My guest in this episode is Evan Shellshear, an expert in artificial intelligence and co-author of the eye-opening book "Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype." With nearly two decades of experience in developing AI tools, Evan shares his insights into the real challenges and pitfalls of data science projects, and how organizations can overcome these hurdles. About Evan Shellshear: Evan is a renowned AI expert with a Ph.D. in Game Theory from the University of Bielefeld. He has worked globally with leading companies across various industries, using advanced analytics to drive innovation and efficiency. As an author, his work seeks to demystify the complexities of AI and guide organizations toward successful implementation. Episode summary: In this episode, we explore the critical themes of Evan's book, which aims to shed light on why so many data science projects fall short of their potential. We unpack the exaggerated promises and oversimplifications that often lead to these failures, and discuss practical strategies to avoid them. Discussion highlights: Why Do Data Science Projects Fail? Evan discusses the common pitfalls, including unrealistic expectations and lack of understanding of project complexities.Balancing costs and benefits: How organizations can weigh the costs of failure against the potential benefits of successful data science projects.Avoiding failures: Practical advice on increasing success rates by setting realistic goals and aligning projects with business priorities.Impact of organizational culture: How cultural factors within a company can make or break data science initiatives.Measuring success: Effective metrics and indicators for evaluating project outcomes.You can find out more about Evan's book here, and connect with him via LinkedIn.

In this episode, I’m joined by the remarkably versatile Akshay Swaminathan, a polyglot who speaks 11 languages and has carved a unique path from medicine to data science. Currently an MD-PhD candidate at Stanford, Akshay's work has taken him from building clinics in Bolivia to pushing the boundaries of healthcare through data science. Akshay's journey is not just about his professional achievements but also his personal commitment to continuous learning and making a global impact. His transition from medicine to data science was driven by his desire to leverage technology for social good, particularly in healthcare. We also explore Akshay's book "Winning with Data Science" aimed at business professionals seeking to integrate data science into their operations. In short, Akshay might just be the most interesting person you’ll come across this year. Previous episode: Ultralearning: How to Master Hard Skills and Accelerate Your Career with Scott Young Akshay's website: https://www.akshayswaminathan.com/ Akshay on LinkedIn: https://www.linkedin.com/in/akshay-swaminathan-68286b51/

Sandy Iyer has been General Manager of Data Science at Sportsbet since the beginning of 2023, leading a dynamic team that leverages data in innovative ways. But what does it take to lead in such a data-driven environment? How does one balance the promotion of betting products with social responsibility? And how does data shape the strategy of a betting giant like Sportsbet? These are just a few of the questions we'll explore today. I’ve watched Sandy's career trajectory skyrocket in the last few years, and It's been nothing short of inspiring. In this conversation we explore the key elements behind her impressive progression, including the leadership lessons has she gleaned from her time in the trenches of data science. And more importantly, Sandy explains how can you apply these insights to your own career. From discussing unique data science use cases that have propelled Sportsbet's success, to exploring emerging trends that will shape the future of the betting industry, Sandy offers a wealth of insights. She also shares personal stories of challenges faced and overcome, revealing the qualities essential for any budding data scientist aspiring to become a senior analytics leader.

As the digital landscape evolves, privacy concerns and regulations are becoming increasingly important for advertisers. With the decline of third-party cookies and the rise of individual data usage consent, measuring advertising attention is more crucial than ever. One of the biggest challenges for advertisers in a cookie-less world is being able to accurately measure the effectiveness of their campaigns. Without cookies, it's harder to track user behaviour and understand how their ads are performing. However, measuring advertising attention through alternative methods such as viewability, brand lift studies, and surveys can be helpful, but they provide vague and delayed signals about advertising effectiveness. How can advertisers measure the attention and effectiveness of their advertising in real-time? To answer this question, I recently spoke to John Hawkins, Chief Scientist at Playground XYZ. Playground XYZ provides a machine learning-based platform for measuring and maximising attention on digital ads. The company’s Attention Intelligence Platform is a unique technology that uses over 40 different signals to track user attention as it happens. In this episode of Leaders of Analytics, we discuss: How Playground’s attention measurement platform works in practiceThe importance of attention time in a world without cookies, where privacy and consent are increasingly of mandated importanceDealing with the complexities of multi-layered machine learning pipelines and convincing stakeholders of their valueHow data science professionals can foster the right non-data science skills that will make them true unicorns, and much more.John on LinkedIn: https://www.linkedin.com/in/hawkinsjohnc/ John's book, Getting Data Science Done.

How does a traditional bricks-and-mortar retailer transform itself into an omni-channel business with strong digital and data science capabilities? In this episode of Leaders of Analytics we learn from Bunnings General Manager, Data and Analytics, Genevieve Elliott, how the company is transforming its operations using data and analytics. As Australia and New Zealand’s largest retailer of home improvement products, Bunnings is a highly complex organisation with a large physical footprint, a wide product range and an elaborate supply chain. Bunnings is almost 130 years old and has undergone tremendous growth over the last three decades. The company’s well-known strategy of “lowest price, widest range and best customer experience” is increasingly being driven by the company’s growing data and analytics capability. In this episode we discuss: Genevieve’s career journey and how she ended up in data and analyticsHow Bunnings uses data to create operational efficiencies, improve customer experience and optimise pricingHow the team prioritises projects and engages with the organisationHow the Data & Analytics team is driving a data-driven culture through the companyGenevieve’s advice to other analytics leaders wanting to drive strategically important results for their organisation, and much more.Genevieve Elliott on LinkedIn: https://www.linkedin.com/in/genevieve-elliott/

Is your company good at customer success and retention? Chances are that you could be better. For most businesses with a recurring revenue model, customer churn is a very costly affair. Whenever a customer leaves, you lose out on recurring revenue, forgo the opportunity of expansion (cross sell) revenue and have to pay for another round of acquisition costs to cover the loss. In my personal experience, customer retention is both art and science. Machine learning and other data science techniques can be used to identify customers who are likely to churn, but it is equally important to craft meaningful and delightful interactions throughout the customer lifecycle. So, what’s required to become a lean, mean retention machine? In this episode of Leaders of Analytics, I speak to Sami Kaipa to learn the best practices of data-driven customer retention.  Sami is an experienced technology executive, serial entrepreneur and start-up advisor. He is co-founder of Tingono, an AI-driven customer retention platform. Listen to this episode as we discuss: Sami's journey as an entrepreneur and corporate technology executiveThe core elements of customer success and retention that every business should masterA deep dive into the concepts of customer retention, expansion and NRRThe economics of customer retention and expansionHow data science and machine learning can help with retention, and much more.Connect with Sami on LinkedIn: https://www.linkedin.com/in/samkaipa/ Tingono's blog: https://www.tingono.com/blog

Most of us take for granted that food is always available to us when we need it. Our local supermarkets have shelves stacked with produce from all corners of the world. Rarely do we stop to think that the items in our shopping carts have been on a long journey involving months of work by many people. How does all this food get produced in the first place, reliably, consistently and to a high standard? How do we combine and utilise scarce resources to feed billions of people around the world every day? I recently caught up with Serg Masis to answer these questions and understand how data science is used to optimise food production around the world. Serg is a Climate & Agronomic Data Scientist at global agriculture company Syngenta and author of the book ‘Interpretable Machine Learning with Python’. In this episode of Leaders of Analytics, we discuss: The biggest challenges facing our global food system and how data science can help solve theseHow data science is used to help the environmentWhy Serg wrote the book ‘Interpretable Machine Learning with Python’ and why we should read itHow to make models more interpretable, and much more.Connect with Serg: Serg's website: https://www.serg.ai/#about-me Serg on LinkedIn: https://www.linkedin.com/in/smasis/ Serg's books from Packt: https://www.packtpub.com/authors/serg-masis

Professional sports have undergone a true data revolution over the last two decades. Today, all major sports teams, regardless of sports code, use analytics and data science to drive team performance, optimise game outcomes and scout young talent. Why has analytics become so popular in professional sports and how does it help drive a competitive edge? To answer these questions and many more relating to the sports analytics, I recently spoke to Ari Kaplan. Ari has spent more than three decades using analytics to measure and understand human ability, scout future superstars and win professional sports titles. He is known as “The Real Moneyball Guy” because of his work in baseball and his involvement in making the Hollywood classic Moneyball. Today, Ari is Global AI Evangelist at DataRobot. Listen to this episode of Leaders of Analytics to learn: How Ari became “the Real Moneyball Guy”The analytics the Chicago Cubs used to break a 108-year drought by winning the World Series in 2016The evolution of analytics and data science in sportsWhat the business world can learn from sports in terms of using analytics to gain a competitive edgeWhere sports analytics is going in the future, and much more.

In a recent conversation with data warehousing legend Bill Inmon, I learned about a new way to structure your data warehouse and self-service BI environment called the Unified Star Schema. The Unified Star Schema is potentially a small revolution for data analysts and business users as it allows them to easily join tables in a data warehouse or BI platform through a bridge. This gives users the ability to spend time and effort on discovering insights rather than dealing with data connectivity challenges and joining pitfalls. Behind this deceptively simple and ingenious invention is author and data modelling innovator Francesco Puppini. Francesco and Bill have co-written the book ‘The Unified Star Schema: An Agile and Resilient Approach to Data Warehouse and Analytics Design’ to allow data modellers around the world to take advantage of the Unified Star Schema and its possibilities. Listen to this episode of Leaders of Analytics, where we explore: What the Unified Star Schema is and why we need itHow Francesco came up with the concept of the USSReal-life examples of how to use the USSThe benefits of a USS over a traditional star schema galaxyHow Francesco sees the USS and data warehousing evolving in the next 5-10 years to keep up with new demands in data science and AI, and much more.Connect with Francesco Francesco on Linkedin: https://www.linkedin.com/in/francescopuppini/ Francesco's book on the USS: https://www.goodreads.com/author/show/20792240.Francesco_Puppini

This is the second episode of a two-part series of Leaders of Analytics featuring global data science thought leader and influencer Felipe Flores. Felipe is a global thought leader and influencer in the field of data science and artificial intelligence. He is the founder of Data Futurology – a podcast and events company with more than 10,000 weekly listeners, Head of Data & Technology at Honeysuckle Health and co-organiser of Data Science Melbourne. In this episode we discuss: Felipe’s work at Honeysuckle HealthWhat Honeysuckle Health does and why the company was founded by two large insurance organisationsHow data-driven personalised health care works in practice and the typical outcomes patients seeHow data will be used to drive positive health outcomes in the future, and much more.

Automated decisions, personalised customer and employee experiences and data-driven decision-making are at the core of digital transformation in the 2020s. In other words, data is eating the world and all modern leaders must know how to use data, analytics and advanced data science to power their organisations. So, how do organisations set themselves up for success in a data-driven world, technically and culturally? To answer this question and many more relating to data-driven innovation and intrapreneurship, I recently spoke to Felipe Flores. Felipe is a global thought leader and influencer in the field of data science and artificial intelligence. He is the founder of Data Futurology – a podcast and events company with more than 10,000 weekly listeners, Head of Data & Technology at Honeysuckle Health and co-organiser of Data Science Melbourne. In this first episode of a two-part series of Leaders of Analytics featuring Felipe, we discuss: Felipe’s journey from a young backpacker to a global data science executiveWhat Data Futurology does and why Felipe started itHow to innovate with data scienceThe biggest trends in data science in the next 1-3 yearsWhat the perfect data-driven organisation looks like and much more.Felipe on LinkedIn: https://www.linkedin.com/in/felipe-flores-analytics/ Data Futurology: https://www.datafuturology.com/ Honeysuckle Health: https://www.honeysucklehealth.com.au/

Ever heard of ‘synthetic data’? Synthetic data is data that is artificially created (from statistical models), rather than generated by actual events. It contains all the characteristics of production data, minus the sensitive stuff. By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated, according to Gartner. The reason organisations may use synthetic data over actual data is because you can get it more quickly, easily and cheaply. But there are concerns with this approach, because synthetic data is based on models and algorithms designed by humans and their biases. More data doesn’t necessarily equal better data. Is synthetic data a brilliant tool for improving data quality, reducing data acquisition costs, managing privacy and reducing overfitting? Or does synthetic data put us on a slippery slope of hard-to-interrogate models that are technically replacing fact with fiction? To answer these questions, I recently spoke to Minhaaj Rehman, who is CEO & Chief Data Scientist at Psyda, an AI-enabled academic and industrial research agency. In this episode of Leaders of Analytics, you will learn: What synthetic data is and how it is generatedThe most common uses for synthetic dataThe arguments for and against using synthetic dataWhen synthetic data is most helpful and when it is most riskyHow to implement best practices for mitigating the risks associated with synthetic data, and much more.Episode timestamps: 00:00 Intro 03:00 What Psyda Does 04:23 Academic Work and Modern Education 06:38 Getting into Data Science 11:30 What is Synthetic Data 13:30 Common Applications for Synthetic Data 18:50 Pros & Cons of using Synthetic Data 21:29 Risks of using Synthetic Data 23:48 When should Synthetic Data be Used 29:23 Synthetic Data is Cleaner than Real Data 34:05 Using Synthetic Data for Risk Mitigation 36:05 Resources on Learning More about Synthetic Data 38:05 Human Biases in Decision Making   Connect with Minhaaj: Minhaaj on LinkedIn: https://www.linkedin.com/in/minhaaj/ Minhaaj's website and podcast: https://minhaaj.com/

Is AI good or bad? That would depend on how AI is applied. AI is a revolutionary capability with the power to do a lot of good and plenty of bad, on purpose or by omission. In order for AI to become a social good that improves our lives in broad terms, we must necessarily pick the right use cases and design solutions with a strong focus on ethics and privacy. So, how is AI being used for social good today, and how do we ensure the important topics of ethics and privacy are front and centre for those designing AI solutions? To answer these questions and many more relating to using data for good, I recently spoke to Dr Alex Antic. Alex is the Managing Director of the Dr Alex Antic Group and an award-winning data & analytics leader with a truly impressive CV spanning across quantitative finance, insurance, academia, several federal government departments and consulting as well as advisory and board roles. In this episode of Leaders of Analytics, we cover: The role data, data science and AI can and should play in societyExamples of how AI is being used for social goodHow public entities ensure people’s privacy is maintained, including the use of Privacy Enhancing TechnologiesThe most important data science and AI skills for us to foster as a societyHow Alex is teaching future data leaders to make ethical design choices, and much more.Dr Alex Antic website: https://dralexantic.com/ Dr Alex Antic LinkedIn profile:  https://www.linkedin.com/in/dralexantic/

Data is eating the world and every industry is impacted. In most modern businesses, customer and employee activities create a plethora of data points and information that can be analysed and interpreted to make better decisions for the business and its customers. Unfortunately, this sounds a lot easier than it is. Despite the huge mountains of data being created, many organisations struggle to get their business intelligence to serve them in the best way. This is not due to a shortage of reports and dashboard floating around – in many cases there are too many ways to get an answer to the same question.  So, why are so many organisations lacking good BI and what should they do about it? I recently spoke to Jen Stirrup to get an answer to this question and many more relating to producing and consuming business intelligence effectively. Jen is the CEO & Founder of Data Relish, a global AI, Data Science and Business Intelligence Consultancy. She is a leading authority in AI and Business Intelligence Leadership and has been named one of the Top 50 Global Data Visionaries and Top 50 Women in Technology worldwide. In this episode of Leaders of Analytics, you will learn how to avoid data paralysis and discover how to create business intelligence that gives your organisation new superpowers. Jen's website: https://jenstirrup.com/ Jen's LinkedIn profile: https://www.linkedin.com/in/jenstirrup/ Jen on Twitter: https://twitter.com/jenstirrup

When we talk about analytics and AI-driven organisations, we often think of the likes of Google, Amazon, Facebook, Netflix and Tencent, which have all risen to dominance during the internet era. But what about companies that have been around for much longer, can they achieve the same results with their data? To answer this question, I recently spoke to Tom Davenport who is one of the world’s foremost thought leaders and authors in the areas of business, analytics, data science and AI. He is the President’s Distinguished Professor of Information Technology and Management at Babson College, a Fellow of the MIT Center for Digital Business, and an independent senior advisor to Deloitte Analytics. He has authored more than 20 books and hundreds of articles on topics such as artificial intelligence, analytics, information and knowledge management, process management, and enterprise systems. He is a regular contributor to Harvard Business Review, Forbes Magazine, The Wall Street Journal and many other publications around the world. In this episode, Tom gives us a history lesson of data and analytics and provides an in-depth description of what it takes for traditional companies to ascend through what he calls the “Four Eras of Analytics”.

Why is the Data Scientist role called the sexiest job of the 21st century? I believe it’s partly because the data science profession is constantly evolving to include new data types, new tech and tools, new modelling techniques along with an increasing ability to drive customer and business outcomes with data. The main challenge for data scientists becomes one of bandwidth. Great data scientists are highly intelligent, technically proficient, curious and creative, but even so, the world of data science is evolving too fast for most individuals to keep up with. I recently spoke with Ravit Jain to understand how data professionals stay relevant and connected to the fast-paced world of data.   Ravit is a true servant leader who has built a global online community of data lovers. Through his work as a book publisher, podcast and vlog host, content curator and conference organiser he helps hundreds of thousands of data professionals learn new skills, share knowledge and connect with each other. In this episode of Leaders of Analytics, we discuss what’s hot in data, including: How Ravit became passionate about the world of dataHow to build your career in dataThe most important trends and topics in data today and the futureThe traits that make some data science leaders stand out from the restWhy Ravit’s first advice for aspiring data professionals is to start networking with others in the industry, and much more.

Data science and machine learning are integral parts of most large-scale product manufacturing processes and are used to understand customer needs, detect quality issues, automate repetitive tasks and optimise supply chains. It’s an invisible glue that helps us produce more things for less, and in a timely fashion. To learn more about this fascinating topic, I recently spoke to Ranga Ramesh who is Senior Director, Quality Innovation and Transformation at Georgia-Pacific. Georgia-Pacific is one of the world’s largest manufacturers of consumer paper products and uses AI technologies throughout their manufacturing process. In this episode of Leaders of Analytics, we explore how computer vision and machine learning can be used to classify tissue paper softness and instantly detect quality issues that could otherwise render large volumes of product useless. Ranga’s work is featured as a case study in our recently published book, Demystifying AI for the Enterprise.

Data science and machine learning are continuing to evolve as core capabilities across many industries. But high-quality data science output is only half the story. As the data science profession matures from “back office support” to leading from the front, there is an increasing need for more integrated systems that plug into business operations. To get the most out of these capabilities, organisations must move beyond just building robust models, and establish operational processes that can produce, implement and maintain machine learning systems at scale. Enter MLOps. To understand the fundamentals and best practices of MLOps, I recently spoke to Shalini Kurapati who is CEO of Clearbox.ai. Clearbox AI is the data-centric MLOps company that enables trustworthy and human-centred AI. Their AI Control Room automatically produces synthetic data and insights to solve the issues related to data quality, data access and sharing, and privacy aspects that block AI adoption in companies. In this episode of Leaders of Analytics, we cover: What MLOps is and why we need it to succeed with advanced data science solutionsHow to get beyond the proof-of-concept-to-production gap and get models into operationThe importance of data-centric AI in building MLOps best practicesThe most common AI pitfalls to avoidHow Human Centred Design principles can be used to build AI for good, and much more.Check out Clearbox here: https://clearbox.ai/ Connect with Shalini here: https://www.linkedin.com/in/shalini-kurapati-phd-she-her-06516324/

My guest on this episode of Leaders of Analytics is Kate Strachnyi. Kate is a well-known figure in the global data community. She is a master educator and prolific content creator who has built an online community of almost 200,000 followers. Through the DATAcated brand she runs online training, seminars, conferences, expos and podcasts while connecting data professionals across the world. She is also the author of four books in the data science genre and a marathon runner. I recently caught up with Kate to learn more about what it takes to keep up with the fast-paced and ever-evolving world of data and analytics. In this episode we discuss: The most important data science skills in the next 5-10 yearsThe most underrated skill in data scienceHow to make your day productive and enjoyableCareer advice for someone starting out in data science todayMinting NFTs for the global data community, and much moreYou can find more from Kate here: DATAcated: https://datacated.com/ LinkedIn: https://www.linkedin.com/in/kate-strachnyi-data/

There are so many ways to use AI technology in retail to improve customer experience, optimise supply chains and reduce waste. Yet it seems to me that most innovations in the retail industry over the last 30 years have focused on automating labour-intensive tasks. In my personal opinion, the retail customer experience has not improved markedly in my lifetime, and in some cases, it has gotten worse. Anyone who’s ever interacted with a self-checkout machine will know what I mean. So, what is next for the retail industry and what can technology and data science do to improve efficiency and customer experience across the many disparate parts of retailing? To answer these questions, I recently spoke to Shantha Mohan who is a true expert in the field. Shantha is currently an Executive in Residence at the Integrated Innovation Institute at Carnegie Mellon University, where she co-delivers courses, contributes to curriculum design, and mentors students in their projects and practicums. Shantha is also a co-founder and long-time executive of Retail Solutions Inc (RSi) where she ran the company’s worldwide product Development team that built the products & services which made the company a leader in retail analytics solutions used by consumer packaged goods companies and retailers across the globe. She holds a PhD in Operations Management and a Bachelor of Engineering in Electronics and Communication Engineering. In this episode of Leaders of Analytics, we discuss: The applications of AI in retail with the most potential, for online and in-store shopping respectivelyThe differences between retail in developed and developing countries and how AI must be customised for different markets across the globe.The typical consequences of items being out of stock and how can AI and other relevant technologies help combat out-of-stock problems.Whether AI in retail will increase or diminish the ability for small retailers to compete, and much more.