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:

1. The two priorities that can unlock budget even mid-cycle (and why cost savings isn't one of them) [07:50]
2. How executive priorities evolve across technology adoption cycles [10:16]
3. Why misaligned compensation metrics doom data science projects [13:03]
4. 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

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:

1. How to build a systematic framework for evaluating AI models [02:05]
2. The key metrics that help you compare different models objectively [02:28]
3. Why understanding speed-cost-accuracy tradeoffs gives you an edge [05:47]
4. 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

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:

1. The critical business questions most technical experts never think to ask [02:21]
2. Why understanding business context makes you better at technical work (not worse) [14:10]
3. How to turn natural curiosity into career opportunities without losing your technical edge [09:19]
4. The simple mindset shift that helps you spot business impact others miss [21:05]

Guest Bio

Andrei Oprisan is a technology leader with over 15 years of experience in software engineering, specializing in product development, machine learning, and scaling high-performance teams. He is the founding Engineering Lead at Agent.ai and is also currently completing an Executive MBA through MIT’s Sloan School of Management.

Links

• Connect with Andre on 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

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:

1. The critical business context most data scientists overlook when presenting their work [02:10]
2. How to ensure your technical content works as hard as you do – whether presented live or shared asynchronously [04:42]
3. 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

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:

1. Why the “last mile” is killing your data science impact – and how to fix it through strategic collaboration [01:00]
2. The counterintuitive findings about Gen AI that sparked global attention (including a 40% increase in code defects) [13:02]
3. How to transform “disappointing” technical results into compelling business narratives that drive real change [17:15]
4. 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

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:

1. The Kanban and focus currency techniques that replace notification-driven chaos [02:09]
2. A 90-day planning system that beats imposter syndrome and drives results [03:09]
3. Why two-hour focus blocks outperform constant context switching [04:19]
4. 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

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:

1. Why lacking clear intention kills productivity — and how to ensure every analysis drives real decisions. [02:11]
2. A powerful “storyboarding” framework for turning vague requests into actionable projects. [09:51]
3. How to deliver results faster using modern data architectures and raw data analysis. [13:19]
4. 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

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:

1. Why focusing purely on technical metrics like accuracy and precision is sabotaging your success — and what metrics actually matter to business leaders. [04:18]
2. The critical mindset shift needed to transform from a back-room technical specialist into a valued business partner. [30:33]
3. How to present data science insights in ways that drive action — and why your fancy graphs might be hurting rather than helping. [25:08]
4. 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

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

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