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.

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 roomsHow Coert became the CEO of MaxMineWhy our ability to tackle climate change depends on the mining industryHow to build and sell successful data productsCoert’s approach to building a fulfilling and rewarding career in data and analyticsThe 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

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 initiativesExamples of organisations doing data well, and whyHow to set clear expectations around the responsibilities of CDAOsThe 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 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 journeyThe typical pain points lenders face and how RDC’s unique AI solution solves these problemsWhat makes RDC’s solution unique and why banks should buy rather than build themselvesHow to find product-market fit or an AI productThe 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.

“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 entrepreneurHow Michael figured out what he wanted to work on as an entrepreneurHow Seeda’s “AI analyst” is a potential game-changer for Shopify-based businesses wanting more out of their marketing effortsThe scaled data architecture that allows small businesses to take advantage of data practices normally reserved for large corporatesMichael’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/  

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

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 likeHow the firm decides which companies to invest inWhy 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.

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

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 analyticsThe role of data, analytics and AI in business 10 years from now and how today’s business leaders must prepareHow we bring today’s business leaders and executives up to speed with data and analyticsHow 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 theseHow data science is used to help the environmentWhy Serg wrote the book ‘Interpretable Machine Learning with Python’ and why we should read itHow to make models more interpretable, and much more.Connect with Serg: Serg's website: https://www.serg.ai/#about-me Serg on LinkedIn: https://www.linkedin.com/in/smasis/ Serg's books from Packt: https://www.packtpub.com/authors/serg-masis

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

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

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

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

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/

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

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

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

We are living in an artificial revolution where the balance of power and political influence is shifting towards those who control data and technology. Automation is transforming our economies and making some jobs obsolete. Companies harvest our most intimate secrets and use them to feed us tailored information and sell us products. The metaverse is the development of a virtual world with the potential to separate us from the physical world altogether. AI is making our lives more curated and convenient, but at the same time more complex and exposed. Privacy and ethics have to be programmed by design to avoid digital versions of oil spills and nuclear disasters. I recently spoke to Ivana Bartoletti to understand how humanity can tackle this newfound challenge. Ivana is the Global Chief Privacy Officer at Wipro and an internationally recognised thought leader in the field of data privacy and AI ethics. She is also the co-founder of the Women Leading in AI Network and the author of the brilliant book on the risks and opportunities of AI, called 'An Artificial Revolution: On Power, Politics and AI'. In this episode of Leaders of Analytics, we discuss: Why everyone should give heed to the challenges of privacy, ethics and fairness in a world driven by dataHow to balance the trade-off between the benefits of AI and the risks of compromised privacyHow large-scale automation will impact society as a wholeWhy data is inherently politicalWhy woman have a special role to play in making AI fair, and much more.Learn more about Ivana and her projects here: http://www.ivanabartoletti.co.uk/ Connect with Ivana: https://www.linkedin.com/in/ivana-bartoletti-77b2b29/

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

In my opinion, any organisation with respect for its data should have a Chief Data & Analytics Officer (CDAO) as part of their C-suite. Although the CDAO role is still nascent, business leaders across many industries are starting to appreciate the need for a data and analytics voice at board and executive level. So, what does a CDAO do? How should they spend their time to balance strategic influence with operational delivery of data products? To answer these questions and many more related to the principal analytics role, I recently spoke to Kshira Saagar, who is the Chief Data Officer at Latitude Financial. As the CDO at one of Australia’s largest consumer financial services firms, Kshira is responsible for the end-to-end journey of data through the organisation, from extraction to value creation through data products. He leads a large team of Data Scientists, Data analysts, Data Architects, Data Engineers, Machine Learning Engineers, Data Warehouse Developers, BI Developers and Data Governance experts, who are responsible for bringing the company’s data and analytics strategy to life. In this episode of Leaders of Analytics, we discuss: What a week in the role of a CDAO looks likeHow to secure strategic support and executive sponsorship for analytics projectsWhat’s required of CDAOs and their teams to foster a data literate organisationHow to structure data and analytics functions for successThe future of the CDAO role, and much more.Learn more about Kshira at https://www.kshirasaagar.com/

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

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