“Out of stock”.Three words with a great deal of significance for retailers and their customers.It is estimated that retail products are out of stock 8% of the time in physical stores, and more than 14% of the time in e-commerce stores, leading to frustration for retailers and customers alike.Retailers miss out on important revenue from the forgone sales.Customers leave unfulfilled and are less likely to return to the same retailer or recommend it to others in their network.Supply chains feel the ripples of the gaps between demand and supply.This is a trillion-dollar problem globally.The solution to this problem is not just about demand forecasting, but also knowing what you have in stock, which is a huge challenge in itself.To understand how to solve this challenge, I recently spoke to Min Chen who is the co-founder and CEO of Wisy Inc. The company’s technology is focused on reducing retail stockouts and waste with artificial intelligence and data analytics.Min is a seasoned entrepreneur and an all-round interesting person. Having migrated from China to Panama at age 4, the now lives in Silicon Valley after moving Wisy from Panama to the US in 2020.In this episode of Leaders of Analytics, you will learn:How AI can help solve a global, trillion-dollar supply chain problemHow to develop a product-market fit for AI solutionsHow to bootstrap a start-up in a difficult environmentWhy Wisy decided to move the company from Panama to Silicon Valley

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/

This might just be the most interesting and thought-provoking episode of Leaders of Analytics yet. Why?Without even recognising it, you make hundreds of ethical decisions every day. Some of these decisions you probably don’t even recognise as being grounded in ethical principles because they are so ingrained in your subconscious.AI on the other hand, doesn’t make decisions based on ethics, unless ethical behaviour is somehow picked up in the training data. Therefore, we must make AI ethical by design, but that is not easy.Many of the ethical dilemmas arising from AI are difficult to solve, because the problems are so novel in a human context. Yet we all need to get used dealing with these ethical dilemmas at scale as we implement AI in our business operations.To understand the unwieldy world of ethical AI, I recently spoke to James Brusseau who is a philosopher at Pace University, specialising in AI ethics.His academic research explores the human experience of artificial intelligence in the areas of privacy, freedom, authenticity and personal identity and he works with organisations around the world to develop ethical AI applications.In this episode of Leaders of Analytics, we discuss:What AI ethics is and why it’s importantThe most common dilemmas or challenges we face when it comes to AI ethicsWhether AI driven curation of information is a good thing or a bad thingHow we can develop a framework for dealing with ethical dilemmas at scaleHow governments might regulate AI or introduce other incentives to achieve ethical AI by designHow leaders can get prepared for managing and governing the ethical implications of using AI in their operations, and much more.

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.

If you dream of using analytics to optimise your customer interactions and squeeze additional value out of your existing operations, then is episode is for you!Today, most large services businesses have established data science functions that churn out countless reports, dashboards, customer insights packs, machine learning models, forecasts and predictions.With all this information to hand, you would hope that front-line operations are making data-driven decisions across the board. But alas, many of these same businesses struggle to turn their analytics into more than glossy PowerPoint packs that describe what could be done.Often, this is because the technical implementation of data science solutions run into resource constraints or remain unsupported by IT departments.So, how can we successfully make use of our analytical output in our front-line operations without spending eons creating overly complex systems that never quite deliver?To answer this question, I recently spoke to Jason Tan who is an expert in operationalising data science solutions that deliver positive customer outcomes and real financial results.Jason Is the managing director of consulting group Data Driven Analytics and an expert in optimising customer experience, pricing and long-term customer value.        In this episode of Leaders of Analytics, we discuss:How to use analytics to optimise your customer interactionsHow to identify the most valuable data science use cases in your organisationHow Jason has created successful data science solutions around legacy IT platformsWhether you should buy off-the-shelf pricing software or build your own solution

AI and machine learning are seen by many as capabilities with enormous potential for unlocking digital personalisation and customer empathy at scale.Organisations that get this right are disrupting industries and leaving old-school competitors broke. Just think of what global businesses like Netflix, Amazon and Facebook have been able to achieve with data-driven personalisation.Yet, for many organisations, the promise of AI seems elusive or at least very hard to achieve. Many businesses are not realising the full potential of their stores of data, simply because they don’t know how.To help us understand the potential of AI and ML for Customer Experience Management, I recently spoke to my friend and co-author of Demystifying AI for the Enterprise, Dr Kirk Borne.Kirk is a truly unique individual who combines his incredible intelligence with a real passion for his chosen vocation. Having graduated with a PhD in Astrophysics, he spent 20 years working at NASA, before moving into the academic and corporate worlds.He spent 12 years as Professor of Astrophysics and Computational Science, where he created the world’s first data science undergraduate degree. He since moved into data science consulting where he has been an executive for the past 6 years.Kirk has a social media following of well over 300,000 which is a testament to the huge amount of value he creates through content creation and knowledge sharing.In this episode of Leaders of Analytics, we discuss:What data science, AI and machine learning can bring to digital and analogue customer experiencesThe most valuable applications of AI for customer experience managementHow AI can be used to amplify the abilities of front-line staffLeading applications of AI-driven customer experienceThe technical and organisational challenges that must be overcome to move up the analytics maturity curveThe importance of ModelOps in operationalising data science

Digitally connected humans like you and me are surrounded by a plethora of AI solutions that make our lives easier and more efficient. Just think about the algorithms driving Netflix and Youtube’s video recommendations or the facial recognition feature on your phone that saves you a few seconds every time you unlock it.But for every useful AI solution, there are probably hundreds of solutions that don’t meet the functional, economic or ethical standards of their end users.So, what’s the trick to building useful and impactful AI solutions that are also financially viable for those who create them?Someone who can answer this question is Corey Quinn, who is the Chief Cloud Economist at The Duckbill Group and the founder of two podcasts called “Screaming in the Cloud” and “AWS Morning Brief”.Corey combines an excellent sense of humour with a deep understanding of the cloud and everything that surrounds it, so he is definitely the right person to go to for an unfiltered view of the hype that surrounds a lot of AI solutions.In this episode of Leaders of Analytics, we talk about:Whether AI is all it’s made up to be or just a complex solution to our problemsWho’s benefiting from the AI hypeThe role of cloud computing in AI and machine learning deliveryHow to use cloud computing effectively when deploying AI solutionsHow to create an impactful career by solving real business problemsCorey’s top 3 recommendations for AI success in the cloud

Digital transformation 2.0 is upon us! We have spent the last two decades digitising many products, services and processes to create digital experiences that are consistent, reliable and always on. That’s digital transformation 1.0 stuff.The next decade will be all about creating data-driven personalisation at scale. Rather than treating everyone the same in our digital environment, we will increasingly be using customer data to tailor the customer experience to individual customer needs.In this episode of Leaders of Analytics, we hear from Prashant Natarajan, Vice President of Strategy & Products at H2O.ai.Prashant has spent more than 15 years helping organisations to successful digital transformations through his leadership roles in the sphere of technology and AI. He has made it his career to demystify AI and digital transformation for organisations and their staff across multiple industries and continents.In this episode of Leaders of Analytics, we discuss:what’s required to do digital transformation 2.0 successfullyhow to create data-first organisationshow to use AI to take the robot out of humansthe future of automated machine learninghow organisations can ensure that their data science investments deliver actual business outcomesour upcoming book, Demystifying AI for the Enterprise, which Prashant and I have co-authored alongside 5 other domain experts.

Data, networks and AI are eating the world and industries such as banking, insurance, utilities and telecommunications are changing rapidly as a result.As an online product comparison portal and trusted third party to millions of consumers, Finder.com is well placed to be a huge winner from this trend. The company sits in the middle of many data-heavy industries that are about being disrupted by the data revolution.The guest on this episode of Leaders of Analytics is Finder.com’s co-founder and CEO Fred Schebesta. Fred is one of Australia's coolest and most successful entrepreneurs, now worth over half a billion dollars – all without funding.He’s passionate about disruptive innovation and is a leader in the startup community where he shares his successes and knowledge as a mentor, international speaker, media commentator and author.In this episode we talk about:How Finder has grown from a two-man band to an international company.How Finder is planning to use their recently received accreditation under CDR/Open Banking and what it means for Australian consumers and the financial services industry.How Finder uses AI and machine learning to understand people’s finances and help them to better financial outcomes.Why the company is betting big on cryptocurrencies and decentralised finance, including paying employees in Bitcoin.How crypto will form part of the financial system of the future.Fred’s new book, “Go Live! 10 Principles to Launch a Global Empire”.

Jamie is a data advocate with a strong belief in the transformative potential of data.She is the founder and CEO of Open Data Australia and the regional director for FDATA Australasia and an advisor on digital identity to the United Nations Capital Development Fund.Jamie is the go-to person for knowledge and insights on the topics of data privacy, governance, strategy, policy and regulation.She has a vision for how data can be used to improve the lives and financial outcomes of everyday citizensIn this episode of Leaders of Analytics we discuss the huge potential for data innovation stemming from the Consumer Data Right and Open Banking, the hurdles that must be overcome by participating as well as who will be the winners and losers from the data sharing revolution.In this episode you will find:An overview of the Consumer Data Right and what it means to consumers and participating organisations?How CDR differs from GDPRHow far participating organisations are in implementing the various components of Open Banking what should we expect to see in this space in the next 12-24 monthsThe most obvious use cases for CDR, and Open Banking in particularThe most important use cases that CDR/Open Banking participants should be focusing onInternational examples of successful Open Banking based products and servicesThe hurdles currently limiting the use of the data sharing environment that CDR and Open Banking facilitatesHow to generate consumer trust and excitement around CDR and Open BankingWhat impacts CDR will have across the wider economy in the futureWho will be the future winners and losers from CDR, Open Banking and a broader Open Data regime