Leaders of Analytics

In this episode, I dive into the world of AI leadership with Andreas Welsh, a renowned AI expert and author of 'The AI Leadership Handbook'. We explore Andreas's impressive career at SAP, his new venture as an AI advisor and expert, his impactful journey on LinkedIn, and his insights into successful AI implementation. Topics we cover: Discover Andreas's background and his remarkable 23-year career at SAP. He shares pivotal moments and lessons learned from working at one of the world's largest tech companies. Learn what motivated Andreas to start sharing his expertise on LinkedIn in 2021, and the significant impact it has had on his professional life. Uncover the inspiration behind Andreas's book, The AI Leadership Handbook, and his mission to guide organisations in harnessing AI effectively. Andreas discusses the critical elements that must be in place for AI projects to thrive and avoid the common pitfalls that lead to failure. Understand the need for the emerging Chief AI Officer role, how it differs from a Chief Data & Analytics Officer, and the importance of giving it a strong mandate within organisations. Explore the concept of multiplier communities and their role in amplifying AI capabilities across organisations. Andreas shares his vision for AI over the next 5-10 years, including opportunities, potential risks, and disruptions. Andreas leaves listeners with a powerful lesson from 'The AI Leadership Handbook' that every leader should consider when integrating AI into their strategy. This episode is packed with valuable insights for anyone interested in AI leadership and innovation. Whether you're an executive, a tech enthusiast, or someone curious about the future of AI, Andreas Welsh offers guidance and inspiration to navigate this transformative field. Connect with Andreas Welsh on LinkedIn: https://www.linkedin.com/in/andreasmwelsch/ Leaders of Analytics Newsletter: https://www.leadersofanalytics.com/newsletter Subscribe to Leaders of Analytics via your favourite podcast app: Apple Podcasts Google Podcasts Spotify

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.

Brian Ferris is a seasoned expert with over two decades of experience in technology and advanced analytics. Brian has an impressive track record, having worked in IT consulting for 9 years and client-side roles for 13 years with major organisations like the European Central Bank, BAT, Heineken, Nike and Loyalty New Zealand. In this episode, we dive into Brian's journey from supply chain operations to becoming Chief Data, Analytics and Technology Officer at Loyalty New Zealand. We explore the pivotal moments that shaped his approach to analytics and the leadership qualities essential for fostering a culture that embraces advanced analytics. We also discuss his new book, "Transition to Advanced Analytics: Get a Return on Your Analytics Investment," co-authored with Jason Tan. Brian shares what inspired him to write the book, provides a synopsis, and highlights key takeaways for organisations looking to transition to advanced analytics. Topics covered: Brian's analytics leadership journey: Discover the key factors that contributed to Brian's successful career progression and the pivotal moments that shaped his approach to analytics. Leadership in analytics: Learn about the essential leadership qualities needed to drive analytics initiatives and foster a culture that embraces advanced analytics. Evolution of analytics roles: Understand how the role of data scientists and analysts has evolved and which skills are now more critical than ever. Underrated tips and tricks: Brian shares practical tips and tricks that all data and analytics teams should use to increase their impact. About the book: Hear what inspired Brian to write "Transition to Advanced Analytics" and get a detailed synopsis of the book, including who it's for and why it's essential reading. Common pitfalls and key takeaways: Find out the most common pitfalls organisations face when transitioning to advanced analytics and the key lesson Brian hopes readers will take away from the book. Brian on LinkedIn: https://www.linkedin.com/in/brian-ferris-a053532/ Brian's book, "Transition to Advanced Analytics: Get a Return on Your Analytics Investment".

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/

My guest in this episode is Coert du Plessis, an impressive data and analytics executive, entrepreneur and general lover of life. Coert shares his wealth of knowledge and experience gained through a career and life full of interesting twists and turns. In this wide-ranging conversation, we talk about: Coert's journey from South African farmland to Australian board rooms How Coert became the CEO of MaxMine Why our ability to tackle climate change depends on the mining industry How to build and sell successful data products Coert's approach to building a fulfilling and rewarding career in data and analytics The importance of taking risks and running life experiments, and much more. Coert on LinkedIn: https://www.linkedin.com/in/coertdup/ My new book, 'Data-Centric Machine Learning with Python': https://www.packtpub.com/product/data-centric-machine-learning-with-python/9781804618127

My guest on this episode is Nikolaj van Omme, CEO and co-founder of Funartech. Funartech is a Canadian company specializing in AI-driven solutions to complex industrial optimisation problems. The company's secret sauce is combining the two disciplines of Operations Research and Machine Learning. Operations Research is about making the best decisions and solving problems in a structured way, using maths to optimize outcomes. Machine learning on the other hand, is really good at spotting patterns and making predictions from lots and lots of data. The cool part happens when we bring these two together. ML is the detective finding clues in a sea of information, and OR is the strategist, using those clues to make the best moves. By working together, they can tackle challenges neither could face on their own. Find Nikolaj on LinkedIn or via Funartech's website. Previous episode discussed in this interview: Using Data to Build a Better World with Dr Alex Antic

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.

In this digital age, data is the lifeblood of business. Just as computer literacy became a non-negotiable skill in the 21st century, data literacy is now an essential competency in our increasingly data-driven world. Yet, despite its critical importance, it's an area where many individuals and businesses stumble. Understanding, interpreting, and effectively using data can be challenging, even daunting. The lack of data literacy skills can lead to misinterpretation, misuse, and missed opportunities for businesses and individuals. Many struggle to find a structured approach to elevate their data literacy skills, often feeling lost in the vast sea of numbers and metrics. My guest in this episode, Angelika Klidas, wants to change that. Angelika is a data literacy expert and author of the book Data Literacy in Practice. In this conversation, she shares her invaluable insights and practical tips on mastering data literacy. Whether you're a novice or a seasoned data professional, Angelika's expertise will empower you to upskill yourself, your team, and your organisation, one data project at a time. Angelika on LinkedIn: https://www.linkedin.com/in/angelikaklidas/ More about Data Escape Rooms here.

Many large organisations have the data to pull off sophisticated marketing strategies, but only if they avoid the common pitfalls that limit the potential. In this episode I interview Tejas Manohar on the huge – and typically unexploited – potential for data-driven marketing and personalisation. Tejas is co-founder and co-CEO of Hightouch. Hightouch is a reverse ETL platform that helps organisations synch their data warehouses with business facing tools and technology. Their products are used by big name corporations like Warner Music, Chime, Spotify, NBA, and PetSmart. In this wide-ranging conversation Tejas and I discuss: What a reverse ETL platform is and why we need it Why Tejas is bullish on turning data warehouses into marketing engines The key steps marketers should take to implement personalization effectively using existing company data and platforms The pitfalls and common mistakes businesses make in data-driven personalisation and how to avoid these, and much more. Tejas on LinkedIn: https://www.linkedin.com/in/tejasmanohar/ Tejas on Twitter (or is it X?): https://twitter.com/tejasmanohar

Every day, like invisible breadcrumbs, we leave trails of personal data scattered across the digital landscape. Each click, every search, every purchase - they all tell a story about us. But do we know where these breadcrumbs lead? Who's picking them up? And most importantly, what are they doing with them? In an era where data is documenting our lives across a host of platforms, understanding these trails and their implications is no longer a luxury but rather, a necessity. It's about our privacy, our rights, and our well-being in an increasingly interconnected world. In this episode of Leaders of Analytics John Thompson and I dive into his newly released book that should be on everyone's reading list - "Data for All". During our discussion, we'll delve into the eye-opening insights Thompson shares in his book, such as understanding the scope and consequences of companies manipulating and exploiting your data. We also explore the step-by-step guide he provides on how to navigate this changing landscape.

We're definitely in AI hype mode at the moment largely driven by the evolution in generative AI. However, it seems like this progress is not necessarily driving lots of data-related innovation inside organisations that are not AI-first tech companies. A recent survey published by Randy Bean's company, NewVantage Partners, confirms this. Here are the main findings compared to when the survey was last run 4 years ago: 59.5% of executives say their companies use data for business innovation – the same as four years ago. A drop from 47.6% to 40.8% of executives say their companies compete using data and analytics. Fewer executives (39.5% down from 46.9%) say their companies manage data as a business asset. Only 23.9% of executives now say their companies are data-driven, compared to 31% before. Just 20.6% of executives report having a data culture in their companies, down 27% from 28.3% in 2019. These numbers spell regression, not progress. Why is it so hard to become a truly data-driven organisation? In this episode, Randy and I explore the challenges facing Chief Data & Analytics Officers and their teams, including: How organizations can create an environment that encourages innovation in data-driven initiatives Examples of organisations doing data well, and why How to set clear expectations around the responsibilities of CDAOs The most important qualities for someone in the CDAO role, and much more. Randy on LinkedIn: https://www.linkedin.com/in/randybeannvp/ Randy's website and book, 'Fail Fast, Learn Faster': https://www.randybeandata.com/book

In a world where data is the new oil, being able to understand, analyse and interpret it is a vital skill. As the saying goes, "knowledge is power," and in this case, data literacy is the key to unlocking that power. I argue that data literacy is as important to individual and organisational success as computer literacy, but unfortunately that is not a consensus view. For many organisations and their leaders, low data literacy is hampering their ability to make effective, data-driven decisions. What is the key to creating a data literate organisation and unlocking the true potential of your data? Who better to guide us through the many aspects of this question than data literacy expert Kevin Hanegan. Kevin is the Chief Learning Officer at Qlik and a renowned author of the books “Data Literacy in Practice” and “Turning Data into Wisdom”. In this episode of Leaders of Analytics, Kevin will be sharing invaluable insights and expertise from his books and his work at Qlik. Listen in as we explore: How data literacy can transform businesses, boost individual careers, and help us make better-informed decisions Practical tips and strategies for developing data literacy skills Common misconceptions or challenges that hold people back from becoming data literate, and how to overcome these How to foster a data-driven culture within organisations, and much more. Kevin's website: https://www.kevinhanegan.com/ Connect with Kevin on LinkedIn. Learn more about the Data Literacy Project.

In a world where data is the new oil, being able to understand, analyse and interpret it is a vital skill. As the saying goes, "knowledge is power," and in this case, data literacy is the key to unlocking that power. I argue that data literacy is as important to individual and organisational success as computer literacy, but unfortunately that is not a consensus view. For many organisations and their leaders, low data literacy is hampering their ability to make effective, data-driven decisions. What is the key to creating a data literate organisation and unlocking the true potential of your data? Who better to guide us through the many aspects of this question than data literacy expert Kevin Hanegan. Kevin is the Chief Learning Officer at Qlik and a renowned author of the books “Data Literacy in Practice” and “Turning Data into Wisdom”. In this episode of Leaders of Analytics, Kevin will be sharing invaluable insights and expertise from his books and his work at Qlik. Listen in as we explore: How data literacy can transform businesses, boost individual careers, and help us make better-informed decisions Practical tips and strategies for developing data literacy skills Common misconceptions or challenges that hold people back from becoming data literate, and how to overcome these How to foster a data-driven culture within organisations, and much more. Kevin's website: https://www.kevinhanegan.com/ Connect with Kevin on LinkedIn. Learn more about the Data Literacy Project.

This episode of Leaders of Analytics features Dhiraj Rajaram, the Founder of global decision sciences company Mu Sigma. Mu Sigma serves more than 140 of the Fortune 500 and the company's mission is to simplify complex problems through the science of decisions. Dhiraj shares his views on problem-solving in business, and how Mu Sigma's three core beliefs have been instrumental in the company's success. At Mu Sigma, they believe in "Learning over Knowing", "Extreme Experimentation", and "The New IP". Their data-driven decision-making approach has helped solve some of the toughest business challenges and has set them apart from the competition. As an entrepreneur or business leader, you'll gain valuable insights into using data to solve complex issues, as well as an insider's perspective on Dhiraj's entrepreneurial journey. In this episode we discuss: Dhiraj's entrepreneurial journey from a one-man band to leading thousands of employees The critical moments that led Dhiraj to become a founder and the key elements of entrepreneurial success Mu Sigma's unique recruitment and training strategy What you can learn from Mu Sigma's three core beliefs How to make better decisions for your organisation, and much more. Mu Sigma's website Connect with Dhiraj on Linkedin.

This episode of Leaders of Analytics features Dhiraj Rajaram, the Founder of global decision sciences company Mu Sigma. Mu Sigma serves more than 140 of the Fortune 500 and the company's mission is to simplify complex problems through the science of decisions. Dhiraj shares his views on problem-solving in business, and how Mu Sigma's three core beliefs have been instrumental in the company's success. At Mu Sigma, they believe in "Learning over Knowing", "Extreme Experimentation", and "The New IP". Their data-driven decision-making approach has helped solve some of the toughest business challenges and has set them apart from the competition. As an entrepreneur or business leader, you'll gain valuable insights into using data to solve complex issues, as well as an insider's perspective on Dhiraj's entrepreneurial journey. In this episode we discuss: Dhiraj's entrepreneurial journey from a one-man band to leading thousands of employees The critical moments that led Dhiraj to become a founder and the key elements of entrepreneurial success Mu Sigma's unique recruitment and training strategy What you can learn from Mu Sigma's three core beliefs How to make better decisions for your organisation, and much more. Mu Sigma's website Connect with Dhiraj on Linkedin.

In this episode of Leaders of Analytics, I am joined by Ada Guan who is one of the most innovative minds in the field of credit decisioning. Ada is CEO and co-founder of Rich Data Co, a company that helps lenders make informed and accurate credit decisions by leveraging AI and machine learning. Listen in as Ada sheds light on the role that AI and machine learning can play in transforming the lending industry and what the future may hold for credit decisioning. In this episode, we'll discuss: Ada's entrepreneurial journey The typical pain points lenders face and how RDC's unique AI solution solves these problems What makes RDC's solution unique and why banks should buy rather than build themselves How to find product-market fit or an AI product The additional benefits an AI solution brings over traditional credit scorecards or rules-based decisioning engines, and much more. Learn more about Rich Data Co here: https://www.richdataco.com/ Connect with Ada Guan on LinkedIn.

In this episode of Leaders of Analytics, I am joined by Ada Guan who is one of the most innovative minds in the field of credit decisioning. Ada is CEO and co-founder of Rich Data Co, a company that helps lenders make informed and accurate credit decisions by leveraging AI and machine learning. Listen in as Ada sheds light on the role that AI and machine learning can play in transforming the lending industry and what the future may hold for credit decisioning. In this episode, we'll discuss: Ada's entrepreneurial journey The typical pain points lenders face and how RDC's unique AI solution solves these problems What makes RDC's solution unique and why banks should buy rather than build themselves How to find product-market fit or an AI product The additional benefits an AI solution brings over traditional credit scorecards or rules-based decisioning engines, and much more. Learn more about Rich Data Co here: https://www.richdataco.com/ Connect with Ada Guan on LinkedIn.

Data is revolutionising our world, yet many companies fail to harness its value. What needs to be done for CEOs to see the value of having analytics as part of the executive inner circle? Unfortunately, many analytics teams struggle to move past the common challenges of fostering analytics literacy, getting executive buy-in for more investment in data and analytics and showcasing the value delivered into the business. How can analytics leaders make their discipline an indispensable superpower in their organisation? In this episode of Leaders of Analytics, long-time analytics C-suite executive Murli Buluswar gives us the formula for success. Murli is Head of Analytics, US Consumer Bank at Citi, and leads a team of almost 600 analytics professionals. He reports directly to the CEO and his team is responsible for supplying the rest of the organisation with insights and data-driven solutions that lead to better customer experience and engagement. In this episode of Leaders of Analytics, Murli explains: How to position an analytics function as a key strategic enabler How Citi's analytics department picks and validates the most valuable use cases to work on How to foster the skills and organisational discipline to push analytics into the rest of the organisation How to measure and communicate an analytics team's impact on the company and its customers What's required of analytics leaders to elevate their function to the C-suite, and much more. Murli Buluswar on LinkedIn Previous episode: Why Sport is Leading the Analytics Revolution with Ari Kaplan

Data is revolutionising our world, yet many companies fail to harness its value. What needs to be done for CEOs to see the value of having analytics as part of the executive inner circle? Unfortunately, many analytics teams struggle to move past the common challenges of fostering analytics literacy, getting executive buy-in for more investment in data and analytics and showcasing the value delivered into the business. How can analytics leaders make their discipline an indispensable superpower in their organisation? In this episode of Leaders of Analytics, long-time analytics C-suite executive Murli Buluswar gives us the formula for success. Murli is Head of Analytics, US Consumer Bank at Citi, and leads a team of almost 600 analytics professionals. He reports directly to the CEO and his team is responsible for supplying the rest of the organisation with insights and data-driven solutions that lead to better customer experience and engagement. In this episode of Leaders of Analytics, Murli explains: How to position an analytics function as a key strategic enabler How Citi's analytics department picks and validates the most valuable use cases to work on How to foster the skills and organisational discipline to push analytics into the rest of the organisation How to measure and communicate an analytics team's impact on the company and its customers What's required of analytics leaders to elevate their function to the C-suite, and much more. Murli Buluswar on LinkedIn Previous episode: Why Sport is Leading the Analytics Revolution with Ari Kaplan

“Being an entrepreneur is basically like going from one crisis to the next”. Those are the words of Michael Kingston, co-founder and CEO of Seeda. At a point in his career when Michael was thriving, he took the daunting plunge from successful executive to entrepreneur and start-up founder. Most people would be too scared to take such an enormous risk; however, this step has been Michael's key toward work-life satisfaction. Three years later, Michael and his co-founders have built Seeda, an AI-assisted marketing analytics product, purpose-built for Shopify-based eCommerce platforms. Seeda helps marketers make sense of the enormous amount of data coming at them from numerous sources and use it to optimise their marketing activities. Whether it's SEO, email marketing or digital advertising, marketers are often stuck with a heavy burden of technical9 implementation and optimisation. Seeda's product is the “AI analyst” that helps the world's 5 million Shopify stores figure it all out, without needing to be a technology or analytics expert. If you're curious about start-up life or are thinking about starting your own business, then this episode is for you! In this episode we discuss: How Michael gradually but surely made the shift from employee to entrepreneur How Michael figured out what he wanted to work on as an entrepreneur How Seeda's “AI analyst” is a potential game-changer for Shopify-based businesses wanting more out of their marketing efforts The scaled data architecture that allows small businesses to take advantage of data practices normally reserved for large corporates Michael's advice for anyone wanting to start their own business, and much more. Michael on LinkedIn: https://www.linkedin.com/in/michael-kingston-35707217/ Check out Seeda: https://www.seeda.io/  

“Being an entrepreneur is basically like going from one crisis to the next”. Those are the words of Michael Kingston, co-founder and CEO of Seeda. At a point in his career when Michael was thriving, he took the daunting plunge from successful executive to entrepreneur and start-up founder. Most people would be too scared to take such an enormous risk; however, this step has been Michael's key toward work-life satisfaction. Three years later, Michael and his co-founders have built Seeda, an AI-assisted marketing analytics product, purpose-built for Shopify-based eCommerce platforms. Seeda helps marketers make sense of the enormous amount of data coming at them from numerous sources and use it to optimise their marketing activities. Whether it's SEO, email marketing or digital advertising, marketers are often stuck with a heavy burden of technical9 implementation and optimisation. Seeda's product is the “AI analyst” that helps the world's 5 million Shopify stores figure it all out, without needing to be a technology or analytics expert. If you're curious about start-up life or are thinking about starting your own business, then this episode is for you! In this episode we discuss: How Michael gradually but surely made the shift from employee to entrepreneur How Michael figured out what he wanted to work on as an entrepreneur How Seeda's “AI analyst” is a potential game-changer for Shopify-based businesses wanting more out of their marketing efforts The scaled data architecture that allows small businesses to take advantage of data practices normally reserved for large corporates Michael's advice for anyone wanting to start their own business, and much more. Michael on LinkedIn: https://www.linkedin.com/in/michael-kingston-35707217/ Check out Seeda: https://www.seeda.io/

Digital transformation is rapidly changing the way we live and work, and governments should be leading the way forward, according to Victor Dominello MP. As the Minister for Digital and Minister for Customer Service in the State Government of New South Wales in Australia, Victor believes government should be playing a central role in fostering a digitally-enabled economy across government, private enterprise and individual consumers. Victor is a true servant leader and an inspirational figure in Australian politics, having served almost 15 years in the State Parliament of New South Wales, and 12 of those as a Minister. He has spent this time turning his vision for data and digital enablement into reality across a large number of ministries and government agencies. In this episode we discuss: How Victor went from reluctant politician to long-serving minister and the sign from above that made him enter politics What the Digital Government is and how it will help change our lives for the better Government's role in digitising small businesses Imminent initiatives to protect consumers against identity theft and cyber attacks What true servant leadership and customer service looks like How to provide leadership and collaboration across a complex web of government entities The biggest leadership lessons Victor has learned as a top politician and executive leader, and much more. Victor Dominello on LinkedIn: https://www.linkedin.com/in/victordominello/

Digital transformation is rapidly changing the way we live and work, and governments should be leading the way forward, according to Victor Dominello MP. As the Minister for Digital and Minister for Customer Service in the State Government of New South Wales in Australia, Victor believes government should be playing a central role in fostering a digitally-enabled economy across government, private enterprise and individual consumers. Victor is a true servant leader and an inspirational figure in Australian politics, having served almost 15 years in the State Parliament of New South Wales, and 12 of those as a Minister. He has spent this time turning his vision for data and digital enablement into reality across a large number of ministries and government agencies. In this episode we discuss: How Victor went from reluctant politician to long-serving minister and the sign from above that made him enter politics What the Digital Government is and how it will help change our lives for the better Government's role in digitising small businesses Imminent initiatives to protect consumers against identity theft and cyber attacks What true servant leadership and customer service looks like How to provide leadership and collaboration across a complex web of government entities The biggest leadership lessons Victor has learned as a top politician and executive leader, and much more. Victor Dominello on LinkedIn: https://www.linkedin.com/in/victordominello/

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 practice The importance of attention time in a world without cookies, where privacy and consent are increasingly of mandated importance Dealing with the complexities of multi-layered machine learning pipelines and convincing stakeholders of their value How 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.

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 practice The importance of attention time in a world without cookies, where privacy and consent are increasingly of mandated importance Dealing with the complexities of multi-layered machine learning pipelines and convincing stakeholders of their value How 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.

Inflation is rising, interest rates are up across the globe and cash is king again. How will this impact the flow of venture investments in start-ups and emerging technologies? While traditional investments may suffer during a recession, the venture capital industry has historically been able to weather the storm and even thrive. One reason for this is that venture capital firms typically invest in early-stage companies that are not yet generating significant revenue. In fact, some of the most successful companies in recent history, such as Uber, Airbnb and Snapchat, were founded during economic downturns. The downturns created opportunities for entrepreneurs to innovate and create new solutions to problems caused by the economic conditions. Mendoza Ventures is one such investor, but with a unique approach. Mendoza's investment strategy is focused on the verticals of AI, fintech and cybersecurity and 80% of their investments go to founders from diverse and minority groups. I recently caught up with Scott Heyes, CFO at Mendoza Ventures to understand how a venture capital firm works in practice and how he and his colleagues think about investing in the current economic climate and beyond. In this episode of Leaders of Analytics, we discuss: How Scott became the CFO at Mendoza Ventures and what a week in venture investing looks like How the firm decides which companies to invest in Why Mendoza Ventures specifically back founders from diverse and minority backgrounds. Which segments within AI, fintech and cybersecurity will win or lose during a period of uncertainty, inflation, reduced access to funding and higher borrowing costs. The trends in AI, cybersecurity and fintech worth watching in the next 2-5 years, and much more. Scott on LinkedIn: https://www.linkedin.com/in/scottheyes/ Mendoza Ventures: https://mendoza-ventures.com Learn more about Annual Recurring Revenue in this episode.

Inflation is rising, interest rates are up across the globe and cash is king again. How will this impact the flow of venture investments in start-ups and emerging technologies? While traditional investments may suffer during a recession, the venture capital industry has historically been able to weather the storm and even thrive. One reason for this is that venture capital firms typically invest in early-stage companies that are not yet generating significant revenue. In fact, some of the most successful companies in recent history, such as Uber, Airbnb and Snapchat, were founded during economic downturns. The downturns created opportunities for entrepreneurs to innovate and create new solutions to problems caused by the economic conditions. Mendoza Ventures is one such investor, but with a unique approach. Mendoza's investment strategy is focused on the verticals of AI, fintech and cybersecurity and 80% of their investments go to founders from diverse and minority groups. I recently caught up with Scott Heyes, CFO at Mendoza Ventures to understand how a venture capital firm works in practice and how he and his colleagues think about investing in the current economic climate and beyond. In this episode of Leaders of Analytics, we discuss: How Scott became the CFO at Mendoza Ventures and what a week in venture investing looks like How the firm decides which companies to invest in Why Mendoza Ventures specifically back founders from diverse and minority backgrounds. Which segments within AI, fintech and cybersecurity will win or lose during a period of uncertainty, inflation, reduced access to funding and higher borrowing costs. The trends in AI, cybersecurity and fintech worth watching in the next 2-5 years, and much more. Scott on LinkedIn: https://www.linkedin.com/in/scottheyes/ Mendoza Ventures: https://mendoza-ventures.com Learn more about Annual Recurring Revenue in this episode.

30 years of “corporate social responsibility” has left our planet in dire straits. Biodiversity loss, climate change, water pollution, micro-plastic pollution, air pollution, species collapse, ecosystem collapse…the list goes on. What can we all do individually and collectively as business leaders and responsible humans to turn the situation around? According to Simon Schillebeeckx from Handprint.tech it is possible to create incremental financial value while regenerating the ecosystems we rely on. Simon and his colleagues at Handprint have written a manifesto for saving the planet, called Regeneration First, that tells us exactly how this can be done. In this episode of Leaders of Analytics, we discuss: The current state of the many environmental issues facing us. The “Regeneration First” manifesto and the 7 action shifts needed in our approach to sustainability. Whose role it is to deal with climate change Promising climate technologies that will help us solve the negative impacts we're having on the planet How we create more short-term environmental incentives to deliver long-term impact What we can do individually to contribute to environmental regeneration, and much more. Links: Simon on Linkedin: https://www.linkedin.com/in/simonschillebeeckx/ Some promising carbon removal solutions discussed on the A16Z podcast. The Road to 100 Percent Renewables in Australia via Energy Insiders.

30 years of “corporate social responsibility” has left our planet in dire straits. Biodiversity loss, climate change, water pollution, micro-plastic pollution, air pollution, species collapse, ecosystem collapse…the list goes on. What can we all do individually and collectively as business leaders and responsible humans to turn the situation around? According to Simon Schillebeeckx from Handprint.tech it is possible to create incremental financial value while regenerating the ecosystems we rely on. Simon and his colleagues at Handprint have written a manifesto for saving the planet, called Regeneration First, that tells us exactly how this can be done. In this episode of Leaders of Analytics, we discuss: The current state of the many environmental issues facing us. The “Regeneration First” manifesto and the 7 action shifts needed in our approach to sustainability. Whose role it is to deal with climate change Promising climate technologies that will help us solve the negative impacts we're having on the planet How we create more short-term environmental incentives to deliver long-term impact What we can do individually to contribute to environmental regeneration, and much more.   Links: Simon on Linkedin: https://www.linkedin.com/in/simonschillebeeckx/ Some promising carbon removal solutions discussed on the A16Z podcast. The Road to 100 Percent Renewables in Australia via Energy Insiders.

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 analytics How Bunnings uses data to create operational efficiencies, improve customer experience and optimise pricing How the team prioritises projects and engages with the organisation How the Data & Analytics team is driving a data-driven culture through the company Genevieve'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/

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 analytics How Bunnings uses data to create operational efficiencies, improve customer experience and optimise pricing How the team prioritises projects and engages with the organisation How the Data & Analytics team is driving a data-driven culture through the company Genevieve'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 executive The core elements of customer success and retention that every business should master A deep dive into the concepts of customer retention, expansion and NRR The economics of customer retention and expansion How 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

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 executive The core elements of customer success and retention that every business should master A deep dive into the concepts of customer retention, expansion and NRR The economics of customer retention and expansion How 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

Do you really need a data-driven culture? Maybe not. According to Bill Schmarzo, the CEO's mandate is to become value-driven, not data-driven. For analytics teams that means one thing: no one cares about your data, they want results! In this episode of Leaders of Analytics, Bill and I explore the economics of data & analytics and how to drive powerful decisions with data. Decisions that turn into business value. Bill is the author of four text books and one comic book on generating value with analytics. He is a long-serving business executive, adjunct professor, university educator and global influencer in the sphere of big data, digital transformation and data & analytics leadership. In this episode of Leaders of Analytics, we discuss: Why Bill has split his career between corporate leadership and education What value engineering is and how it pertains to data and analytics How to determine the economic value of data and analytics Why data management the single most important business discipline in the 21st century, and much more. Bill's website: https://deanofbigdata.com/ Bill on LinkedIn: https://www.linkedin.com/in/schmarzo/ Bill on Twitter: https://twitter.com/schmarzo

Do you really need a data-driven culture? Maybe not. According to Bill Schmarzo, the CEO's mandate is to become value-driven, not data-driven. For analytics teams that means one thing: no one cares about your data, they want results! In this episode of Leaders of Analytics, Bill and I explore the economics of data & analytics and how to drive powerful decisions with data. Decisions that turn into business value. Bill is the author of four text books and one comic book on generating value with analytics. He is a long-serving business executive, adjunct professor, university educator and global influencer in the sphere of big data, digital transformation and data & analytics leadership. In this episode of Leaders of Analytics, we discuss: Why Bill has split his career between corporate leadership and education What value engineering is and how it pertains to data and analytics How to determine the economic value of data and analytics Why data management the single most important business discipline in the 21st century, and much more. Bill's website: https://deanofbigdata.com/ Bill on LinkedIn: https://www.linkedin.com/in/schmarzo/ Bill on Twitter: https://twitter.com/schmarzo  

Great analytics teams understand that they are responsible for two things concurrently: production and consumption. Most analytics teams master the production part well. After all, that's why they exist, to produce analytics. However, analytics only matter if someone consumes them and makes valuable decisions as a result. “Decision + value” is what we're after. To be able to make valuable decisions from analytics, consumers must be data and analytics literate, and that often comes down to education and culture creation. So, how do you build analytics literacy in your organisation? In this episode of Leaders of Analytics, Ben Jarvis, Head of Scaled Customer Services and Operations AUNZ at Google, answers this question and many more related to building a strong analytics culture. Listen to learn: How Ben went from practicing law to becoming a senior analytics leader and operational GM How to coach and mentor technical and non-technical stakeholders on data and analytics literacy How do traditional businesses that aren't born out of the internet era can transform into data-driven and analytics-literate organisations, and much more. Connect with Ben on LinkedIn: https://www.linkedin.com/in/ben-stuart-jarvis/

Great analytics teams understand that they are responsible for two things concurrently: production and consumption. Most analytics teams master the production part well. After all, that's why they exist, to produce analytics. However, analytics only matter if someone consumes them and makes valuable decisions as a result. “Decision + value” is what we're after. To be able to make valuable decisions from analytics, consumers must be data and analytics literate, and that often comes down to education and culture creation. So, how do you build analytics literacy in your organisation? In this episode of Leaders of Analytics, Ben Jarvis, Head of Scaled Customer Services and Operations AUNZ at Google, answers this question and many more related to building a strong analytics culture. Listen to learn: How Ben went from practicing law to becoming a senior analytics leader and operational GM How to coach and mentor technical and non-technical stakeholders on data and analytics literacy How do traditional businesses that aren't born out of the internet era can transform into data-driven and analytics-literate organisations, and much more. Connect with Ben on LinkedIn: https://www.linkedin.com/in/ben-stuart-jarvis/

If you're anything like me, you have a love/hate relationship with marketing. Marketing can be delightful, obnoxious or somewhere in-between, depending on content and context. Most of us remember an ad from our youth that has given us a life-long emotional connection to a brand or product. Most of us also remember that obnoxious sales call or email campaign that made us swear never to buy from the offending company again. In this episode of Leaders of Analytics, you will learn from Ikechi Okoronkwo why data-driven marketers have a leg-up when it comes to designing and executing impactful campaigns that hit the right audiences and create delight. Ikechi is Executive Director, Managing Partner and Head of Business Intelligence & Analytics at Mindshare. Mindshare is a global media and marketing agency, and part of global marketing powerhouse GroupM. Listen to this episode to learn: What Ikechi sees as the biggest opportunities in data-driven marketing What kinds of analytics to invest in to optimise the impact of your marketing efforts What kinds of data is needed to take advantage of these opportunities, and how to collect it How Ikechi and colleagues use data and analytics to distinguish between rational and emotional reactions to advertising How to drive a culture of experimentation and measurement among colleagues and stakeholders who are more creatively than analytically minded, and much more. Ikechi on LinkedIn: https://www.linkedin.com/in/ikechi-okoronkwo-0318579/

If you're anything like me, you have a love/hate relationship with marketing. Marketing can be delightful, obnoxious or somewhere in-between, depending on content and context. Most of us remember an ad from our youth that has given us a life-long emotional connection to a brand or product. Most of us also remember that obnoxious sales call or email campaign that made us swear never to buy from the offending company again. In this episode of Leaders of Analytics, you will learn from Ikechi Okoronkwo why data-driven marketers have a leg-up when it comes to designing and executing impactful campaigns that hit the right audiences and create delight. Ikechi is Executive Director, Managing Partner and Head of Business Intelligence & Analytics at Mindshare. Mindshare is a global media and marketing agency, and part of global marketing powerhouse GroupM. Listen to this episode to learn: What Ikechi sees as the biggest opportunities in data-driven marketing What kinds of analytics to invest in to optimise the impact of your marketing efforts What kinds of data is needed to take advantage of these opportunities, and how to collect it How Ikechi and colleagues use data and analytics to distinguish between rational and emotional reactions to advertising How to drive a culture of experimentation and measurement among colleagues and stakeholders who are more creatively than analytically minded, and much more. Ikechi on LinkedIn: https://www.linkedin.com/in/ikechi-okoronkwo-0318579/

Business leaders are changing. Today, it's not enough to be a strategic thinker and good people leader to be successful in the corporate world. Why? Modern business leaders are customer-centric and understand how to create a personalised customer experience using customer data. Modern business leaders are data-driven and understand how to make decisions based on probabilistic outcomes, not just gut feel. Modern business leaders understand what it takes to develop and deploy artificial intelligence in their organisation. So, how do we educate our future business leaders to be analytics literate, technically capable and able design and use AI effectively and responsibly? I recently spoke to Professor Hind Benbya to answer this question and many more relating to educating our future business leaders. Hind is the Head of the Department of Information Systems & Business Analytics at Deakin University, where she leads the strategic direction of the department as well as academic aspects of teaching, research and industry engagement. In this episode of Leaders of Analytics, you will learn: The critical must-learn skills for students wanting to shape the future of business with data and analytics The role of data, analytics and AI in business 10 years from now and how today's business leaders must prepare How we bring today's business leaders and executives up to speed with data and analytics How analytics leaders can drive their organisations to become truly data-driven, and much more. Hind on LinkedIn: https://www.linkedin.com/in/hindbenbya/ Hind's research and publications: https://scholar.google.com/citations?user=KNAW0xsAAAAJ&hl=en Deakin's Department of Information Systems & Business Analytics: https://www.deakin.edu.au/business/department-of-information-systems-and-business-analytics

Business leaders are changing. Today, it's not enough to be a strategic thinker and good people leader to be successful in the corporate world. Why? Modern business leaders are customer-centric and understand how to create a personalised customer experience using customer data. Modern business leaders are data-driven and understand how to make decisions based on probabilistic outcomes, not just gut feel. Modern business leaders understand what it takes to develop and deploy artificial intelligence in their organisation. So, how do we educate our future business leaders to be analytics literate, technically capable and able design and use AI effectively and responsibly? I recently spoke to Professor Hind Benbya to answer this question and many more relating to educating our future business leaders. Hind is the Head of the Department of Information Systems & Business Analytics at Deakin University, where she leads the strategic direction of the department as well as academic aspects of teaching, research and industry engagement. In this episode of Leaders of Analytics, you will learn: The critical must-learn skills for students wanting to shape the future of business with data and analytics The role of data, analytics and AI in business 10 years from now and how today's business leaders must prepare How we bring today's business leaders and executives up to speed with data and analytics How analytics leaders can drive their organisations to become truly data-driven, and much more.   Hind on LinkedIn: https://www.linkedin.com/in/hindbenbya/ Hind's research and publications: https://scholar.google.com/citations?user=KNAW0xsAAAAJ&hl=en Deakin's Department of Information Systems & Business Analytics: https://www.deakin.edu.au/business/department-of-information-systems-and-business-analytics

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 these How data science is used to help the environment Why Serg wrote the book ‘Interpretable Machine Learning with Python' and why we should read it How 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

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 these How data science is used to help the environment Why Serg wrote the book ‘Interpretable Machine Learning with Python' and why we should read it How 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 2016 The evolution of analytics and data science in sports What the business world can learn from sports in terms of using analytics to gain a competitive edge Where sports analytics is going in the future, and much more.

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 2016 The evolution of analytics and data science in sports What the business world can learn from sports in terms of using analytics to gain a competitive edge Where sports analytics is going in the future, and much more.

It's no secret that data and analytics can be used to create a competitive advantage for almost any modern business. In fact, the customer data you capture in the course of doing business is one of the strongest differentiators between you and the competition. So, how do we build an organisation that is capable of both producing and consuming truly differentiating data products? It's not enough to just have a great analytics team that is capable of producing high quality work. We also need an organisation that is able to consume this output, however advanced it might be. Back by popular demand, analytics executive and author of ‘Building Analytics Teams' John Thompson is returning to Leaders of Analytics to talk about the future of analytics leadership. In this episode, we discuss: Where analytics teams should sit in the organisational structure The typical mistakes businesses make when designing analytics teams and embedding them in the organisation How we plant the seed of advanced analytics and build a data-driven culture How we select and prioritise the right data and analytics projects to work on The main purpose and remit of a Chief Data & Analytics Officer What the perfect data-driven organisation looks like, 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 Defensive vs. offensive data & analytics: https://hbr.org/2017/05/whats-your-data-strategy

It's no secret that data and analytics can be used to create a competitive advantage for almost any modern business. In fact, the customer data you capture in the course of doing business is one of the strongest differentiators between you and the competition. So, how do we build an organisation that is capable of both producing and consuming truly differentiating data products? It's not enough to just have a great analytics team that is capable of producing high quality work. We also need an organisation that is able to consume this output, however advanced it might be. Back by popular demand, analytics executive and author of ‘Building Analytics Teams' John Thompson is returning to Leaders of Analytics to talk about the future of analytics leadership. In this episode, we discuss: Where analytics teams should sit in the organisational structure The typical mistakes businesses make when designing analytics teams and embedding them in the organisation How we plant the seed of advanced analytics and build a data-driven culture How we select and prioritise the right data and analytics projects to work on The main purpose and remit of a Chief Data & Analytics Officer What the perfect data-driven organisation looks like, 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 Defensive vs. offensive data & analytics: https://hbr.org/2017/05/whats-your-data-strategy

Every company, regardless of size, is dealing with a barrage of data. In any typical organisation, there is more information on hand than we know how to use or manage. While every team in the organisation is screaming for analytics professionals to turn data into insight, a strong data and analytics tech stack is foundational to being able to make sense of it all. The need for a robust and efficient data and analytics tech stack has created a sprawling industry for new technology solutions that sell the promise of seamless integration and faster insights. Today, there are a plethora of data and analytics platforms available, most with very high valuations attached to them. But do we really need all these tools to make us super-powered data users? To answer this question and many more related to the data and analytics tech stack, I recently spoke to Benn Stancil. Benn is the co-founder and Chief Analytics Officer at Mode. Mode is a modern analytics and BI solution that combines SQL, Python, R and visual analysis to answer questions for its users. In this episode of Leaders of Analytics, you will learn: What the perfect analytics tech stack looks like and why. Programmatic automation of the analytics workflow. What will cutting-edge analytics tech be able to do 5-10 years from now. Why Been thinks the Chief Analytics Officer role should be redefined, and much more. Connect with Benn Benn on LinkedIn: https://www.linkedin.com/in/benn-stancil/ Benn on Twitter: https://twitter.com/bennstancil Benn's (brilliant) Substack blog: https://benn.substack.com/

Every company, regardless of size, is dealing with a barrage of data. In any typical organisation, there is more information on hand than we know how to use or manage. While every team in the organisation is screaming for analytics professionals to turn data into insight, a strong data and analytics tech stack is foundational to being able to make sense of it all. The need for a robust and efficient data and analytics tech stack has created a sprawling industry for new technology solutions that sell the promise of seamless integration and faster insights. Today, there are a plethora of data and analytics platforms available, most with very high valuations attached to them. But do we really need all these tools to make us super-powered data users? To answer this question and many more related to the data and analytics tech stack, I recently spoke to Benn Stancil. Benn is the co-founder and Chief Analytics Officer at Mode. Mode is a modern analytics and BI solution that combines SQL, Python, R and visual analysis to answer questions for its users. In this episode of Leaders of Analytics, you will learn: What the perfect analytics tech stack looks like and why. Programmatic automation of the analytics workflow. What will cutting-edge analytics tech be able to do 5-10 years from now. Why Been thinks the Chief Analytics Officer role should be redefined, and much more. Connect with Benn Benn on LinkedIn: https://www.linkedin.com/in/benn-stancil/ Benn on Twitter: https://twitter.com/bennstancil Benn's (brilliant) Substack blog: https://benn.substack.com/

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 it How Francesco came up with the concept of the USS Real-life examples of how to use the USS The benefits of a USS over a traditional star schema galaxy How 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

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 it How Francesco came up with the concept of the USS Real-life examples of how to use the USS The benefits of a USS over a traditional star schema galaxy How 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