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 FrancescoFrancesco on Linkedin: https://www.linkedin.com/in/francescopuppini/Francesco's book on the USS: https://www.goodreads.com/author/show/20792240.Francesco_Puppini

Modern analytics teams are central business functions directly and indirectly responsible for increasing revenue, reducing costs, optimising processes and improving customer and employee satisfaction.But there are many obstacles along the way. Data needs collecting, projects need careful design and execution and stakeholders need convincing. Analytics teams are required to cover a wide range of technical knowledge, business acumen and leadership skills to be impactful.What is the recipe for creating analytics teams that deliver impactful solutions and drive real business value? What are the technical, interpersonal and leadership skills required to lead the business through change and adoption of analytics?To answer these questions, and many more relating to the art and science of building excellent analytics functions, I recently spoke to John K. Thompson.John is an international data and technology executive with over 30 years of experience in business intelligence and advanced analytics and author of the best-seller ‘Building Analytics Teams’.In this episode of Leaders of Analytics, we discuss:The hallmarks of an excellent analytics teamWhat a perfect analytics team looks likeThe skills, personality traits and behaviours you need in an analytics teamThe common traits of highly effective analytics leadersHow analytics leaders set themselves up to meet the expectations of business stakeholdersHow to select and prioritise the right projects to work onWhere organisations typically fail when designing analytics teamsThe lowdown on John’s upcoming book, and much more.John on LinkedIn: https://www.linkedin.com/in/johnkthompson/John's book 'Building Analytics Teams': https://www.packtpub.com/product/building-analytics-teams/9781800203167

There is so much to learn!If you’re anything like me, you’re overwhelmed by the number of books, articles, podcasts, online and offline courses, webinars and other training opportunities out there.Today, we’re not short of learning materials, but often lack the time and capacity to learn new things.But what if there’s a better way to learn?Enter the concept of “Ultralearning”, coined by best-selling author Scott Young.A few years ago, I read Scott’s book Ultralearning and it changed my life. Not only did Scott’s approach to learning increase my learning rate significantly, it also made the process a lot more enjoyable overall! Scott is an impressive Ultralearner who has used his advanced learning strategies to complete a 4-year computer science degree in 12 months, learn languages such as Spanish, Chinese, Korean and Macedonian and become a decent portrait artist.And then he’s written a book about it.In this episode of Leaders of Analytics, you will learn:How Scott has used his learning principles to master very complex and diverse skills in a very short timeHow we learn and retain informationHow we can structure our learning to faster absorption and better retentionHow Scott designs a learning strategy from scratchWhether Malcolm Gladwell’s “10,000 hour rule” is true or BSStrategies for learning hard and soft skills, and much more.Scott's website (full of excellent learning resources): https://www.scotthyoung.com/Scott's podcast: https://www.scotthyoung.com/blog/podcast/Scott on Twitter: https://twitter.com/scotthyoung/Scott on LinkedIn: https://www.linkedin.com/in/scott-h-young-867ab21/

An estimated 80 to 90 percent of the data in an enterprise is text. Sadly, this rich information is mostly neglected for analytical purposes.Textual data is typically full of information, but also very complex to interpret computationally and statistically. Why?Because textual data is both content and context. The same words and sentences can have very different meanings depending on the context.Textual data is truly a goldmine, but how can we mine it without being digital superpowers like Google, Microsoft or Facebook?To answer this question and many more relating to interpretation of textual data, I recently spoke to Bill Inmon.Bill is the Founder, Chairman and CEO of Forest Rim Technology and author of more than 60 books on data warehousing. He is often described as the Father of Data Warehousing due to his pioneering efforts in making data and data technologies available to organisations across all industries and sizes.In this episode of Leaders of Analytics, we discuss:How Bill became the Father of Data WarehousingThe history of data warehousing and the most exciting developments in this space todayThe typical challenges holding us back from extracting value from textual dataThe concept of the “Textual ETL” and it’s benefits over other text data storage and analytics approachesWhy NLP is not the best approach for textual data analyticsThe biggest opportunities for textual analytics today and in the future, and much more.Connect with Bill:Forest Rim Technnology: https://www.forestrimtech.com/Bill on LinkedIn: https://www.linkedin.com/in/billinmon/

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/

When we talk about data and AI ethics, we typically view this through a privacy lens.That is, someone’s personal data has either been compromised and ended up in the wrong hands, or personal data is used to manipulate or create adverse outcomes for individuals or minority groups.These factors are still fundamental to AI ethics, but there is now also a big focus on the broader social impact of AI, including human rights, data privacy and using AI for good.Enter the concept of data pollution.The data pollution paradigm describes how the use and intentional or unintentional sharing of personal data can create social harm – not just private harm affecting only the individuals included in the dataset.To understand the concept of data pollution and its impact on individual privacy and society as a whole, I recently spoke to Gianclaudio Malgieri.Gianclaudio is Associate Professor of Law and Technology at the Augmented Law Institute of EDHEC Business School (Lille, France), Co-Director of the Brussels Privacy Hub, lecturer in IP and Data Protection and an expert in privacy, data protection, intellectual property, law and technology, EU law and human rights.In this episode of Leaders of Analytics, we discuss:The evolution of data and AI ethics over the last 20 yearsWhy data protection is so important to the future of our society as we know itWhat data pollution is and why we should care about itWhat we can do to create data sustainabilityWhat business leaders, legislators and legal professionals can do to deal with AI sustainability issues, and much more.Gianclaudio's website: https://www.gianclaudiomalgieri.eu/Gianclaudio on LinkedIn: https://www.linkedin.com/in/gianclaudio-malgieri-410718a1/Brussels Privacy Hub: https://brusselsprivacyhub.eu/

Is the typical hiring and job search process broken?It is definitely full of bias.First, we get interested candidates to submit their resumes.Then someone (typically not the hiring manager) will pick out the resumes that look most interesting to them.Resumes that survive are typically carefully curated for someone to be able to form a positive opinion in just a few seconds.Then the hiring manager will pick their favourites out of that smaller pile. At this point, the lion’s share of candidates has been excluded purely based on resumes.Then comes the first interview. According to a study in the Journal of Occupational and Organisational Psychology, 60% of interviewers make their decision in the first 15 minutes.What’s more, according to Hubspot, 85% of jobs are filled through networking. We prefer to hire someone we already know, because we think we have an idea of their ability.We are genetically designed to make quick decisions based on limited data points, which is at odds with very complex decisions such as hiring the right candidate. We try to deal with this through resumes, but these documents are also heavily biased.How do we limit our own biases and measure all candidates objectively?How do we identify the rising stars and unique talents who don’t yet have a long resume full of experience?I recently spoke to Tim Freestone to get an answer to these questions and many more relating to hiring the right data and analytics candidates.Tim is the founder of Alooba, the world’s first data and analytics assessment platform. Alooba’s tools help organisations around the world objectively assess the skills and capabilities of new candidates and existing team members alike.In this episode of Leaders of Analytics, we discuss:The biggest challenges for hiring managers in the data and analytics industry and how we can solve theseThe typical mistakes hiring managers and candidates make when they recruit and apply for roles respectivelyThe biggest opportunities to improve the hiring process for data and analytics professionalsWhat skillsets make data & analytics candidates stand out in today’s job marketMust-have skills that hiring managers should look for in their candidates, and much more.Tim Freestone on LinkedIn: https://www.linkedin.com/in/tim-freestone-alooba/Alooba's website: https://www.alooba.com/ 

Blockchain technology, cryptocurrencies and decentralised finance are described by some as massively disruptive technologies that will turn our existing financial system on its head.For the traditional financial services industry, these technologies have the potential to create huge efficiency gains and democratise more complex financial services for individual users.On the other hand, DeFi also reduces – and potentially removes – the need for trusted intermediaries, which makes the model unsettling to some operators in the current financial system.DeFi also opens the opportunity for global financial inclusion of enterprises and private individuals in developing markets – a very large group whose needs are typically unmet by traditional finance.With all this huge potential about to be released, we better learn why these technologies are so revolutionary and what will they do for us now and in the future.To answer these questions and many more relating to DeFi, I recently spoke to Daniel Liebau. Dan is the Chief Investment Officer, Blockchain Strategy at Modular Asset Management and the Founding Chairman of Lightbulb Capital, a DeFi investment and consulting firm.In this episode of Leaders of Analytics, Dan and I discuss:Why is DeFi so revolutionary and the opportunities and risks that lie within this space for individual users, corporations and nation statesThe difference between Payment, Utility and Security tokens and how these are likely to be used in our future financial systemThe utility of NFTs and their future as an asset categoryHow blockchains, cryptocurrencies and DeFi will be part of our lives in 5, 10 and 20 years respectivelyWhat Dan is teaching his FinTech, crypto and DeFi students, and much more. Daniel Liebau on LinkedIn: https://www.linkedin.com/in/liebauda/Lightbulb Capital: https://www.lightbulbcap.com/

Data is everywhere, but do we know what it means?A common problem for many enterprises wanting to adopt cutting edge, data-driven solutions is that they have a ton of legacy applications interlinking with more modern tech stacks.If the organisation is large or complex enough, it typically becomes unrealistic for any one individual to understand how it all hangs together.All of these applications generate data points with their own definitions, meaning and naming conventions.How do organisations like these set themselves up for success in a data-driven world, technically and culturally?How can we create a consistent and holistic view of our data that can be used equally by technologists, analysts and business users?To answer these questions, I recently spoke to David P. Mariani who is the founder and Chief Technology Officer of AtScale. Dave is an incredibly talented technology executive and entrepreneur with more than $800 million worth of company exits on his resume.In this episode of Leaders of Analytics, we discuss:How to create successful technology companies from scratchWhat David learned during his time at Yahoo! that made him start AtScaleWhat a semantic layer is and what it does for your organisationWhat David’s utopian technology stack would look like and whyDavid’s vision for how data-driven organisations will function in the futureHow a universal semantic layer fits into this future, and much more.David's LinkedIn: https://www.linkedin.com/in/davidpmariani/AtScale's company website (lots of great content on here): https://www.atscale.com/