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.

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 structureThe typical mistakes businesses make when designing analytics teams and embedding them in the organisationHow we plant the seed of advanced analytics and build a data-driven cultureHow we select and prioritise the right data and analytics projects to work onThe main purpose and remit of a Chief Data & Analytics OfficerWhat 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/

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

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

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

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

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

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

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

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

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

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

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’re at a crossroads when it comes to data and its ability to make a difference. Data sprawl has become a real and costly problem inside organizations, and it is hurting innovation. Throwing good money at bad ideals is no longer acceptable, ROIs must be attained. Let us embrace innovative technology, but let us also keep in mind that data itself is useless unless you do something with it!" These are the words of Carla Gentry, one of my guests in this episode. And I agree with her. Data is a strategic asset in most organisations and need to be organised, managed and deployed with the same respect and rigour as a company’s financial capital. For data leaders it is now incumbent on them to be more than technical specialists. We need to set the vision and agenda, in terms of what data can create for customers and the business. We need to lead our organisations, not just work in them. In this episode of Leaders of Analytics, we hear from Carla Gentry, Owner and Chief Data Scientist at Analytical-Solution and Whitney Myers, CEO of Zuar on what it takes to succeed with data in 2022 and beyond. Carla and Whitney are true experts and thought leaders in data-driven business leadership and I trust that you will learn a lot from the two of them, just like I have. Learn more about Carla here: Website: https://analytical-solution.com/ Twitter: @data_nerd LinkedIn: https://www.linkedin.com/in/datanerd13/ Learn more about Whitney here: Website: https://www.zuar.com/ LinkedIn: https://www.linkedin.com/in/whitney-myers-365b057/

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”.

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

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/