Value Driven Data Science

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

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

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.

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

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

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

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