Todd Foley: Hello and welcome to the CDO Magazine interview series. I’mTodd Foley, Chief Digital Officer and CISO with Lydonia, an AI solutions company. Today, I have the pleasure of speaking withShruti Sharma, Data Delivery Team Leader for Analytics, Insights, and Automation at Philips. One of the things that I always like to say is that nowadays everyone has self-service data or at least declares they have, but nobody is truly self-service in the same way across all of the individuals in an organization, right? And there’s often a big gap in being able to meet people where they are on their journey, as you described as a critical part of that. Well, one of the things we are seeing is that AI has the promise of being able to bridge some of those gaps and analytic maturity between different groups within organizations and democratize analytics a little bit. But how do you see AI enabling individuals to become better at self-service analytics?
Shruti Sharma: I’m not really sure how much this whole self-service model has really happened. But I do see AI being increasingly used to solve the last-mile problem, where users can interact with data models in a more natural way—asking questions. But everything ties back to: Do you have your semantic models labeled appropriately? Are they tied? You’ve got to have your KPIs, not just your KPIs tagged on your data products, but a truly shared vocabulary with your users. Because you will get what you ask for, but are you asking the right question?
Todd Foley: Well, you’ll get an answer that might be wrong.
Shruti Sharma: Yeah, exactly. So, do you know what you’re asking for? Do you stand by that? Do you know what that means? A lot of things, which are less sexy when it comes to AI, are getting your house in order, having visibility and a common vocabulary for terms, and knowing what that is. So, like the data lineage, data dictionaries—knowing what’s an apple and what’s an orange. When you get into that.
Todd Foley: I think it’s interesting. I think AIcertainly has the potential to make engagement with data more natural for users, even with only basic analytics proficiency. But it also makes it incumbent upon us to make sure that the abstraction layer is backed by actual data and structure. You know, it’s funny. At my company, Lydonia, we do AI solutions. You’d be amazed how much master data management work we do to make those solutions actually effective. And I think that those challenges—splitting the challenge of making things more accessible, meeting people wherever they might be in terms of their proficiency or understanding, but making sure that the underlying capability and ultimately the answers they obtain are accurate and usable—are huge. The abstraction makes it more complex on the back end. It also exposes challenges that maybe weren’t exposed before, right? Because people weren’t accessing that data as broadly or in real time, right? And that just makes the technical complexity a little bit more. How do you see that evolving? How do you approach it? Is it really blocking and tackling on the data side and then trying to open up access, or is it more nuanced than that?
Shruti Sharma: I think it’s all of the above. You can’t wait to get your data house in order before you can open the gates because the demand is that much more. The engagement, the thirst for knowledge or information is greater. So, you’ve got to have all your asterisks about creating a more curated experience, but you’ve got to have human oversight. So, this is what you asked for. Is this what you mean? I’m aware of certain companies that are working on an AI, like an analytic assistant, who’s going to do certain things for you. But what’s complicated in that is how do you get users’ sign-offs, right? Like, is this what you want? You may want it, but is this what you need? A lot of times when you’re dealing with ambiguity, that’s where humans work best. We are best at dealing with ambiguity—some even better than others. But overall, like AI at this point, to a large extent, and I love this analogy I heard from within my community, is that at this point, you should see AI as this enthusiastic college intern. You’ve got to give it tasks that are very specific, and you’ve got a very clear definition of what’s acceptable and what’s not acceptable. And while it’s learning rapidly, that’s where it’s at today. And you’ve got to be realistic about what you’re asking AI to do.
Todd Foley: We’ve covered a lot of ground here. I think there are themes that you’ve stressed that are absolutely critical and I don’t always hear in conversations like this—around making sure that you have effective teams and how you go about that, paying attention to the relationships and the trust aspect of engagement, both within your own teams as well as within the business. How critical it is to take the variable technology and the ever-changing landscape of those things that we’re being asked to do and approaching it in a logical way with a more holistic business context, but also trying to drive things forward, trying to have greater impact, greater effect on the business, and more positive outcomes. I think that’s a very experienced viewpoint and one that I’m sure comes with plenty of stories along the journey. If you had to give advice to people, maybe even to a biologist who was considering getting into the data space, what would you say to them that you wish you had heard back then as well?
Shruti Sharma: Jump in with both feet. Don’t hold back. There’s a lot of ground to be discovered. There is a lot of impact that the world still needs. And the whole space needs more intentional, more driven, more committed people than ever.
Todd Foley: I think your enthusiasm clearly comes through just in the way you talk about it. And I love that the North Star for you from the get-go was the immediate gratification aspect of this, which compared maybe to longer research, medical research projects, is true and was even true back then. But I think it’s probably more so today than it ever has been. And certainly with your approach, your ability to engage with the business, to be able to have an impact, to shorten that time from insight to impact is something not only to be proud of, but I think something we all aspire to.
Shruti Sharma: In fairness, though, when I’m talking about instant gratification, I’m talking about decades to years. It still takes time to do things worth doing together. And as long as it’s time well spent, and that’s when the vision comes in—that’s when what you’re working for, the “why,” is important. Because the day-to-day gets grabbed if you don’t have a North Star to look to. If you don’t know what you’re working towards, what are you making better?
Todd Foley: I love that. Clearly, it’s a message that allows you to inspire your teams as well. Thank you for joining me today. For those who are listening to this for more interviews and insights, please visit cdomagazine.tech. And Shruti, thank you. It’s truly been a pleasure.
Shruti Sharma: Thank you, Todd. Thanks so much.
