Value Driven Data Science

Genevieve Hayes Consulting Episode 52: Automating the Automators – How AI and ML are Transforming Data Teams 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 websiteConnect with Barzan on LinkedIn Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE 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 Episode 51: Data Storytelling in Virtual Reality 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 LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE 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. 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

Genevieve Hayes Consulting Episode 49: AI-Generated Advertising and the Future of Content Creation 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 websiteConnect with Hikari on LinkedInFollow Hikari on X Connect with Genevieve on LinkedInBe 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 Episode 48: Overcoming the Machine Learning Deployment Challenge 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 LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE 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 Episode 47: Leveraging Causal Inference to Drive Business Value in Data Science 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 LinkedInJoanne’s website Connect with Genevieve on LinkedInBe 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 Episode 46: Empowering Democracy with LLMs 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.nycVikram’s websiteVikram’s talk at NYC School of Data about citymeetings.nycFollow Vikram on X Connect with Genevieve on LinkedInBe 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 Episode 45: AI-Powered Investment Insights 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 LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 45: AI-Powered Investment Insights first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 44: Designing Data Products People Actually Want to Use 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 listDesigning for AnalyticsData Product Leadership CommunityCED framework Connect with Genevieve on LinkedInBe 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 Episode 43: Shaping the Future of AI 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 LinkedInConexus AI website Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 43: Shaping the Future of AI first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 42: Should You Outsource Your Data Team? 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 LinkedInNorth Labs website Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 42: Should You Outsource Your Data Team? first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 41: Building Better AI Apps with Knowledge Graphs and RAG 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 LinkedInGraphlit websiteFollow Graphlit on X Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 41: Building Better AI Apps with Knowledge Graphs and RAG first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 40: Making Data Science Teams Profitable 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 websiteSquirrel Squadron Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 40: Making Data Science Teams Profitable first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 39: The Impact of Data Science on Data Orchestration 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 LinkedInFollow Sandy on XDagster Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 39: The Impact of Data Science on Data Orchestration first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 38 – The Art and Science of Survey Design 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 LinkedInGradient Website Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 38 – The Art and Science of Survey Design first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 37: Data Privacy in the Age of AI 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 InnovationKatharine’s Research (SSRN Page)Consumer Policy Research CentreSingled Out Report Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 37: Data Privacy in the Age of AI first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 36: Sequential Decision Problems 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 LinkedInWarren’s website (SDA Links) Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 36: Sequential Decision Problems first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 35: Data-Driven Podcasting 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 LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 35: Data-Driven Podcasting first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 34: Financial Modelling for Start-Up Founders 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 LinkedInLauren Pearl Consulting Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 34: Financial Modelling for Start-Up Founders first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 33: Making the Shift from Data Scientist to Datapreneur 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 LinkedInValue Driven Data Science has recently featured in Feedspot’s list of the 4 Best Australian Data Science Podcasts. You can be among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 33: Making the Shift from Data Scientist to Datapreneur first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.