From Courtside to CDO: Inderpal Bhandari's Journey from NBA Analytics to Global Data Visionary

Chris: [00:00:00] Welcome to another Data Driven Podcast. My name is Chris Detzel. And I have two special guests on . One is Manish Sood. He is the CEO and founder of Reltio. How you doing,

Manish Sood: Manish?

Manish Sood: Doing well, Chris great to be on the show again. Yeah, glad to have you on.

Chris: And then we have none other than Dr.

Chris: Inderpal Bhandari. He is an independent director at Walgreens Boots Alliance and the former IBM Global Chief Data Officer. How are you

Inderpal: today, Inderpal?

Inderpal: I am very good, Chris. I'm very happy to be here.

Chris: We're glad to have you. What I'm going to do is I'm going to give the reins over to Manish and let him kick this off.

Manish Sood: Manish?

Manish Sood: Thank you, Chris. And in the ball, it's great to have you on the show. There's so many questions in the age of data where everybody is talking about the things that can be done with AI and how data is a key ingredient. [00:01:00] For all of that work. I have so many questions for you. And I'm sure the audience is really interested as well.

Manish Sood: But before we dive into any of that, um, everybody's heard about money ball and in a way you were a pioneer. In applying the same kind of principles to scouting talent for NBA, so, tell me a little bit more about that. How did you get started with that? How did that come about? And just a little bit of a background that would help us understand how, very early on in your career.

Manish Sood: You were able to apply some of these data type of principles and techniques. to something that we all love, NBA.

Inderpal: Yes. No. And it's interesting that you mentioned Moneyball, Manish, because that work that you talk about predated Moneyball. And it was only [00:02:00] later that I realized that I should have actually just written the book.

Inderpal: As opposed to starting a company and doing all that stuff around it, because I did have all the access in the NBA to, to many of the players, definitely all the coaches, the commissioner Davidson, who was the commissioner then, and we could have easily made for a tremendous book, a tremendous story, but that was a learning experience, so to speak, but we did have tremendous fun in terms of the project itself.

Inderpal: And to answer your question about what led me there, it's probably useful to back up a few years and then give you the progression. And it all it all is relevant in the sense that it all applies to the general topic that you're interested in this podcast. So it started with my PhD thesis.

Inderpal: which was a very early AI engineering thesis. And I did it [00:03:00] with a gentleman called Herb Simon, who was the Turing Award winner and the Nobel Prize winner at Carnegie Mellon, as well as Dan Zavorik, who was, really has won all the engineering awards you can think So, and what that thesis Was about exhibiting general intelligence as opposed to very specialized intelligence like the expert systems had in those days.

Inderpal: In, in the context of troubleshooting, and I applied it specifically to troubleshooting computer boards because computers have these different boards that, that have very different behavior the CPU board versus the memory board versus the input output board, very different behavior.

Inderpal: But I had the same program that was able to diagnose and troubleshoot. Any part of the computer, including all these different boards. And so that was the PhD thesis. And then IBM was interviewing at that time. This was my first go around with IBM at the Watson Research Center. So they were looking for [00:04:00] somebody on their research team.

Inderpal: As a research staff member, and they, my, my thesis caught their eye, because they liked the idea of what I had done in terms of troubleshooting these computer boards, which could also be done in process as opposed to when the computers actually shipped. And they decided that Perhaps I should look at the problem of software development, because they had so many different products.

Inderpal: And once once, once it was shipped, the product was shipped that the millions of customers, if they had lots of defects, it was very difficult. For them to recover. So they said he's applied it to hardware. Maybe the technique will move over to software. So let him do that. And when I started looking at software, the approach that I've taken for hardware actually was very simulated.

Inderpal: There was a simulator called Verilog which was used to design all these boards. And so I that computer program would [00:05:00] capture knowledge from the simulator. But in software, it didn't carry through. What did carry through, though, was all the data that was being produced as part of software production.

Inderpal: And the way the process worked, they would have the development team and they would have the quality control team. The quality control team would look at the data and say, you guys seem to be done. There were very few defects found this month. Maybe we should ship the product next month. And the team lead was saying no, that's because we were mostly all on vacation.

Inderpal: And that's, that's why. When I saw this kind of stuff, it made me realize that. The way the process worked currently, it was not very conducive to producing good software in the sense that defect free software, because you have this bifurcation of responsibility, and in terms of data analysis, it was all done by a different team who really didn't, they didn't have an idea of what was happening with [00:06:00] the actual production.

Inderpal: So I came up with this notion of let's have the team itself. Analyze their defect data and introduce that concept. Now, of course, the team, even though they were technical, they weren't statisticians. They didn't have that kind of knowledge. So we had to make it sufficiently user friendly and novice friendly with regard to data analysis.

Inderpal: for the team to be able to do it. At the same time, it had to be pretty powerful in the sense that it had to come up with these hidden and counterintuitive findings because otherwise, the team knew everything already and they would just think you were wasting their time. So that's how I backed into data mining.

Inderpal: And we began to apply it and then it was a very successful project. IBM used it in six different labs across all kinds of products and it, it worked very well. It actually helped the team tremendously in terms of being more proactive and shipping better

Manish Sood: products.[00:07:00] So Inderpal, just for context, this is back in the early 1990s.

Manish Sood: You're working on this project and applying it to software defects. So how? How do you jump from there all the way to applying it to scouting talent for the NBA?
Inderpal: That's the wonderful thing about the IBM Research Unit is once that project was over, they said, What do you want to do next?

Inderpal: And because, in a sense, I've proven myself as a researcher, they said, What do you want to do next? And I've been looking at the trend of the Internet really taking off and impacting people. And their jobs and their lives. And I realized that this, this insight that I had about the software developer finding data mining useful could actually apply to everybody just because data was going to become central to [00:08:00] everybody's jobs.

Inderpal: So I. And they posted that back as a hypothesis and said, Why don't I work on this? And they said, it's a great idea, but we deal with the big banks and they've got these old room full of analysts. This thing about gearing this for the lay user is not something that, we understand or even feel that is relevant to what we do, but you've done some great work and you've earned the right to prove us wrong.

Inderpal: So why don't you take a few months To figure out what this really means, and I have this habit of picking up at least one article, usually, on a monthly basis, which I don't know anything about, and I'll read it end to end. And I did that with the New York Times Educational Supplement uh, one day.

Inderpal: And there was this article right at the end about how the New York Knicks had hired a gentleman called Pat Riley to become their head coach, and he was creating a [00:09:00] database. And that's when the penny dropped, and it hit me that this could be a tremendous example. To demonstrate that yes, there is going to be applicability of these types of methods and approaches and it could completely change what we do.

Inderpal: So then I got hold of the guy who ran IBM sponsorships and said, Why don't you introduce me to the mix? And he thought I was, nuts, but he took a leap of faith and he decided to do it. And one of the assistants of Pat Riley actually I became enamored of the idea and the way I did that was I didn't have any data at that point.

Inderpal: But I had all the box scores that were reported in the newspapers, and the Mets had just finished losing the finals, I think it was for the Houston Rockets or something like that, and in seven games, so it was quite a [00:10:00] heartbreaker for me. And in I took all the box scores, and I ran it through, uh, my data mining program.

Inderpal: And then that was the precursor to advanced scout that you mentioned the and then that program actually found some counterintuitive things even there. So there was a guy called Charles Smith, who was viewed as a offensive player, but the program demonstrated that. The games that they won, he actually played a major defensive and things like that, that they hadn't appreciated.

Inderpal: So I came to the to this assistant coach, Bob Salmi, and showed him this stuff. And then he was all in. And he said, this is something that we really want to do. And Pat's creating this database. If we had this kind of capability, it would give us an edge. And then one of the other teams, the Orlando Magic, somehow found out about that, and then they wanted in, and then the NBA found out about it, and their head [00:11:00] of technology and media, Stephen Helmuth, who's actually a good friend now called me and said, Nola, you can't do this.

Inderpal: You can't just work with one team or another. It's going to be either all teams or no, no teams. Because they wanted to make sure that, it was a level playing field and didn't and so I said, okay, we'll, you sponsor the meeting and we'll work with all teams.

Inderpal: So he did that. And we had a meeting in Secaucus, New Jersey and then the press got hold of it. So there's a guy from the Wall Street Journal showed up and listened in on the presentations and then he wrote about it and then it took on a life of its own after that. But, all the teams ended up using it except for the team the Chicago Bulls, they had other weapons like Michael Jordan and Scotty Pippen.

Inderpal: So they, they were not what I would call avid users. But they were still, they used to use a lot of video, but they weren't that, but every other team just because of how dominant Chicago was.

Manish Sood: Yeah, in their desire to catch up with the bulls, everybody has to look [00:12:00] for other areas where they could get an edge.

Manish Sood: So that's one great way to take down hurdles and propagate the advantage of data across an ecosystem. But let's fast forward a little bit. And having played the role of global chief data officer at IBM that's quite a remarkable career that you have had.

Manish Sood: Um, how did you get into the chief data officer realm? What drove you there? What were some of the key highlights of your career as you work your way towards it?

Inderpal: What happened very quickly after this advanced scout program that we just talked about was IBM and I came to a parting of ways.

Inderpal: They wanted me to continue doing research. And by that time I realized I enjoyed the end to end process. Just not just the research, but creating the product, dealing with clients, et cetera. And so I, and that's the reference I made, [00:13:00] but, I should have just written the book instead I went off and started a company a data mining company and did practically everything that you could think of in a startup I did wrong almost everything, any, every mistake that you could imagine I made in that context.

Inderpal: And I continued doing it for 10 years because that's how long I was really active trying to do this stuff. So for 10 years, practically, but I did, I do think of it as getting my MBA on the street and in the street of hard knocks. And so it did give me also deep understanding because I was selling into the large enterprises.

Inderpal: And it gave me a very deep understanding that data should really be an asset and it should be treated as an asset by the enterprises because it's highly strategic for them and they weren't really the CEOs and the other executives didn't quite understand that. buT so [00:14:00] that stint with virtual gold, which was the company I started did give me that insight that there is this opportunity.

Inderpal: And when I managed to, get a modest exit out of Virtual Gold, I I began working as a consultant with some large enterprises, and there was a healthcare company called Medco Health, which I was consulting with, and their chief product officer said, why don't you come and join us?

Inderpal: And I said, What would you like have me do? He said, Why don't you define the job? So I took that opportunity to define this role of chief data officer. I was the first in healthcare, really. There were only four of us worldwide at that time with that title or any, anything related title. And then, of course, the profession took off.

Inderpal: And I was fortunate to be there at the right time and actually grow with it. And I ended up then creating this chief data office for four different companies. The last being IBM, which [00:15:00] obviously was the most complicated job as well, and it took the longest, but I was there for eight years in that second stint.

Inderpal: It was a tremendous learning experience. It also. allowed me to to be part of growing that community of chief data officers, chief analytics officers, chief digital officers. And so it was really quite an exciting journey to see that both on the community front, but also on the enterprise front and be at the forefront of all that.

Inderpal: iN terms of accomplishments, what's been the, probably the most satisfying accomplishment is that many of the people. Because most of the, all the time in these four companies, I actually created the office in the sense that the company didn't really have anything there. So I would start with essentially a team of one or two people, and then it would grow, and the largest [00:16:00] team that I had was about a thousand people, starting from, so there was tremendous experience in actually growing the organization, navigating through everything that entailed, And then leaving the company with that legacy, but the most satisfying piece of all that was now that I look back and look at all the people that I hired, many of them in their own right.

Inderpal: Our chief data officers, chief technology officer, the second. So that's probably been the most satisfying piece of it all.

Manish Sood: So in Nepal, having been there from the very inception of this idea of a chief data officer all the way to the success that you had at IBM um, building out that capability.
Manish Sood: What, in your opinion, has been the evolution of the role of a chief data officer through this?

Inderpal: Yeah, I think so. Initially, it was all about the understanding, including my own, [00:17:00] uh, was it was all about delivering capability and organizational construct and organizational capability. And by the time that I got to IBM, I realized that really, and it's not that was wrong.

Inderpal: That was the right thing to do. And we were doing it in the previous companies. But. By the time I got to IBM, I think maybe also I had it down to a craft by then. I began to understand that it was about totally changing the culture of the company, not just delivering the capability or changing the culture within your area, which is you would do that anyway.

Inderpal: And I did that on all the last three jobs, but actually changing the culture of the entire company to become much more data driven. And so I think that was the, in my mind, the biggest progression, in terms of what, what had to be delivered, just understanding that the [00:18:00] outcome that made the most sense.

Inderpal: for the company because you would get tremendous outcomes even delivering the organizational capability and, from a new product differentiation, market share capture, efficiency, all that stuff was great. You would do that. But in terms of actually changing the culture of the company, then you left a legacy that was much deeper.

Inderpal: It went far beyond your own organization, all the accomplishments that your organization had. It was just, you completely changed the way the company operated and would operate for many years to come. iN some sense, by the time you reach that stage, the chief data office itself is redundant because you've you suffused the entire company with that philosophy.

Manish Sood: Yeah, so bringing that ethos into play where it becomes a natural part of the day to day fabric is a long lasting legacy to leave behind in those [00:19:00] organizations. So when you think about your current role as an independent director at the Walgreens Boot Alliance board what are you bringing in from your experience of having worked with data shape the business in a certain manner?

Manish Sood: How are you applying those skills and principles to your new role as a board director?
Inderpal: Yeah, it was, again, very interesting journey because previously, when I became CDO, the CEOs had just started, they were just dimly aware of the fact that there was something strategic about data and creating this chief data officer role.

Inderpal: It allowed us to crystallize that much more and, enabled the CDO. The biggest difference between the CDO and the data [00:20:00] leader of your of the past is that today's CDO can actually talk to the C suite, right? They know how to engage and talk to the C suite in terms that the C suite will actually Respect and listen to you.

Inderpal: Previously, that wasn't the case at all. So that's a different skill set, right? So I think in today's world, people, CDOs have that and they have to develop that, etc. And so that allowed the CDO role to essentially educate the CEOs as to the strategic nature of data. And I think what's then, a natural progression of that, as people began at those levels, at the C suite levels, as they began to understand that this is, data is extremely important, it started receiving more attention from the board.

Inderpal: And mainly because the large projects, the big ticket items that were rolling up. For approval, [00:21:00] and that all centered around the data, making those changes, coming up with a big data architecture, providing those capabilities to the organization, becoming data driven, etc. And I think that the companies are beginning to understand that.

Inderpal: You probably need somebody permanent at the board level, who is a technologist, who naturally just intuitively understands those issues, as opposed to somebody who would, may understand the business issues, but then needs a consultant to actually go in on and figure out what's whether projects are, being governed appropriately, they should be allocated around, etc.

Inderpal: So that awareness, I think, is what you're seeing now. And I think I was brought in because of that. Because once you, once a board arrives at that awareness. They have just a couple of choices. They've got to either get [00:22:00] somebody on the board who's already there, schooled very rapidly to deal with these types of issues, or they bring somebody in somebody new.
Inderpal: And and I think you're just seeing the, uh, the beginnings of that play out. It's in its infancy, I would say, but I think it is it is again, one of those movements that's going to snowball just because of the outsized Influence that data has. And now with with AI entering the picture in a big way, it's going to drive that, that you're going to need a technologist on the board who is able to govern through those types of issues and help the boards.

Inderpal: It's going to be challenging again because boards today are not really set up for that. They might have. Technology component bundled in with some other committee, and usually those things are not not really, they haven't really, all that stuff hasn't quite been thought through. So we are at the beginnings of that, the forefront of [00:23:00] all that, and we'll see where it goes.

Inderpal: It's an exciting development, also an exciting time, also an exciting career progression for a lot of people like us. So it's just it's the way of just playing out, but now at the border of,

Manish Sood: yes, you're absolutely right that this is a really interesting point in time, especially for people in data, who have been working their entire career and data are focused on building their career in data.

Manish Sood: And, when I listen to you talk about how not just the evolution of your career has been, but how at the board level now you're contributing, bringing in that perspective it points to me shift in two areas. Culture and talent. And when I say shift, you either have to be in the driver's seat to drive that ship with [00:24:00] the right culture and the right talent that understands data, uses data as a strategic asset or you'll be caught in that snowball where you'll be driven by it no matter what.

Manish Sood: How are you approaching it especially with the involvement at the board level and how would you guide other CDOs to think about it?

Inderpal: Yeah, so I think two questions there. So I'll take them take them and maybe the second one first. But the other, the CDO, I think that The first observation I'd make is the don't downplay the aspect of being able to communicate at the business level, right?

Inderpal: Because that's so critical that you have to be able to talk business. It can't just be a technical conversation. You will lose the CEO and the C suite very quickly than that. So you have to be able To communicate [00:25:00] effectively at the C suite level, and all they're really interested in talking about is business.

Inderpal: I can tell you that they're not interested in anything else. Eventually, if the boat is good, if the C suite is good, they're hardcore business. They want to understand, what are the business outcomes that are going to be impacted? What are the processes that are going to be changed etc.

Inderpal: They couldn't care less about the tech. And I think a lot of the A lot of us, come in tech first, but you have to pay that price, you have to understand the business, you have to be able to do the necessary due diligence and learn about it, put in the time to develop the relationships with people who actually teach you about the business, etc.

Inderpal: And then you're able to have the conversation much, much more effectively. So I think that's the key. The second question that you had, or the first question that you had in terms of how am I thinking about it at the board level, after the IBM stint and having [00:26:00] recognized that it's far more about culture in the company than it is about individual outcomes, even though the individual outcomes can be outside.

Inderpal: And in my history, they have been literally in the billions of dollars. But, those are also yeah. You might get one hit like that. And then if you leave the job, it's all right. Somebody else comes in. But if you change the culture, it's a very different company. And it just leaves a lasting impact on the company.

Inderpal: And I think at the board level, now that I have that insight, it also makes it easier for me to Then talk with the CEO, like a thought partner as to whether they have the right culture at play in the company for data and ai, and if they have the right talent, then all the other questions then flow from that.

Inderpal: And it also [00:27:00] I think, makes the c-suite understand. That there is more to it than just the hard, business numbers, business outcomes and the tech that you really have to make that investment in people, especially if you want AI to pan out to be able to pull that off. And I think that's, very appropriate on the board level. It's the right conversation to have at the board level. And it's a good conversation. It also gives you a very quick read on the preparedness of the c-suite itself. You know where they are. There

Manish Sood: is education at all levels. At all levels, yes. So in Nepal, it's very important for data professionals to understand how the business operates, what are the different mechanics, as well as the KPIs that they should understand and embrace, because then they'll be able to talk the same language.

Manish Sood: As the business people [00:28:00] that they're trying to work with or help um, in a similar
manner the business also has to understand how they can leverage data to their benefit. So how do you suggest that, this type of cross pollination of knowledge and understanding on both sides of the fence should be driven?

Manish Sood: How did you make it possible in your past experience? Yeah

Inderpal: I'll give you a couple of points on that. I think the it's a very subtle point that you raised, which is you do have to bring the business side along as well. And to appreciate the realities of what needs to happen on the data side.

Inderpal: Yeah,

Manish Sood: just because you have a great system doesn't mean that business is going to sign up for it.

Inderpal: Yes, exactly right. Even if you talk the right, presented the right way and all that. They'll give you the benefit of the doubt, but it would be great if they truly became thought partners in the process, right?

Inderpal: So then, and [00:29:00] it works a couple of ways. The CEO is going to look at the CDO. as the catalyst. So they're going to think, okay, this is really, it's going to be your job to bring them along and make them understand what you're trying to, what you're trying to do, which is probably the single most reason why you see the rapid turnover with the CDOs and so forth, just because that is such a tough requirement when you just see the CDO as the catalyst.

Inderpal: So you're almost saying that The business really has no responsibility in this. It's it's your job. You've got to get that done. You have to, as a CDO, make it everybody's job. And I think the couple of insights I'll give you there is to some extent, as a CDO, you come in, you look at things, it's natural to look at things more parochially and say this is my area.

Inderpal: I own this system. It's my data. You guys have to listen [00:30:00] to it. There's a counter a counter view, which I often feel when I'm sitting at the C suite level of the executive committee, something like that, where I'll suddenly have this feeling that I really don't own anything here.
Inderpal: I don't run the business. The IT guys are running the systems. So it's actually, both those viewpoints are true, and it's instructive for the CDO to appreciate the second viewpoint that you really are in a position where you really, in some sense, don't own anything. And I think once you appreciate that, then you realize that it's all about collaboration, and you've got to put aside that parochial feeling.

Inderpal: And really you're better served by figuring out or adopting this stance that I really don't own anything. I've got to convince everybody to collaborate with me to be successful. [00:31:00] And I think that, that kind of helps you move the ball forward tremendously in this in this aspect.

Manish Sood: As you think about the CDO responsibility and especially how it will be shaped in the coming years. In the past, I would have said in the next 10 years, what would be your take? But as we have all seen, nobody anymore has a luxury of 10 years to think about because every cycle is getting shorter.

Manish Sood: So let's have that time to five years in the next five years. What are some of the critical skills? that CDOs must have?

Inderpal: I think for one CDOs have to embrace AI. They really, it's the natural progression. I really don't think that companies need to bring on chief AI officers.

Inderpal: It's really should be the CDO role that fix that up. And I did that. Within IBM, the [00:32:00] internal AI was all me. And that's, it's just the natural way to do things. Otherwise you're just going to create some tension. But see, the CDOs have to embrace it, right? They have to be able to then deliver on that.

Inderpal: And I think the other reason, which is where I'll just differ a little bit with you, because I think While the tech cycles are moving very rapidly, I think that the issues with AI and to some extent, yes, we've got LLM's now and we've got foundational models. Now that's all that stuff has taken off.

Inderpal: But you did have AI with tremendously impressive results even 10 years ago. And but you just couldn't make it work, right? You had the IBM's AI program beating the Jeopardy champion, et cetera. But it's very hard to get this stuff into market. And the main reason for that is people don't trust AI.

Inderpal: The executives don't trust it. They think they could be disinterested. Their company could [00:33:00] be disinterested. The people who are working the business processes don't trust it because they think they could be, their jobs could be replaced. Also, many times they don't understand. It's going back to that NBA example.

Inderpal: The first time we found a really counterintuitive pattern, it, essentially asked, suggested to the coach, the Orlando Magic coach actually, that he needed to start two of his backup players. And he was down 2 0 and he said, if I do this and I lose, I'm going to also lose my job, not only will I lose the series, but, and I'll be the laughing stock.

Inderpal: I'll probably never get, never recover from that. So you've got to be able to explain why these things make sense. And many times, that's just not done with these more black box types of approaches. I personally don't see much headway being made yet. On all those aspects. So I do think that when it comes to scaling AI within a [00:34:00] large enterprise.

Inderpal: aLl those wins are going to be still very hard, regardless of the fact that you've got these new technologies. I think where the new technologies are going to help is they are going to help prepare the culture. So people are a bit more, they are more ready to adopt them. But I, I myself, I Look at all these techniques, and I find myself trying to reinvent practically everything that I do using, and so I do think that there's tremendous potential, but I think at an enterprise level to scale it much of that hard one engineering work that was being done with data to make sure the data is high quality, you can trust it and so forth, has to extend over to the AI models as well.

Inderpal: And then the two going together, you'll be able to make some headway at scale. They're going to be individual projects that will succeed, no question. [00:35:00] But in terms of actually scaling it pervasively across the enterprise, I think it's going to take a lot of hard work. I think the CDOs are well positioned to ride that one, perhaps better than anyone else.

Inderpal: And they

Manish Sood: take full advantage

Inderpal: of it. Yes, I think they should.

Manish Sood: Yeah. So it seems that with the AI ML type of innovation that is taking place one of the things that becomes available to all of us is the new set of tools and capabilities. But how we apply it to different business problems how do we take it to the last mile off the stretch and deliver outcomes still has a lot of work to be done.

Manish Sood: And that precision or that level of engineering. Is no different from what we had to do
Inderpal: before. Yes. Yes. No, I totally believe that. I think these issues about explainability, transparency, privacy, these as [00:36:00] fairness, these aspects are, they all have to be worked through. And it's not going to be easy.

Inderpal: Those are very difficult problems to actually work through if you're trying to scale stuff. And I think in enterprises, as opposed to consumers, right? In the case of consumers, there's a lot of just people just accept things out of convenience and also just out of frustration because it's too hard for them to actually fight it.

Inderpal: But at an enterprise level, it's different. I think there's just going to be a lot more scrutiny in that. And that hasn't changed that much. In my mind, that's also a huge opportunity.

Manish Sood: Yeah. And you're absolutely right. With the consumers, not only are consumers more willing to accept some of the things that may come up as new inventions, but at the same time if it doesn't deliver to what they need, they also move away from it at an equal equally fast pace while the enterprises need more durable [00:37:00] solutions that they can put to use on an ongoing basis.

Manish Sood: With that in mind, Inderpal we are coming up to time. So before we sign off, would love to, ask you a question about predictions and really thinking about three things that are top of mind for you when you think about this combination of AI and data and the future ahead.

Inderpal: mY, and again, this is just my view on it.

Inderpal: People might view it differently. I think that AI's tremendous potential as a technology. I also think that it's so fundamental. That it's one where it can't just accrue to the value of business. It actually has to be valuable for society for it to truly take hold and to truly deliver.

Inderpal: And I think that will also remove a lot of the fear [00:38:00] factor that today. So I think that's one of the things that's top of mind for me and I think should be top of mind for everyone. And my sense on that in terms of how you address that is really through educating people so that you really bring them along, not just in terms of being able to work with AI, but also being able to understand the implications of what it means.

Inderpal: What it can do and not do, etc. And so it's almost when Ben Franklin created all these libraries. Because he said, knowledge is, it's just. Too valuable to be left in the hands of a few or something like that. I'm mangling that phrase, but you get the point. I think it's the same with AI.
Inderpal: I think it's just it's something that has so much potential that it's too valuable to be left in the hands of a few. And I think the more we can get the just the everyday person in the community is involved in that that's so that's one momentum that I think does need to be pushed and [00:39:00] established.

Inderpal: I think in terms of data professionals, the biggest takeaway I've got is, embrace AI. It actually is something that you're really well prepared to do, and you just have to go the extra mile to embrace it. Pick it up and then deliver with it. And with regard to enterprises, I, I think the top of mind aspect there is the trustworthiness of it.

Inderpal: And I don't just don't mean the usual, marketing the tautology that comes out of that. Actually substantial progress in terms of delivering a trustworthy app, I think is the key aspect that needs to happen.

Manish Sood: So that we can deliver meaningful impact both to society as well as to what we are trying to do in order to drive our business.

Inderpal: Yes, I think otherwise it won't scale. I think the promise will be lost. It'll be the hype cycle. Maybe there'll be more damage than promise. I don't know. That's what I think. Because those who are, involved in [00:40:00] activities that are not so trustworthy, they won't care.

Inderpal: They're going to use it anyway. So that's why I think that we do have to push that aspect of trustworthiness. Trustworthiness

Manish Sood: and responsibility associated with it. Inderpal, thank you so much for your time and for sharing your thoughts with us. I really enjoyed the conversation. And once again thank you so

Inderpal: much.

Inderpal: Always a pleasure, Manish. Thank you for having me.

Chris: Manish and Inderpal, thank you so much. What a great conversation. So thanks for everyone for tuning in to listening to the Data Driven Podcast. I'm Chris Detzel. Please rate and review us. And thank you guys.