strategy based on data.

In this episode, Tom Schwab joins Dr Genevieve Hayes to discuss how Interview Valet uses data to optimise business results in podcast interview marketing.

Guest Bio

Tom Schwab is the founder and Chief Evangelist Officer of Interview Valet and the author of Podcast Guest Profits and One Conversation Away. He is also an engineer whose first job out of college involved running nuclear power plants in the US Navy.

Talking Points

What is podcast interview marketing and how it differs from traditional digital marketing approaches?
How Tom uses data to inform podcast guest marketing strategies at Interview Valet.
The most important metrics for targeting podcast marketing and optimising return on investment.
What makes a top podcast?
How Tom’s use of data and analytics has evolved over time.

Links

Interview Valet
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 34: Financial Modelling for Start-Up Founders

Dr Genevieve Hayes — Thu, 11 Apr 2024 08:02:54 +1000

Start-ups and data science go hand in hand, but usually when people think about how data science can help start-ups, it’s with regard to product development and enhancement. However, it doesn’t matter how great a start-up’s product is, if the financials are a mess, the business is going to struggle.

This is where data science can also help start-ups, in the form of financial modelling and analysis.

In this episode, Lauren Pearl joins Dr Genevieve Hayes to discuss her work in helping start-up founders translate their business ideas into maths via financial models.

Guest Bio

Lauren Pearl is a CEO-turned-CFO who helps start-up founders work better with financial data. She holds an MBA from NYU’s Stern School of Business and is the resident start-up finance expert at NYU’s Berkley Centre for Entrepreneurship.

Talking Points

What is meant by financial modelling?
The challenges of building financial models with little or no data.
Why is it important for founders to understand their financials.
The potential consequences of not understanding financial data.
How founders can use data and technology more generally to help in running their business.

Links

Connect with Lauren on LinkedIn
Lauren Pearl Consulting
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 33: Making the Shift from Data Scientist to Datapreneur

Dr Genevieve Hayes — Thu, 21 Mar 2024 07:42:29 +1100

Data science is among the most in-demand skills of the 21st century, with opportunities existing for data scientists to make a difference and earn good money as an employee in a range of industries. Yet there has also never been a better time to be a data science entrepreneur (or datapreneur).

But for data scientists who have never experienced the entrepreneurial life and who are used to the security of a steady pay check, making the transition from employee to entrepreneur may seem like an impossible leap, regardless of how desirable it may seem.

In this episode, David Shriner-Cahn joins Dr Genevieve Hayes to discuss how data scientists can escape the corporate world and make the transition from employee to datapreneur.

Guest Bio

David Shriner-Cahn is the podcast host and community builder behind Smashing the Plateau, an online platform offering resources, accountability, and camaraderie to high-performing professionals who are making the leap from the corporate career track to entrepreneurial business ownership.

Talking Points

How entrepreneurship differs from being a regular employee, particularly with regard to mindset.
The advantages and disadvantages of each way of making a living.
Making the transition from employment to entrepreneurship and how to gauge if entrepreneurship is right for you.
Building your network as an entrepreneur.
How taking a sabbatical can help ease the transition between being an employee and an entrepreneur.
The value of community.

Links

Smashing the Plateau
Connect with Genevieve on LinkedIn
Value Driven Data Science has recently featured in Feedspot’s list of the 4 Best Australian Data Science Podcasts. Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 32: Blockchain and Cryptocurrency for Data Science

Dr Genevieve Hayes — Thu, 07 Mar 2024 09:22:02 +1100

Depending on who you speak to blockchain and cryptocurrency are either the way of the future or the scam of the century. But few would be able to tell you what either of them actually is – including among data scientists for whom data and technology are a way of life.

In this episode, Luke Willis joins Dr Genevieve Hayes to demystify blockchains, cryptocurrency and the data behind them.

Guest Bio

Luke Willis is the dApp UX guy. He’s a web3 developer with extensive front end and UX experience. He’s also the founder of the Koin Press where he writes a regular newsletter, hosts the Koin Press podcast and helps others make their dApp ideas a reality.

Talking Points

What is the blockchain?
The different types of blockchains and the differences between them?
How the blockchain relates to cryptocurrency.
What is a dApp and Luke’s experiences in building them.
How data is stored on the blockchain and how it can be accessed.

Links

Luke’s Website
Koinos Blocks
Koiner
Etherscan
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 31: The Business Leader as Data Consumer

Dr Genevieve Hayes — Thu, 22 Feb 2024 07:53:31 +1100

When data science first became the must-have skill of the 21st century, organisations were fighting to recruit the best and brightest data science talent. But the glory of having a data scientist on staff was often short-lived, as many organisations soon found they didn’t know what to do with them.

Business leaders had been sold the dream of being able to turn their data into business gold but were unable to maximise the value of the data science expertise they had brought in because they couldn’t communicate effectively with their new data science teams.

In this episode, Dr Howard Friedman joins Dr Genevieve Hayes to discuss how adopting a customer mindset can help business leaders capitalise on the hidden value of data.

Guest Bio

Dr Howard Steven Friedman is a data scientist, health economist, and writer with decades of experience leading data modelling teams in the private sector, public sector and academia. He is an adjunct professor, teaching data science, statistics, and program evaluation, at Columbia University, and has authored/co-authored over 100 scientific articles and book chapters in areas of applied statistics, health economics and politics. His previous books include Ultimate Price and Measure of a Nation, which Jared Diamond called the best book of 2012.

Talking Points

How Howard’s personal experiences informed the writing of Winning with Data Science.
What business leaders should know, in order to be effective customers of data science teams.
How important is it for business leaders to be up to date with the latest data science trends and buzzwords?
What data scientists should know in order to work more effectively with business leaders.
Howard’s previous book, Ultimate Price.
How data scientists and economists go about placing a price on human life.

Links

Connect with Howard on LinkedIn
Howard’s website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 30: Cause and Effect Data Science

Dr Genevieve Hayes — Thu, 08 Feb 2024 08:00:07 +1100

Correlation does not equal causation, as anyone who has studied statistics or data science would know. But understanding causality isn’t just important when you’re developing models.

If you’re working in business and want to be recognised for your work, it’s essential to be able to demonstrate causality between what you do and the benefit flowing through to the business.

In this episode, Mark Stouse joins Dr Genevieve Hayes to discuss how data science can be used to comprehend the underlying cause-and-effect relationships in business data.

Guest Bio

Mark Stouse is the CEO of Proof Analytics, an AI-driven marketing analytics platform. Prior to becoming an analytics software CEO, Mark had a successful career in B2B marketing and in 2014 was named Innovator of the Year at the Holmes Report In2 SABRE Awards for his work in tying marketing and communication investment to key business performance metrics.

Talking Points

The benefits to organisations of understanding causality.
How such techniques can be applied to use cases and disciplines beyond marketing analytics.
How data scientists can drive conversations about analytics at the C-suite level to maximise their impact.
The potential future impact of generative AI on data science and the world in general.

Links

Connect with Mark on LinkedIn
Proof Analytics
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 29: Creating Order From Data Chaos

Dr Genevieve Hayes — Thu, 14 Dec 2023 07:45:49 +1100

The insurance sector owes its existence to data and insurers were some of the first companies to utilise data expertise. Yet, being an early adopter isn’t always as great as it seems. And many big insurers are now discovering the challenges of bringing their long-established data systems into the 21st century.

In this episode, Maria Ferrés joins Dr Genevieve Hayes to discuss the complexities of creating order from data chaos in the insurance industry.

Guest Bio

Maria Ferrés is an actuary with extensive experience throughout Europe and Australia, who now specialises in establishing the enterprise data functions of multinational insurers. She is currently the Enterprise Data Officer at trade credit insurer Atradius and she also advises companies within the insurtech space on the use of data to comply with Data Protection laws.

Talking Points

The ideal state of an insurer’s enterprise data capabilities.
How to transform insurers’ data capabilities from their present, often chaotic state, to this ideal.
The challenges in transforming insurers’ data capabilities.
Where data scientists fit into the transformation process.
How to overcome resistance encountered while transforming the data capabilities of an organisation.
The impact of the GDPR on enterprise data capabilities and on the work of people using insurance data, including data scientists and Insurtechs.

Links

Connect with Maria on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 28: The Data Science Behind ChatGPT

Dr Genevieve Hayes — Thu, 30 Nov 2023 09:02:32 +1100

ChatGPT was one of the best things to ever happen to data science – not so much because of what it can do, but because, virtually overnight, it made AI and data science mainstream.

However, while most data scientists now have experience with ChatGPT and other large language model (LLM)-based technologies as end users, few have had experience in building their own LLM-based tools.

In this episode, Dr Mudasser Iqbal joins Dr Genevieve Hayes to discuss the data science behind LLMs and how to go about doing just that.

Guest Bio

Dr Mudasser Iqbal is the Founder and CEO of TeamSolve, a company dedicated to leveraging AI for digital transformation with a sustainable focus. He has extensive experience in Industrial AI, including multiple patents, and was recognised as an MIT Young Innovator. He also played a key role in the growth of his previous start-up, Visenti, and its subsequent acquisition by Xylem Inc.

Talking Points

The data science behind LLMs.
How TeamSolve’s Lily, compares to ChatGPT and the advantages of a domain-specific, private chatbot, such as Lily, over a more general, public chatbot, such as ChatGPT.
How knowledge graphs can be combined with LLMs to overcome many of the shortcomings of LLMs.
The changing attitudes of organisations around the use of generative AI tools.
What the emergence of cutting-edge AI tools, such as LLMs, mean for more traditional data science tools, such as analytics dashboards.
The future of generative AI, and the potential benefits and risks to society.

Links

TeamSolve
Connect with Mudasser on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 27: The Future of Technology in Financial Services

Dr Genevieve Hayes — Thu, 16 Nov 2023 06:52:35 +1100

Despite its conservative reputation, the financial services industry has always been a big adopter of cutting-edge technologies. Dating back more than a century, it’s also been one of the biggest employers of people with technology and data-related skills. But what does the future hold for the use of tech in the financial services industry?

In this episode, Ben Shapira joins Dr Genevieve Hayes to discuss what this future might look like and how technology is being used right now to improve the lives of consumers.

Guest Bio

Ben Shapira is a digital strategist and UX specialist turned tech entrepreneur. He is the founder and Chief Product Officer of Australian fintech start-up Dinero, as well as being a lecturer in the Master of Media and Communication program at Swinburne University.

Talking Points

Where the financial services industry is heading, regarding the use of technology and how this will affect the lives of consumers.
The types of data modelling and analysis that are possible because of the data produced by these new technologies.
What is Dineiro and how data informed its creation.
The impact of data security considerations on financial services organisations’ ability to adopt new technologies and make use of the data they produce.
Advice for data scientists looking to build a career in marketing and advertising.
How marketing techniques can be applied to data science to make data scientists more effective, regardless of their industry.

Links

Connect with Ben on LinkedIn
Dineiro
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 26: Data Storytelling and Data-Informed Education

Dr Genevieve Hayes — Thu, 02 Nov 2023 07:18:49 +1100

Data science is only useful if it can create value. And one way that value can be created is by using data to influence decision-making. Yet, to influence decisions, data scientists need to effectively communicate the outcomes of their work – which is something many struggle with. This is because effective data science communication is about more than just rattling off statistics and expecting your end users to piece them together.

In this episode, Dr Selena Fisk joins Dr Genevieve Hayes to discuss how data scientists can improve their communication by using those numbers to tell a story.

Guest Bio

Dr Selena Fisk is a data storyteller and researcher, with a background in education, who now works with the corporate sector to develop data-informed strategies. She is also the author of a number of books, including I’m Not a Numbers Person: How to Make Good Decisions in a Data-Rich World and Data-Informed Learners: Engaging Students in their Data Story.

Talking Points

What is data storytelling and how does it differ from data visualisation?
How can data scientists make use of storytelling techniques to maximise the impact of their work?
The difference between being data-informed and data-driven, and what that means for schools and businesses.
How data is being used in schools to inform learning and improve educational outcomes.
How educators can involve students in the data conversation, and what data scientists can learn from this when it comes to engaging business stakeholders in their work.

Links

Selena’s Website
Connect with Selena on LinkedIn
Follow Selena on Twitter
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 25: The Risks of Applying Data Science to Financial Modelling

Dr Genevieve Hayes — Thu, 19 Oct 2023 07:16:31 +1100

Pretty much everyone has a retirement plan, but those plans aren’t always robust enough to see you through to the finish line of life. And part of that is a direct consequence of incorrectly applying data science principles to financial modelling.

In this episode, Todd Tresidder joins Dr Genevieve Hayes to discuss the risks and limitations of using data science when planning for retirement.

Guest Bio

Todd Tresidder is a former hedge fund manager who “retired” at age 35 to become a financial consumer advocate and money coach. He now runs the popular retirement planning website FinancialMentor.com and is the author of a range of books on retirement planning and investments including How Much Money Do I Need to Retire? and The Leverage Equation.

Talking Points

What are some of the limitations of traditional financial modelling?
Examples of what can happen when traditional financial modelling goes very wrong.
How to do financial modelling the right way.
The Engineer’s Fallacy or why you shouldn’t apply pure data science to financial planning.
The implications of this for fields outside of the financial services industry.

Links

Todd’s Website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 24: AI and IP

Dr Genevieve Hayes — Thu, 05 Oct 2023 07:26:27 +1100

If you look at the list of the greatest inventions of the 20th century, you’ll find they all have two things in common. From tea bags to toasters and from cell phones to cellophane, they all take the form of physical objects, and all are, or at least were, protected by patents.

Yet, since the turn of the century, the nature of inventions has changed significantly. And many of the greatest inventions of this century now take the form of computer code or models.

But how do you protect an invention you can’t physically touch?

In this episode, Helen McFadzean joins Dr Genevieve Hayes to discuss the intersection of artificial intelligence and intellectual property.

Guest Bio

Helen McFadzean is a patent and trademark attorney, with a background in artificial intelligence and mechatronics engineering. She has successfully obtained patents, trademarks and designs for businesses in Australia and overseas in a large number of technology areas including machine learning and image classification, automation, smart devices, audio signal processing, embedded software, and control systems.

Talking Points

What is the difference between patents, trademarks and copyrights?
How do you know if an AI/ML-based invention is worth protecting and how do you protect it if it is?
What parts of an AI/ML-based invention can be protected through patent law?
The importance of good communication in capturing IP.
What happens if an invention was invented by a generative AI, rather than a human?

Links

Connect with Helen on LinkedIn
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 23: Reinforcement Learning – The Other Type of Machine Learning

Dr Genevieve Hayes — Thu, 21 Sep 2023 07:28:53 +1000

Most Intro to Machine Learning courses cover supervised learning and unsupervised learning. But did you know there is also a third type of machine learning, which was used in the development of ChatGPT and is likely to become increasingly important in the not too distant future?

In this episode, Prof Michael Littman joins Dr Genevieve Hayes to discuss reinforcement learning – the other type of machine learning – as well as his new book, Code to Joy: Why Everyone Should Learn a Little Programming.

Guest Bio

Prof. Michael Littman is an award-winning Professor of Computer Science at Brown University, specialising in reinforcement learning; is co-creator of the Machine Learning and Reinforcement Learning courses offered as part of Georgia Tech’s Online Master of Science in Computer Science (OMSCS) program; and is currently serving as Division Director for Information and Intelligent Systems at the (US) National Science Foundation. He is also the author of Code to Joy: Why Everyone Should Learn a Little Programming.

Talking Points

What is reinforcement learning and why has it traditionally been seen as “the other type of machine learning”?
Current and future applications of reinforcement learning.
How reinforcement learning is being used to create business value.
Michael’s new book, Code to Joy and why everyone should learn to code.
How non-programmers can get started with coding and what it would mean for the world if more people did code.

Links

Michael’s Website
Follow Michael on Twitter
Computing Up Podcast
Machine Learning A Cappella (Thriller Parody)
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 22: Software Engineering for Data Science

Dr Genevieve Hayes — Thu, 07 Sep 2023 07:52:00 +1000

Data science sits at the intersection of Computer Science and Statistics, so it comes as no surprise that many of the best data scientists have a computer science or software development background. And those that don’t? Well, there’s a lot they can learn from software developers.

In this episode, Ethan Garofolo joins Dr Genevieve Hayes to discuss techniques from software engineering and software development that you can use to become a better data scientist.

Guest Bio

Ethan Garofolo is a software developer and software architect, specialising in microservice-based projects and using Lean and DevOps principles to make software development teams more effective. He is the author of Practical Microservices: Build Event-Driven Architectures with Event Sourcing and CQRS and runs the Utah Microservices Meetup group.

Talking Points

What is the difference between a software engineer, software developer and software architect?
The impact of team structure and communications on software design.
How Lean and DevOps principles can be used to make technical teams run more effectively.
The benefits of pair programming and mob programming.
What is test-driven development and how can it be used to enhance the quality of data science outputs?
Using ChatGPT/AI to enhance developer capabilities.

Links

Ethan’s Website
Connect with Ethan on LinkedIn
Follow Ethan on Twitter
Follow Ethan on Twitch
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 21: Responsible Data Sourcing for AI Model Building

Dr Genevieve Hayes — Thu, 17 Aug 2023 07:46:04 +1000

The saying goes that if you’re not paying for the product, then you are the product. And every time you interact with the digital world, there’s a good chance your data is going to be harvested for some alternative use.

In this episode of Value Driven Data Science, Dr Kate Bower joins Dr Genevieve Hayes to discuss the data rights of consumers and what data scientists need to be aware of when using consumer data.

Guest Bio

Dr Kate Bower is a consumer data advocate for Australian consumer advocacy group CHOICE, following a previous career in academia, where her focus was on qualitative health research.

Talking Points

The rights and responsibilities of consumers and organisations, when it comes to personal data.
How organisations currently collect consumer data and what they are using that data for.
The use of “harvested” data in AI tools, such as ChatGPT and Stable Diffusion.
What data scientists should be aware of when sourcing data for their work.
How to source data ethically.

Links

Connect with Kate on LinkedIn
Follow Kate on Twitter
CHOICE – Consumers and Data
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 20: Using Data Science to Live Better for Longer

Dr Genevieve Hayes — Thu, 03 Aug 2023 07:26:36 +1000

We all want to live long, happy and healthy lives, and in the age of technology, it comes as little surprise that people are turning to data for help.

Between smart watches, Oura rings and even just fitness apps like Strava, we’re all generating massive quantities of personal health and fitness data each day, sometimes literally in our sleep. But that data is only valuable if it can be converted into useful insights.

In this episode of Value Driven Data Science, Dr Torri Callan joins Dr Genevieve Hayes to discuss how health tech start-ups, such as UAre, are now looking to do just that.

This is the third part of a three-part special focussing on the use of data science in start-ups.

Guest Bio

Dr Torri Callan is the Data Scientist at Australian health tech start-up UAre, as well as working as a data scientist with fintech start-up Spriggy. He has spent the past 5 years setting up AI and automated risk management for leading finance companies in Australia.

Talking Points

How UAre is using data science to encourage people to exercise more and improve their lives.
The challenges of combining data from multiple sources.
How to go about building a data product from absolutely nothing.
The importance of domain knowledge and research when building a health tech app.
What are Bayesian methods and how can they raise the level of rigour of statistical analysis?

Links

Connect with Torri on LinkedIn
UAre
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 19: The Democratisation of AI and Data Science

Dr Genevieve Hayes — Thu, 20 Jul 2023 07:40:11 +1000

Once upon a time, data scientists needed to develop programming skills to rival those of software engineers, and this limited the ability of people without such skills to make use of AI. But recently, this has changed, with the huge number of no-code and low-code tools entering the market.

In this episode, I’m joined by Geo George to discuss how start-ups are leading the way in leveraging such tools, and in the process, helping to make AI and data science available to all.

This is the second part of a three-part special focussing on the use of data science in start-ups.

Guest Bio

Geo George is a director and co-founder of Mayfly Accelerator, a company that helps founders build, grow and scale disruptive start-ups. He is also a start-up founder in his own right and has experience as an executive in the Government sector, with a focus on strategy and risk management.

Talking Points

How are start-ups facilitating the democratisation of AI and data science.
The consequences of this democratisation for current and aspiring data scientists.
How no code and low code AI and data science tools can be used to develop AI-driven products.
The impact of ChatGPT on start-ups, businesses and education in general.

Links

Connect with Geo on LinkedIn
Mayfly Accelerator
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 18: Making AI Commercially Viable

Dr Genevieve Hayes — Thu, 06 Jul 2023 07:59:23 +1000

Many data scientists dream of using their skills to develop ground-breaking AI technology. Yet, few manage to translate their dreams into commercially viable products – or even know where to begin.

In this episode, start-up founder Dr Jeroen Vendrig joins Dr Genevieve Hayes to discuss his experiences in developing AI-driven products, both in an academic setting and in a variety of organisations within the commercial world.

This is the first part of a three-part special focussing on the use of data science in start-ups.

Guest Bio

Dr Jeroen Vendrig is the Chief Technology Officer of ProofTec, an Australian technology start-up specialising in the development of AI-driven software for damage detection and assessment of high value assets. He has over 20 years’ experience in video analytics with world leading R&D labs and has over 25 patents in force.

Talking Points

The key differences between doing data science/AI in an academic setting and doing it in the commercial world.
How to go about translating academic research into commercially viable AI-based products.
What makes for a successful university/commercial collaboration?
The challenges of building AI products from scratch, including lack of data and how to tell if a project has the potential to be commercially viable.
Protecting IP for AI systems.
The impact of having real end users on AI product development.
The most valuable skills data scientists can develop for building commercial AI technologies.

Links

Connect with Jeroen on LinkedIn
ProofTec
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 17: How to Avoid an AI Scandal

Dr Genevieve Hayes — Thu, 15 Jun 2023 07:49:07 +1000

AI technology has now reached the point where it can potentially damage the reputation of an organisation, if improperly managed. As a result, many data scientists are now becoming very interested in understanding AI ethics and responsible AI.

In this episode of Value Driven Data Science, Chris Dolman joins Dr Genevieve Hayes to discuss strategies organisations and data scientists can apply to de-risk automated decisions, and in doing so, avoid an AI scandal.

Guest Bio

Chris Dolman is the Executive Manager, Data and Algorithmic Ethics at Insurance Australia Group, a Gradiant Institute Fellow and regularly contributes to external research on responsible AI and AI ethics. In 2022, he was named the Australian Actuaries Institute’s Actuary of the Year, in recognition of his work around data ethics, and was also included in Corinium Global Intelligence – Business of Data’s list of the Top 100 Innovators in Data and Analytics.

Talking Points

The risks associated with the use or design of AI-based decision-making tools.
How these risks might potentially be amplified in the case of new, cutting-edge algorithms, such as ChatGPT.
Why “boring” is sometimes better, when it comes to AI.
Examples of where things have gone wrong in the past.
Strategies for identifying and avoiding potential AI scandals before they occur.
The regulation and governance of AI, both now and in the future.

Links

Connect with Chris on LinkedIn
De-Risking Automated Decisions Report
Checkmate Humanity
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 16: Improving the Data Science Customer Experience

Dr Genevieve Hayes — Thu, 01 Jun 2023 07:25:30 +1000

The launch of Chat-GPT turned the business world upside down and left many people wondering about the future of their careers. How do you compete against AI? One solution is by delivering a superior customer experience.

In this episode, Dasun Premadasa joins Dr Genevieve Hayes to discuss why technical people often trip up when it comes to customer experience and what data scientists can do to overcome these issues.

Guest Bio

Dasun Premadasa is the founder of DASCX, an independent business analyst consultancy that helps businesses with their digital transformations and IT project delivery. He is also the host of the DASCX Show on YouTube.

Talking Points

How delivering a superior customer experience can boost your value as a data scientist.
Why technical people, such as data scientists, tend to neglect CX.
What does good CX look like?
The consequences of bad CX for data scientists and end users.
The importance of identifying the right customer when pitching a data science solution.
Strategies data scientists can employ to improve the experience of their end user.

Links

Connect with Dasun on LinkedIn
DASCX Show episode with Dasun and Genevieve
DASCX
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 15: Graph-Powered Data Science

Dr Genevieve Hayes — Thu, 18 May 2023 08:04:09 +1000

From social media to electricity grids and the internet itself, we live in a highly interconnected world. But traditional data science techniques don’t adequately allow for the relationships that can exist between data points in such networks. This is where graph data analysis comes into play.

In this episode, Dr Alessandro Negro joins Dr Genevieve Hayes to discuss how data scientists can exploit the natural relationships that exist within network datasets through the use of graph-powered machine learning.

Guest Bio

Dr Alessandro Negro is the Chief Scientist at GraphAware, the world’s #1 Neo4j consultancy, and Managing Director at GraphAware Italy. He is also the author of Graph-Powered Machine Learning and the recently released Knowledge Graphs Applied.

Talking Points

What is graph data and how does it differ from structured data?
Use cases for graphs and graph databases?
What is a knowledge graph, how are they created and what are their benefits?
How can graphs be used to power machine learning?
How can machine learning algorithms be used to build knowledge graphs?
Steps data scientists can take to get started with graph data science and knowledge graphs.

Links

Connect with Alessandro on LinkedIn
Graph-Powered Machine Learning
Knowledge Graphs Applied
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Episode 14: Building Your Authority in Data Science

Dr Genevieve Hayes — Thu, 04 May 2023 07:23:50 +1000

Data science is an in-demand skill. Yet, many data scientists find it challenging to get started in the industry and to differentiate themselves from other data scientists once they find a job.

In this episode, Jonathan Stark joins Dr Genevieve Hayes to discuss how data scientists can find their niche and build a reputation as a data science authority.

Guest Bio

Jonathan Stark is a former software developer who now helps independent professionals make a living while increasing their impact on the world. He is the author of Hourly Billing Is Nuts, the host of the podcast Ditching Hourly and the co-host of The Business of Authority.

Talking Points

How data scientists can build their authority and move away from being viewed as commodities.
The benefits of specialisation, both as an independent professional, and as an employee of a larger organisation.
Is it necessary to manage staff in order to establish your credibility as an authority in data science?
How your authority as a data scientist, once established, could potentially be leveraged both within the corporate world and as a springboard into an independent career.

Links

Jonathan’s Website
Ditching Hourly
The Business of Authority
Connect with Genevieve on LinkedIn
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Episode 13: Breeding Data Science Unicorns

Dr Genevieve Hayes — Thu, 20 Apr 2023 08:24:13 +1000

“Data science unicorns” are those rare people who “understand the (data) problem they seek to resolve, have the mathematical expertise to analyse the problem and possess the computing skills to covert this knowledge into outcomes.” In fact, they are considered so rare that some people have suggested they don’t really exist. Yet, although nobody is born a data science unicorn, organisations with the right know-how can create them.

In this episode, Dr Peter Prevos joins Dr Genevieve Hayes to discuss his work in creating data science unicorns from water industry subject matter experts around the world.

Guest Bio

Dr Peter Prevos is a civil engineer, social scientist (and amateur magician) who manages the data science function at Coliban Water in regional Australia and runs courses in data science for water professionals. He is also the author of a number of books including Principles of Strategic Data Science and the recently released Data Science for Water Utilities.

Talking Points

Why Linux is the best operating system for data science.
How the social sciences can make you a better data scientist.
Creating data science unicorns in the water industry.
The similarities and differences between data science in the water industry and in other industries.
What data scientists can learn from the world of theatrical magic.

Links

Connect with Peter on LinkedIn
Peter’s website
Computer Magic: Software Illusions and Deceptions
Computer Science 4 Fun
Connect with Genevieve on LinkedIn
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Episode 12: The Role of Data in Environmental Justice

Dr Genevieve Hayes — Thu, 30 Mar 2023 08:07:04 +1100

Are you familiar with “environmental justice”? It’s all about equitable access to environmental amenities and the equitable distribution of pollution, and has its roots in the American Civil Rights movement of the 1960’s and 1970’s.

In this episode, Robin Rotman and Amber Spriggs join Dr Genevieve Hayes to discuss the environmental justice movement and how open access GIS-based tools are being used to achieve environmental justice in the USA today.

Guest Bio

Robin Rotman is an Assistant Professor of Energy and Environmental Law and Policy at the University of Missouri-Columbia. She is also a qualified lawyer, focussing on energy, environmental, and natural resource issues, and is a Counsel at Van Ness Feldman, a law firm in Washington DC.

Amber Spriggs is a civil engineering Masters student at the University of Missouri-Columbia with a research focus on hydrology, hydraulic engineering, GIS-based risk assessment, and flood insurance policy.

Talking Points

What is environmental justice?
Why the environmental justice movement and the American Civil Rights movement are one and the same.
The role of data and analytics in achieving environmental justice both now and when the term was first coined.
Examples of how spatial data analysis has been used to achieve environmental justice.
How similar techniques could potentially be used to achieve positive outcomes for the community in other ways.
The role of data and analytics in legal proceedings relating to environmental justice.

Links

Connect with Robin on LinkedIn
Connect with Amber on LinkedIn
EJScreen
Connect with Genevieve on LinkedIn
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Episode 11: Better Workplace Conversations for Data Scientists

Dr Genevieve Hayes — Thu, 16 Mar 2023 07:26:42 +1100

Data scientists are constantly being told of the importance of effective communication for their career success. But this advice typically translates to being able to communicate effectively the results of their work. One aspect of communication that is often overlooked is conversational communication.

In this episode, Julia Lessing joins Dr Genevieve Hayes to discuss the skills and techniques data scientists can combine to make their workplace conversations a lot easier.

Guest Bio

Julia Lessing is the principal actuary and Director of Guardian Actuarial, which specialises in helping clients use data to solve complex people-oriented problems, and runs the Guardian Actuarial Leadership Program and the Easier Conversations course. She is also the host of the We Are Actuaries podcast and has trained and served as a Lifeline phone counsellor.

Talking Points

The importance of effective conversations in the workplace.
Common stumbling blocks for technical people, when it comes to effective communication.
The potential consequences of poor workplace conversations and the benefits of good conversations.
The key skills involved in conducting an effective conversation.
How you go about preparing for important conversations
Conducting effective conversations within large groups and how to make meeting communications more effective.

Links

Connect with Julia on LinkedIn
Easier Conversations
Connect with Genevieve on LinkedIn
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Episode 10: ChatGPT and the Future of Human Computer Interaction

Dr Genevieve Hayes — Thu, 02 Mar 2023 07:29:38 +1100

In December 2022, OpenAI released ChatGPT for public testing and within a week of its launch, the user count exceeded 1 million. For many, ChatGPT provided a first glimpse at what an AI-powered future might look like.

In this episode, Dr Genevieve Hayes is joined once again by Dr David Joyner to discuss the implications of AI-driven technology, such as ChatGPT, for education, business and the world in general, and to finish their discussion of Georgia Tech’s OMSCS program.

This is the second part of a two-part conversation, which began in Episode 9.

Guest Bio

Dr David Joyner is the Executive Director of Online Education and the Online Master of Science in Computer Science at Georgia Tech’s College of Computing. Between 2019 and 2021 he taught a total of 21,768 for-credit college students, more than any other person on the planet. He is also the author of the recently released Teaching at Scale, and co-author of The Distributed Classroom.

Talking Points

The evolution of human computer interaction.
The role of Masters programs vs shorter courses in helping data scientists keep up with the latest technological developments.
Why ChatGPT is a game changer for education and business in general.
The opportunities and challenges presented by AI chatbots, such as ChatGPT.
The importance of understanding the context when analysing data.

Links

David’s website
Georgia Tech’s OMSCS
Connect with Genevieve on LinkedIn
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Episode 9: Learning Data Science at Scale with OMSCS

Dr Genevieve Hayes — Thu, 16 Feb 2023 07:18:28 +1100

What if you could get a Masters degree in Machine Learning for under US$8000, from a top US university, without quitting your day job or moving location? Georgia Tech’s pioneering Online Master of Science in Computer Science (OMSCS) program offers just that.

In this episode, Dr David Joyner joins Dr Genevieve Hayes to discuss OMSCS, the world’s first MOOC-based degree.

This is the first part of a two-part conversation, which is continued in Episode 10.

Guest Bio

Talking Points

What is the OMSCS?
How OMSCS compares to other Computer Science Masters programs and MOOCs?
How online education can help data scientists keep pace with the rapidly changing technological landscape.
Challenges and opportunities associated with teaching and learning in the online space.
Why Georgia Tech students talk about “getting out” instead of “graduating”.

Links

David’s website
Georgia Tech’s OMSCS
Connect with Genevieve on LinkedIn
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Episode 8: Data Science in the Metaverse

Dr Genevieve Hayes — Thu, 02 Feb 2023 07:33:01 +1100

Ever since Facebook rebranded itself as Meta, the term “metaverse” has entered everyone’s vocabulary, but there’s still a lot of confusion about what it actually is and how it’s likely to affect our lives in the future.

In this episode, Romeo Cabrera Arévalo, a data scientist working in the immersive technology space, joins Dr Genevieve Hayes to answer these questions and more.

Guest Bio

Romeo Cabrera Arévalo is a senior AI and computer vision researcher and engineer at Immersed, “the world’s first professional metaverse.” He is also an AI and tech advisor to the Board of Laboratorio iA, and has lectured in the Masters of Data Science program at the Escuela Superior Politéchnica del Litoral.

Talking Points

What is the metaverse and why should people care?
The difference between virtual reality, augmented reality and mixed reality.
The potential benefits of immersive technologies, both now and in the future.
The role of AI, data science and machine learning in the metaverse.
The types of algorithms and techniques that go into building metaverse technologies.
The “uncanny valley” and the challenge of giving avatars legs in the metaverse.

Links

Connect with Romeo on LinkedIn
Immersed
Connect with Genevieve on LinkedIn
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Episode 7: Finding and Retaining the Best Data Talent

Dr Genevieve Hayes — Thu, 15 Dec 2022 07:42:30 +1100

Over the past decade, demand for data talent has grown exponentially, and this has had a massive impact on talent acquision in the data space. Employers of data professionals frequently cite talent acquisition as one of the biggest challenges they face in building their internal data capabilties.

In this episode, Dr Genevieve Hayes is joined by data recruiter Joel Robinstein to discuss the data science recruitment landscape, including practical advice for both data scientists and those looking to employ them.

Guest Bio

Joel Robinstein is Head of Clients Services and Operations at Precision Sourcing Australia, where he has over 12 years’ experience working in the data recruitment space. He is also the co-host of the podcast Keeping Up With Data.

Talking Points

The evolution of the data science recruiting space over the last 10 years.
What employers look for when hiring data staff, what data scientists look for in prospective employers, and how this varies by role seniority.
Strategies organisations can employ to identify, attract and retain data talent.
What makes a great data science leader.
Career paths available to data scientists and how you can make them happen.

Links

Connect with Joel on LinkedIn
Keeping up with Data podcast
Connect with Genevieve on LinkedIn
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Episode 6: Bridging the Chasm Between Data Science and Engineering

Dr Genevieve Hayes — Thu, 01 Dec 2022 07:58:50 +1100

The success of data science projects often depends on being able to get stakeholders, from a variety of backgrounds, to work well together. But what if the stakeholders involved come from very different backgrounds and struggle to understand each other – as can be the case with data scientists and engineers?

In this episode, Dr Genevieve Hayes is joined by software engineer turned data scientist Hendrik Dreyer, who has carved a niche for himself by acting as a intermediary between Team Data Science and Team Engineering.

Guest Bio

Hendrik Dreyer is both a qualified data scientist and a qualified engineer. He worked extensively in a range of senior software engineering roles, in both South Africa and Australia, prior to making the transition into data science. He is now the Manager of Analytics Capability at Australia’s largest superannuation fund, AustralianSuper.

Talking Points

The different mindsets commonly held by data scientists, data engineers and the business in general, when it comes to data science and analytics.
How these diverse mindsets can give rise to challenges, when it comes to delivering data science solutions, and the potential consequences if these challenges aren’t adequately addressed.
Approaches to bridging the chasm between data science, data engineering and the business.
Actions each of these different groups of people can take in order to help bridge the divide within their own organisations.
The unexpected benefit of agile project management as a people development tool.

Links

Connect with Hendrik on LinkedIn
Connect with Genevieve on LinkedIn
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Episode 5: Identifying Data Science Use Cases for your Business

Dr Genevieve Hayes — Thu, 17 Nov 2022 07:26:27 +1100

Businesses rarely approach data scientists with well-defined problems to solve. Sometimes, the problems businesses devise aren’t appropriate for solving using data science at all. This makes it difficult for data science projects to succeed.

In this episode, Dr Genevieve Hayes is joined by Rob Deutsch to discuss strategies businesses and data scientists can employ to identify data science use cases and maximise their probability of success.

Guest Bio

Rob Deutsch is the Chief Operating Officer of AkuShaper, a company that uses advanced modelling algorithms and software to build better surfboards faster. He is also a data science consultant with Parity Analytic, and previously founded Boxer, which built software for creating better financial models.

Talking Points

Processes for identifying and understanding business problems, and determining whether a data science solution is appropriate and what that solution should look like.
The different ways in which people from different backgrounds can look at a data science problem and how that influences the questions they ask of data/the way they tackle problems.
The role of the business vs the role of the data scientist in defining/scoping data science projects.
How to maximise the probability of success of a data science project.
How the data science/data analytics skill set can be transferred to areas outside of technical data analysis, such as running a SaaS company.

Links

Connect with Rob on LinkedIn
Bird App XKCD Comic
Connect with Genevieve on LinkedIn
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Episode 4: The Role of the Board in Maximising the Value of Data

Dr Genevieve Hayes — Sun, 30 Oct 2022 11:07:16 +1100

Have you ever wondered what your organisation’s Board are thinking, when it comes to data use?

In this episode, Dr Genevieve Hayes is joined by Dr Stuart Black to discuss the attitudes of Boards to data use and their implications for the organisations they govern.

Guest Bio

Dr Stuart Black is an Enterprise Fellow in data, analytics, disruption and innovation at the University of Melbourne. Prior to joining academia, Stuart spent 30 years in professional services and industry, at employers including Deloitte, where he was Senior Partner, National Australia Bank and AT Kearney. He is also a co-author of the recently released book Business Model Transformation – the AI and Cloud Technology Revolution.

Talking Points

The Board’s role in catalysing and controlling data-driven business model transformation.
What is meant by the secondary use of data and what are some of the opportunities and threats presented by it?
Why intellectual curiosity is more important than prior data experience in maximising the competitive advantage of data.
The importance of taking a medium term view when it comes to data initiatives.
The key Board attributes that determine an organisation’s attitudes towards data as an enabler of strategy.
Strategies for shifting the attitude of your Board in order to encourage a mindset that is more supportive of data initiatives.

Links

Connect with Stuart on LinkedIn
Stuart’s University of Melbourne Profile
Business Model Transformation – the AI and Cloud Technology Revolution Microsite
Connect with Genevieve on LinkedIn
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Episode 3: Fairness and Anti-Discrimination in Machine Learning

Dr Genevieve Hayes — Mon, 17 Oct 2022 07:58:37 +1100

We all know what it means for a human to discriminate against another human, but the concept of a predictive model or an artificial intelligence is relatively new. What does it mean for a model or an AI to discriminate against someone?

In this episode of Value Driven Data Science, Dr Genevieve Hayes is joined by Dr Fei Huang to discuss the importance of considering fairness and avoiding discrimination when developing machine learning models for your business.

Guest Bio

Dr Fei Huang is a senior lecturer in the School of Risk and Actuarial Studies at the University of New South Wales, who has won awards for both her teaching and her research. Her main research interest is predictive modelling and data analytics, and has recently been focussing on insurance discrimination and pricing fairness.

Talking Points

Direct vs indirect discrimination and how data scientists can create discriminatory machine learning models without ever intending to.
What it means for a model to be fair and the trade-off that exists between individual and group fairness.
How fairness and discrimination come up (and have been addressed) in different applications of machine learning, including (but not limited to) insurance.
How different jurisdictions are currently addressing algorithmic discrimination, through regulation and other means.
What this means for organisations who currently make use of machine learning models or would like to in the future.
Why organisations should start considering fairness and discrimination when using analytics and what they can do about it now.

Links

Fei’s papers on fairML and insurance pricing:

Anti-Discrimination Insurance Pricing: Regulations, Fairness Criteria and Modelling
The Discriminating (Pricing) Actuary
Welfare Implications of Fairness and Accountability for Insurance Pricing
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Episode 2: Leading a Technical Team – Transitioning from Individual Contributor to Manager and Beyond

Dr Genevieve Hayes — Mon, 03 Oct 2022 08:07:38 +1100

The two most challenging transitions you can make in your career are transitioning from individual contributor to team lead, and moving from team lead to managing managers. This is true across all professions, but is particularly pronounced in technical fields, like data science.

In this episode, host Dr Genevieve Hayes is joined by guest Tim Davey to discuss the challenges faced by data scientists looking to climb the corporate ladder, and how employers of data professionals can support them in developing their careers.

Guest Bio

Tim Davey has spent the majority of his career working in the organisational development and HR space where his work has focussed strongly on the development of leaders and working with individuals to understand and maximise their careers. This has included, among other things, providing executive coaching to senior management across a wide range of industries, including media, the performing arts, manufacturing, financial services, transport, education, insurance, legal, and not-for-profit sectors.

Yet, Tim also has a strong technical background himself, having completed a Science degree at the University of Melbourne, and starting his working career in the chemical manufacturing sector, so has first-hand understanding of the challenges faced by the members and leaders of technical teams.

Talking Points

The key differences between working as an individual contributor vs line manager vs senior manager.
Why people can struggle to make the transition between data scientist and team lead and what can be done to make it easier.
The importance of technical capability vs managerial skills in technical leadership roles, and how organisations can support staff to develop those skill sets if one is lacking or weaker.
Managing a team and building your profile in the post-COVID, remote working world.
Advice for data scientists considering moving into managerial roles – and for those who would prefer to remain an individual contributor.

Links

Connect with Tim on LinkedIn
Connect with Genevieve on LinkedIn
Download the FREE Data Science Project Discovery Guide
Genevieve Hayes Consulting offers one-on-one coaching for new and aspiring data science and analytics leaders. To find out more, or to share your thoughts and feedback on the podcast, you can get in touch here.
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Episode 1: Building Data Science Capability in Data-Focussed Teams

Dr Genevieve Hayes — Fri, 16 Sep 2022 08:03:26 +1000

Data presents incredible opportunities for organisations to create value, but with the current skills and labour shortages that are affecting all businesses, finding and retaining data scientists and other data professionals can be hard.

In this episode, host Dr Genevieve Hayes is joined by guest Amanda Aitken to discuss a practical way in which organisations can address the skills shortage, gain much needed data skills and increase staff retention – by upskilling their existing staff.

Guest Bio

Amanda Aitken is a fully-qualified actuary who is currently an educator with the Actuaries Institute of Australia. She teaches data analytics and data science to actuaries through the Actuaries Institute’s Data Analytics Application course and is also a member of the Institute’s Data Analytics Practice Committee and Data Analytics Education Faculty.

Talking Points

The difference between an actuary and a data scientist.
Why data skills are becoming increasingly important and the benefits to organisations of upskilling their data team.
What’s involved in upskilling data-focussed staff.
The importance of considering “soft skills”, such as communication, privacy and ethics, when training data scientists.
How an organisation can get the most out of their data staff once they have been upskilled.

Links

Connect with Amanda on LinkedIn
Data Science Applications Microcredential
Data Analytics Seminar
Machines Behaving Badly by Toby Walsh
How Humans Judge Machines by Cesar Hidalgo
Download the FREE Data Science Project Discovery Guide
Connect with Genevieve on LinkedIn
To find out more about custom data science training from Genevieve Hayes Consulting or to share your thoughts and feedback on the podcast, you can get in touch HERE.
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