Genevieve Hayes Consulting

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.

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.

• (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

• Keebo website
• Connect with Barzan on LinkedIn

• Connect with Genevieve on LinkedIn
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The post Episode 52: Automating the Automators – How AI and ML are Transforming Data Teams first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting

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.

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.

• (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

• Grapho website

• Connect with Genevieve on LinkedIn
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The post Episode 51: Data Storytelling in Virtual Reality first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

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.

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.

Genevieve Hayes Consulting

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.

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.

• (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

• 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

The post Episode 49: AI-Generated Advertising and the Future of Content Creation first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting

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.”

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.

• (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

• BizML website

• Connect with Genevieve on LinkedIn
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The post Episode 48: Overcoming the Machine Learning Deployment Challenge first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting

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.

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.

• (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

• 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

The post Episode 47: Leveraging Causal Inference to Drive Business Value in Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting

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.

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.

• (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

• 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

The post Episode 46: Empowering Democracy with LLMs first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting

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.

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.

• (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

• Levelfields website

• Connect with Genevieve on LinkedIn
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The post Episode 45: AI-Powered Investment Insights first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting

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.

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.

• 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)

• 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

The post Episode 44: Designing Data Products People Actually Want to Use first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting

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.

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.

• (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

• Connect with Eric on LinkedIn
• Conexus AI website

• Connect with Genevieve on LinkedIn
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The post Episode 43: Shaping the Future of AI first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.