Inderpol Bhandari, Global Chief Data Officer at IBM

Speaker A: Foreign m.

Speaker B: Welcome to the leading analytics podcast where we dig inside the minds of analytics leaders from some of the world's most recognized organizations. I'm your host, Jack Phillips. Today I'm joined by one of the biggest voices in data and an old friend of IIAs, Inderpal Bundari. Inderpal is here to talk about his time as the global Chief Data Officer at IBM, coming to us today from the company headquarters in Armonk, New York. Like many of the executives I speak with on the leading analytics podcast, Interpol has been deeply rooted in the data analytics and technology world for much of his career. He's been in this global CDO role at IBM now for about six and a half years, but before that held senior roles in the healthcare industry, first at Express Scripts and then later at Cambia Health Solutions here in lovely Portland, Oregon where IA is, uh, headquartered. The IBM name really needs no introduction to our listeners. Such a storied history of innovation and invention. Uh, they were the creators of the automatic teller machine, the atm, the floppy disk, the hard disk drive, uh, the magnetic stripe card, SQL programming language, UPC barcode, and of course the IBM mainframe, which dominated computing in the 60s and the 70s. But more recently under CEO Arvind Krishna, a well chronicled turnaround and pivot with a discrete focus on cloud, AI and advanced analytics, which is the area Inderpal spends much of his time in today. Interpal, welcome to the podcast.

Speaker A: My pleasure, Jack. It's good to hear from you.

Speaker B: It's good to talk to you again as well. Uh, so tell us just quickly about the role that you have at IBM, the scope, uh, the exact title. I think I got it right.

Speaker A: Yes, I'm the Global Chief Data Officer at IBM. It's my fourth go around as Chief Data Officer and by far the most complicated. Uh, just because IBM is a large enterprise, global enterprise, lots of business units and uh, they wanted to create a data pillar essentially to drive the digital transformation journey that IBM was on. That felt to me to create that data, uh, organization, uh, for IBM. Now obviously they had a lot of data work, uh, that was going on, but they did not have a C suite role as in the chief Data Officer and an organization that reported high up into the C suite. Uh, essentially that was my role, having done the job. When I joined IBM, it was my fourth go round. Uh, I knew exactly what needed to be done, uh, in a nutshell, what the role ended up being because whenever I go into these roles to create the data organization, the end result is very different from how it was originally envisioned. And IBM was no different. But, uh, where it ended up was we were, like you mentioned, uh, about six years ago, just on the cusp of a new strategy, which was IBM was going to be a cloud and AI company. But, uh, from an enterprise standpoint, it wasn't clear what AI meant. People understood AI from a consumer standpoint thanks to the likes of Google and Amazon, etc. People had experienced it. So they intuitively understood AI in the consumer context, but they did not in the enterprise context. And uh, one of the first discussions we got into with regard to the data role and what its focus ought to be was, well, I mean, are we going to be marketing to consumers now? And the answer was no, we are very much a B2B business and that's what we're going to remain. And so that raised the next question, well, what is AI for an enterprise? If you are going to be a cloud and AI company, you have to know that. And so, long story short, our data strategy became to create an AI enterprise within IBM itself because we look a lot like our major customers and then use that as a showcase for our clients and customers. That's been the role over the last six, six and a half years that I've been here. Uh, in a nutshell, that's what I do. I spend my time about maybe 50, 50 internal operations infusing AI into our business processes and uh, the other 50% using what we've done internally to showcase, uh, that work to our clients and customers. So we act as the voice of experience when we talk with the clients and customers so that they can avoid the same pitfalls that, you know, that we actually went through to put it all in place.

Speaker B: And I suspect that puts a fair amount of pressure on you if you're showcasing your internal capabilities externally to customers, uh, all the time. You, you know, we'll talk about maturity here in a minute, but uh, that must be some pressure to perform at a pretty high level, uh, to kind of walk the talk. Is that right?

Speaker A: Yes, no. And you're absolutely right about that. And just to give you some perspective on that, when I joined we had uh, there was a Chief Data Officer summit that we would do and we had maybe about uh, 20 or so uh, data officers that uh, were part of that community. And uh, now it's well over 1000. We've also broadened over to the chief Technology officers as well, as we realize that to infuse AI into an enterprise it really has to be a Holistic transformation and involves technology, technology and data and IT and business process and so forth. So we broadened our scope and we have well over 1,000 CDOs now. And we have usually about over 100 of them, uh, attending any event that we have. So it's grown tremendously. And obviously with that comes pressure. Uh, as you alluded to, what I've found works really well to manage that pressure is, uh, just to operate with the utmost, uh, transparency. You know, we are there to share our experience and uh, what we've gone through. We've also, uh, we know we've been at the cutting edge of that. And so there have been lots of hiccups, uh, lots of, um, you know, pain points, lots of failures that we've learned from. And, uh, by coming to the, to the table with that kind of mindset to help our clients and customers avoid those situations, as well as understand what the potential is and the outcomes that we are experiencing. You know, that playbook takes away some of that pressure because, uh, right up front, uh, you know, that's how I present it. And by sticking to that transparency, we've been able to be quite successful at what we've been doing.

Speaker B: Well, it's interesting you mentioned 1,000 CDOs in your network, um, over the past 12 years. Certainly we at IIA, Tom Davenport myself, have been following the emergence of the cao, the cdo, the cdao and other flavors that drive off of those. Uh, obviously you are a cdo. Uh, have we arrived? Are we still at the early innings in terms of awareness among enterprises of the importance of data, or are we starting to get into the middle innings of this journey?

Speaker A: So it's a very good question, Jack. Uh, and when I say over 1,000 CDOs, I actually meant the transformation roles, which includes the CDO, CAOs, CTOs, uh, and even some Chief Digital Officers are part of that community. But they're all about digital transformation. And yes, the bulk of them are CDOs, but, uh, we also have quite a few CEOs and CTOs, uh, in the mix, uh, in that community. But to get to your question,

Speaker B: when

Speaker A: I started out as chief, ah, data officer, that was 2006, um, there were only four of us globally with that title. I think the first, uh, cdo, and you probably know this better than I do, I think the first one was 2002. Uh, anyway, 2006, I know there were only four of us globally. Then the profession just took off and now there literally are thousands of people, uh, doing that job. But to answer your question as to has the role arrived, what is the awareness level and so forth, here's the way I think about it. If you go back Even beyond uh, 2006 or even 2002, there were people doing data roles. We had people at IBM who were doing data management roles, data roles and so forth. But I think what happened was that those roles, and they were doing some incredible work, but they were usually buried somewhere in the CIO's organization. And so the C suite aspect of that was missing, the strategic aspect was missing. And so when the role actually got uh, elevated to the title of um, Chief Data Officer or Chief Analytics Officer, that strategic component, uh, came to bear in a big way with the role. You had to be able to operate at the boardroom level, uh, to pull it off successfully. Otherwise you were back to doing the more tactical operational stuff. Again, very good work. But perhaps the most significant component of the role, uh, since uh, the time that these positions began to be appointed, uh, has been the strategic element. The awareness that this is a strategic asset for the company and we need somebody to steward it. That's how I think the role has evolved. Where is it now? Uh, I think there's an incredible amount of awareness of the strategic importance of data and also the need for leadership to develop that to its potential. Uh, I think I've, uh, also seen several articles that uh, uh, I think Davenport's actually authored some of those where uh, the short shelf life of CDOs has been expounded upon that generally speaking, these jobs, uh, people are done in about uh, a year and a half or so. I think there's still a ways to go. But certainly in terms of the awareness of the strategic importance of data and the role and the need for somebody to be there, I think that's been established.

Speaker B: Yeah, makes perfect sense. And that leads me then, you know, we're squarely focused here on quant, on defining this new leader. Uh, Tom used to talk in the early days about analytical leadership. That is if you are a head of marketing or a head of sales, uh, doing your role, performing your role in an analytical fashion. And we've identified, now we need to be talking about this new leader of data and analytics. And you've been doing this long time, Obviously you have a big network. What are the four or five most important qualities that you think this new emerging or, uh, whether we think it's emerging or here to stay. But, but what are the four or five qualities, attributes that you think a leader in this world in this area

Speaker A: needs to possess, I think, and I do think it's definitely here to stay. I think it's an established role by now. I think it's established at the C Suite. I do think that um, it's going to get um, elevated even more than it is right now. So I do think it's here to stay. But with regard to what are the attributes that help someone along in being a good CDO or a CAO or cto, I think the first one, and I alluded to this earlier, has uh, to be the ability to think strategically as well as articulate a strategic position in a very clear, compelling fashion so that you're able to operate at the C Suite level. You. I think that's the biggest uh, requirement, uh, uh, otherwise it kind of goes back to the history of the role where you could be uh, uh, in some back room on the IT shop. So I think that strategic uh, acumen, that strategic ability is critical, uh, and both in terms of not just the thinking but also in terms of the articulation. Because at the boardroom level it's a very collaborative effort. It's like there's lots of overlap in the roles and you have to be able to get along with uh, your peers there and in fact you all have to work together. So the ability to articulate that I think is also very, very critical. Now after that, I think it kind of depends on where you end up reporting. So let me explain that. So in my case, you know, I ended, I ended up reporting an IBM to our chief Financial officer who also doubles up as the senior Vice President of Operations. So we don't have a CEO, all that stuff reports into the cfo and uh, he's the head of our digital transformation for IBM. So it's a transformation role. Right. So when I talk to my boss uh, about my role, he uses two words to describe it, which is basically change agent. He thinks of me as change agent in Chief. And so that aspect of change, of being able to, of being a change agent is critical if you're in a digital transformation role. So I would kind of put that, uh, put that next. If on the other hand, if you were in a ah, specific uh, area that you know, business area, then they might value a different way to um, approach it. But in terms of um, the digital transformation, in terms of creating uh, a new organization within the company that focuses on data, focuses on analytics, focuses on AI, all with the mindset of creating uh, a business advantage, uh, the change agent piece I think rises to the front and center. So I would have to put that down as um, the second most important uh, aspect of what we do, um, building on that as a leader, data leader, analytics leader, AI leader, but again not theoretical. You're actually making changes in the business processes, you're impacting the business. The third thing that I think is um, that it kind of goes hand in hand with the uh, with the change management aspect, the change agent aspect. You can't just be a change agent or one, your whole organization has to act as a change agent. And when you're leading your data or analytics organization, they all have to be thinking the same way. So you've got change agents all the way down to the front lines. So there has to be that culture within the organization, within the data organization, uh, to be able to pull that off. And in my experience, what I have finally settled on for a data organization, it has to be about empowerment of employees. So they really feel empowered to get to a data first, data driven kind of culture. And if you just think about that, if you're in a data first culture or data driven culture and you have to go up for permission, then you're not in a data driven culture because you should be able to look at the data and then make the actions that are necessary. So I think the employee empowerment, employee engagement piece of it is also extremely important for the leader. Then I think there are four areas that need to kind of move in lockstep for uh, these roles to be successful. There's the technology aspect of it, because if you're dealing with uh, uh, AI for instance, you've got to know and understand technology well. You don't necessarily have to be hands on, but you have to know that if you're going to be running tens of thousands of uh, deep learning experiments, then you need to have very fast interconnects between specialized hardware called GPUs and your CPUs. Ditto for the cloud aspects of things. You have to understand the technology, the data you have to know also because the readiness in the data, is it fit for purpose, the quality, all that stuff you've got to know very well then. I alluded to this earlier. To impact the business, all this has to be infused into a business process. And that's what we learned. And AI enterprises, you have to infuse AI into a business process like a supply chain, like procurement, all the stuff that enterprises uh, do. And so there again you have to have a view of innovation where it's not just about the algorithms or what you're implementing. But how do you actually change the way people work to advantage? Right. So that business process aspect, the innovation aspect comes in there and finally culture, which I alluded to. Right. So that mix, having an understanding of that mix. So it's a combination of the hard skills and the soft skills that makes all that up. I would have to put that up there as well. That, that needs to be something that uh, the leader uh, has to understand and uh, be able to pull off. So I think that those, those four are probably what I'd put up. Yeah.

Speaker B: Okay, talk to me a little bit about how you define success, how you think about performance. And you know, at ia we spend a lot of time thinking about maturity levels for enterprises. But how do you define success both for the team you oversee, but I think probably more importantly for IBM as an enterprise when it comes to the work that you do?

Speaker A: Yeah, no, it's an excellent, excellent question. And uh, you know, in my mind it has to be business outcomes driving your success. So again, after doing this job four times, what I've arrived at is um, at the beginning of every year we put together a scorecard that uh, uh, I agree upon with my boss. Uh, the scorecard will typically have three elements for the kinds of jobs that we are talking about. There'll be essentially some aspects about impacting the top line, revenue growth, those types of things. And it could be like sales enablement or finding insights, uh, in competitive situations and things like that. Um, but it's all about driving revenue. There'll be items that go to the bottom line which is improving your operations. The stuff we were talking about about uh, infusing AI into business processes. So that ends up with things like reducing end to end cycle time by X percent and then that leads to savings and efficiencies, uh, and so forth. Right. So that's the bottom line piece. And then the third piece is the risk reduction piece. Just because if you're dealing with data, there is uh, risk associated with that. There's risk associated uh, with the fact that data can be sensitive in the sense that there's potential to invade people's privacy. And that's not just the data, but also the kinds of analytics and the discoveries you do with that data. Sometimes you could start with data that doesn't look sensitive and end up with something that's very sensitive. Uh, it's the entire process, uh, that there's risk associated with that. There's the aspects of cybersecurity where again there are actors even at the state level that would love to get hold of uh, data either for competitive reasons or other reasons, geopolitical reasons. That aspect is all under risk reduction now, depending on what the job is and the strategy that the company is following. I have seen one or the other emphasized more uh, in any given year. So in six years I've been at IBM, I've actually seen that, uh, seen that shift three times myself in terms of where we ended up. But I think it's extremely important to uh, have that uh, thought through, agreed upon with your leadership so that you're able to then march towards those outcomes as to what they are. And what I do is I review that every month, uh, with my boss, just to show him exactly where we are in uh, the progress and you know, the outcomes that we had. We are tracking the milestones that we promised at the beginning of the year and uh, where we are at any given point of time. There's several reasons for that. I mean one is, you know, obviously if you're doing well that's great, but if you're not doing well, you get uh, the correction. Not just the correction, but you also start involving uh, your leadership in that process and helping to solve the issues. And uh, very often things are quite easily solved if you can just bridge across to some peers or uh, my boss can bridge across to his peer and then uh, they can sort things out. So that's the way we've handled it, which is essentially business outcome, um, milestones agreed upon and then continuously tracked and reported on on a monthly basis, at least in my case, to arrive at, ah, you know, the final, the final state.

Speaker B: Yeah, I think that's a great collection. Top line, bottom line, risk. Uh, and it sounds like you're tracking it on a monthly basis. Uh, so that's, that's really helpful for our listeners to hear in terms of uh, you know, everything focused on business outcomes. Um, that's it. On the one breath it seems obvious, uh, but it's crucial. Obviously sort of relevance and impact across those three areas are important. Um, Indrapal, in the last couple of minutes here we have a lot of listeners who are entering the field. Uh, they might be recent graduates, they might be career shift shifters. Uh, as I say, you've been doing this a while. Any advice for the aspiring data and analytics professional? Any people that have inspired you or books or frameworks that you've used?

Speaker A: Yeah, so this is a very fast moving field. So I think uh, um, just understanding that uh, is really helpful in navigating those waters and it's fast moving along those four dimensions that I mentioned, the technology data, uh, business process, innovation and culture and organization change. You should have a pretty wide reading list. That's uh, one thing that I would uh, recommend to everyone. If you focus in too much on one uh, or the other, uh, then in a sense you're probably better off doing a job that's more specialized, uh, as opposed to um, the job at the CDO level or the CAO level. You kind of have to keep all those things in sync and by extension there are always people who will know more about specific aspects of those four areas that one can learn from. And those people change just because things change. So I tend to keep track of um, who those folks are in those four areas, uh, and I try to cultivate them. I think the other thing that I found to be also extremely helpful is to be very aware of what's happening in the startup space, uh, with regard to data analytics, AI, cybersecurity, now emerging areas like Quantum. You'll be surprised at how talented some of those people are. Now we know that a large number of those companies are going to fail, but it's not so much, I'm not coming at it so much from the standpoint of uh, acquiring those startups or things like that. I'm coming at it more from the standpoint of learning, just learning yourself by understanding what's happening in that space. I found that to be extremely useful. It's a counterintuitive tip because uh, in most cases the startups and those smaller companies will be screened by people who are uh, playing different roles, other roles seldom at the C suite level. But I tend to find a lot of uh, learning by talking to those folks.

Speaker B: Makes sense. Well Inderpart, you've covered a lot of ground here. Uh, what stood out for me certainly was, I think you called it change agent in chief when I asked you about linear qualities that uh, nobody's said it that way and I think that's a great summary of this role by its name it sounds technical but in fact uh, that idea of changing and bending the organization somewhat from non data driven approaches to data driven I think summarizes it well. And boy I like the four part sound bite of technology data, business process, innovation and culture listeners. That's a nice little package to think about in terms of how you spend your time if you will, if you aspire uh, to be a leader. So uh, you know that boy, that stood out for me, uh, Indra Paul, because it sounds like with having done this as Long as you have now and really a good run at, IBM, this is not only obviously is it core to the company's strategy so you know, you're in it, you're in an area obviously that, that is important to the company. But as you said at the very beginning, you spend half your time building, nurturing, empowering the team and then half of your time uh, making sure that you're outward facing into one of IBM's obviously main pillars. So did I, did I kind of get that summary, right, of our conversation?

Speaker A: Yes, yes, absolutely Jack. I mean what I would uh, also add is on that change agent in chief, it's worth dwelling on a couple of things there that may have not come across. You know, it's hard to change organizations. So this, by being a change agent in chief, you are going to fail many, many, many times. You know, there have been a lot of failures in the six years that I've been at IBM or even in the previous jobs. Lots of false starts, lots of, you know, but then, so the resilience, the ability to continue to push, uh, is extremely important because it is like pushing a rock uphill. But the point I'll make is the moment you get to the peak, the downhill is much, much easier for the same reason as ah, the uphill was very hard. Right. It's the inertia of the organization that kind of flips over to your favor. But I think people underestimate that piece and can get discouraged. And I would say that that just goes, goes with being the change agent.

Speaker B: Yeah, so sort of a tipping point. And you know, we've heard from, from other, other executives over the first uh, 25 episodes we a word, the word patience and stamina often come to mind in terms of quality to get through that tipping point, to get through that point where the rock starts to gain its own momentum as opposed to just relying on your moment. Well, interesting. Interpol. Hey, thanks so much for taking the time today. There's a lot we didn't cover, so I'd love to come back to you in a later season and touch on some more, particularly some more technology topics because I know our listeners are hungry for it. So thanks very much for joining us today.

Speaker A: Happy to be here, Jack, and m. Happy to come back.