AiThority Interview with Bob Parr, Chief Data Officer at KPMG

Hi, Bob, please tell us about your role and the team / technology you handle at KPMG. How did you reach here?

I am the Chief Data Officer for our U.S. Advisory Practice at KPMG, where I lead our Advisory Services’ data agenda and help teams integrate a data-driven approach into their services and offerings. My goal is to inspire and support our focus to become more data driven – in how we serve clients, our people, and communities.

I joined KPMG over ten years ago and have spent my time at the firm working with Chief Data Officers. I have over 25 years of experience within the financial services industry, during which I’ve worked for two top tier U.S. banks on senior leadership teams.

How did your role evolve through the pandemic months?

AI and the capabilities that it offers has expanded dramatically over the last few years, and it’s no secret that the pandemic has accelerated this even further.

Yet, as organizations have dialed up their use of data-driven technologies, data literacy and employee preparedness tend to be overlooked. Throughout the pandemic, my focus has remained on developing strategies that are helping business leaders bolster data literacy within their organizations.

At the same time – the pandemic accelerated the use and focus on data. Many of us routinely began going to common COVID sites to track spread, infection rates and trends.  Awareness of the role data plays exploded and so has my organization’s appetite for data assets.  In response, I created a new category of roles entitled the “Data Concierge,” whose job is to interpret the business need that our solution and client teams have – and connect them with the right data – where ever it is. Even if we have to buy it from a trusted source.  This new role was especially important since our client teams would otherwise spend too much valuable time getting the data vs using the data.

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Could you shed some light on KPMG’s D&A literacy program? How does it align with the current demand for highly skilled data science professionals and analysts?

Our progressive, 5 level Data & Analytics Literacy Program is designed to deepen employees’ understanding of data, analytics, and AI, ultimately enabling them to deliver meaningful data-driven insights to clients on a greater scale. It realizes that our people have varying skill levels – so it is used differently depending on your role and current capabilities.  This has been rolled out to over 10,000 of our Advisory Professionals.

As part of the program, for those with an established base of data and analytics and looking to deepen their focus on AI, we’re leveraging the deep capabilities of some of the MIT Sloan School of Management  and the University of California – Irvine – Paul Merage School of Business.

What I’m really excited about is the Data Citizens with Purpose Program, which allows the professionals to apply their new skills, and offers pro-bono data-driven services to non-profits. The program is helping more than 80 nonprofit organizations across the country impacting a range of social, inclusion, diversity and environmental issues. The Data Citizens program blends our desire to help the communities we serve with our need to develop and upskill our professionals through applied learning experiences.

While the demand for skilled data science professionals will continue to grow as AI adoption increases, organizations also need to focus on training and upskilling their existing talent. This is a real challenge that will have lasting impact on business performance.

According to you, which industries have been the most agile in the adoption of data and analytics technologies? How is KPMG playing its role in leveling the field for others?

We’re seeing executives across industries looking to AI to deliver value. According to a recent KPMG survey, 92% of respondents agree AI would make their organization run more efficiently, and individual industries report confidence in AI’s potential to solve some of their biggest challenges.

It’s been incredibly fascinating to see how business leaders are using AI to solve major industry problems, including the challenges brought on by COVID-19, including helping with vaccine development and distribution, detecting fraud, and improving bureaucratic efficiency.

KPMG’s network of Data & Analytics professionals recognizes that analytics has the power to create great value. That is why they take a business-first perspective, helping solve complex business challenges using analytics that clients can trust.

That’s why we’re so excited about our new Data & Analytics Literacy program. Because it provides our Advisory professionals a foundation that can be built upon and customized to achieve the broad vision of a data-driven organization. Our approach delivers a multi-path program built on learning levels that combine highly relevant, customized content with a variety of delivery methods to balance cost, scale and effectiveness.

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What are your thoughts on the concept of Data Democratization? Does it really work in the practical world where companies are building strategies to stay competitive with cutting edge innovation?

Data democratization is fundamentally about broadening the base of access and not just relying on a specialized group of data scientists to perform analysis. I believe data democratization, data literacy and a robust data control environment go hand in hand. All three of these should be a key focus/ concern for organizations if they are looking to accelerate an organization’s use of D&A. The risk of data being underleveraged within organizations can ultimately be a liability and barrier to achieving business objectives. Data democratization ensures that the right people can access data with the appropriate level of guardrails on its use – so organizations can more quickly develop the insights that drive innovation. However, without broad based data literacy and a proper understanding of how to leverage these insights, it’s essentially useless.

While a literacy program provides a structured approach to upskilling the workforce – organizations will also need to focus on the platforms to physically connect their people to the data assets of the organization.   Historically platforms and applications have had more of an IT focus that created a technical barrier to broad use.  However, today with the advent of data exchange technologies and 3^rd party data marketplaces – organizations can much more quickly deploy mechanisms that allow users to explore and “shop” for data the way they would on buying a consumer product.  From a control perspective, KPMG has pioneered the use a policy engine to help track restrictions on the use of data to help prevent non permissible use.  Using data isn’t binary.  Some uses are permitted, others aren’t. This complexity was historically a major barrier to data democratization. This is especially critical given data privacy, contractual and firm data policy that are all changing very quickly.  Policy engine type controls enable organizations to use their data assets with confidence.

What are your predictions on the future of AI-based analytics for business decision-making teams? Would AI training become a part of the culture / organization development processes?

The need for data-driven organizations and cultures isn’t going away, and organizations will continue to need AI training programs integrated within their business strategies. This will only increase in importance, especially as AI continues to be a key driver in unlocking greater value for organizations.

Data & Analytics needs to be seen as a vital business driver that is anchored in a wider understanding of the business strategy and its objectives. With that, it’s critical that organizations are arming their employees at all levels with the skills required to derive value from the capabilities and insights that AI has to offer.

Up to now – just getting people involved, aware and starting to acquire the basics of data, analytics and AI has absorbed much of the calories expended in this space by organizations.   However, in the next several years we need to also focus on not just CAN we do it but rather SHOULD we do it.   Trusted AI and Data Ethics will start to become more than just conceptual as pragmatic frameworks, tools and training enable the controls needed to help prevent/mitigate bias in datasets that skew conclusions and machine learning results.   Increasingly this will become an essential part of Data, Analytics and AI literacy programs across organizations.

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 Tell us more about the hiring trends in the AI-based tech industry. What kind of talent / skills do you hire for in your company to lead Product and Marketing goals?

The competition for deep data and AI talent remains very fierce.  The AI skills has been and will continue to stratify.   For instance, Data Science resources increasingly will specialize into Conversational AI, Computer Vision, Deep Learning as the frontier for these areas continue to advance.   Supply will continue to be very constrained for these resources.   There are skill tiers below the data scientist where we will see the largest volume growth.  Aided by tools sets that increasingly low code in their orientation, basic machine learning capabilities are now accessible to a much broader base of resources.   For these resources we focus on:  1) Domain subject matter expertise paired with the Data, Analytics and AI capabilities  2) Data Story Telling – how well can they interpret the insights (or the results) and translate that back to the business objectives.   This is an essential skill often overlooked by more technical resources.

Thank you, Bob! That was fun and we hope to see you back on AiThority.com soon.