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 Intro03:00 What Psyda Does04:23 Academic Work and Modern Education06:38 Getting into Data Science11:30 What is Synthetic Data13:30 Common Applications for Synthetic Data18:50 Pros & Cons of using Synthetic Data21:29 Risks of using Synthetic Data23:48 When should Synthetic Data be Used29:23 Synthetic Data is Cleaner than Real Data34:05 Using Synthetic Data for Risk Mitigation36:05 Resources on Learning More about Synthetic Data38:05 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_nerdLinkedIn: 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/