The Three Roles of the Chief Data Officer: ADP’s Jack Berkowitz

**Sam Ransbotham** (0:02)
When outcomes don't motivate artificial intelligence efforts, how can they be successful? Find out how one Chief Data Officer thinks about AI on today's episode.

**Jack Berkowitz** (0:12)
I'm Jack Berkowitz from ADP, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:18)
Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI.
I'm Sam Ransbotham, Professor of Analytics at Boston College. I'm also the AI and Business Strategy guest editor at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:37)
And I'm Shervin Khodabandeh, senior partner with BCG, and I co-lead BCG's AI practice in North America. Together, MIT SMR and BCG have been researching and publishing on AI for six years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

**Sam Ransbotham** (1:03)
Today, Shervin and I are excited to have Jack Berkowitz, Chief Data Officer of ADP. Jack, thanks for joining us. Welcome.

**Jack Berkowitz** (1:09)
Thank you. Glad to be here.

**Sam Ransbotham** (1:11)
Let's get started. You're the Chief Data Officer at ADP. Can you tell us about what that role means?

**Jack Berkowitz** (1:16)
ADP, known as Automatic Data Processing, is the world's largest provider of HR services, payroll, taxes, things like that. We operate in 140 countries. We have over 900,000 clients. Millions of people are getting paid from us every day.
I sort of have a two-sided job. On the one hand, I'm responsible for all of the data that flows through our systems. We're a really big company. We have massive amounts of data. So all the things are classically associated with Chief Data Officers. Things about data governance, data security, usage of analytics.
The other side of my job, and it's probably even a bigger job, is I build data products. And so my team builds people analytics, benchmarks, compensation information, all that type of products that our clients are using to take decisions about the world of work every day.

**Sam Ransbotham** (2:08)
I didn't hear the word artificial intelligence in there anywhere. How is that involved?

**Jack Berkowitz** (2:13)
I also run that for the company as well. But we use machine learning throughout those processes, whether we're cleaning the information, whether we're building embedded capabilities in our HR applications or our payroll applications, whether we're doing things like aligning job titles. People would say, well, how hard can that be? You know, in any given month, we pay about 21 million people. We have about 14 million job titles.
And we crunch that down to between 6,000 and 8,000 job titles. So, an awful lot of very sophisticated natural language processing and machine learning to make that happen.

**Shervin Khodabandeh** (2:49)
It seems like there's three different roles that you mentioned that all come together. And I say this because at many companies, there are literally three different roles for what you mentioned for data governance, for data products, and for AI, which creates maybe a bit of silo-ness and a bit of maybe disconnectedness, right? Because all these things have to work together.
Comment a bit, please, on how it came about that it's one person leading all three. That's my first question. And then my second question is, is the AI involvement only in the data products? Or is there a broader role that you have that you're also supporting AI for the broader enterprise?

**Jack Berkowitz** (3:35)
It's a really good question. The thing to know about ADP is, yes, we're a services company in the sense that we provide, for example, payroll for about one in six or even more than that people in the US. But we also are a SaaS product company.
And because of that, there's a whole bunch of different development organizations working on building SaaS products, whether it's for the small businesses all the way up to the biggest companies in the world, using our applications to do HR or recruiting or payroll or taxes, things like that. And because of that, this role emerged, really, started as building data products. But to build data products and things like reporting, it grew the data platforms. And off the data platforms, it grew more and more capabilities in terms of doing machine learning, best practices, we got into the ethical use of data and the ethical use of machine learning and AI. And that allowed us to be additive in terms of capabilities.
The other thing about it, then, is, well, okay, well, where's the extent? Because we have all of those SaaS applications, my teams will sometimes build the embedded capabilities for other applications. But we also enable those other development organizations to use the frameworks that we build. So we, for example, build a whole bunch of machine learning operations capabilities, things about bias monitoring and data shape monitoring, because that makes sense to be done once in a company and then allow other people to take advantage of it. We've seen a massive growth in people identifying themselves as data scientists over the past four years. We've been hiring people and everything else, but they don't all have to learn how to do model deployment into production.