 From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. Hi everybody, welcome to this special digital presentation where we're covering the topic of data ops and specifically how IBM is really operationalizing and automating the data pipeline with data ops. And with me is Inderpal Bandari who is the global chief data officer at IBM. Inderpal is always great to see you. Thanks for coming on. My pleasure to answer your question. So, you know, the standard throwaway question from guys like me is, you know, what keeps the chief data officer up at night? Well, I know it's keeping you up at night. It's COVID-19. How are you doing? I think it's keeping all of us. Yeah, for sure. So how are you guys making out? As a leader, I'm interested in, you know, how you have responded with whether it's, you know, communications, obviously you're doing much more stuff, you know, remotely, you're not on airplanes, certainly like you used to be. But what was your first move when you actually realized this was going to require a shift? Well, I think one of the first things that I did was to test the ability of my organization to work remotely. This was well before the recommendations came in from the government. But just so that we wanted to, you know, to be sure that this is something that we could pull off if there were extreme circumstances where even everybody was good. And so that was one of the first things we did. Along with that, I think another major activity that we embarked on is even that we had created this central data and AI platform for IBM using our hybrid multi-cloud approach. How could that be adapted very, very quickly to help with the COVID situation? Well, those were the two big items that my team embarked on very quickly. And again, like I said, this was well before there was any recommendations from the government or even internally with an IBM, we didn't have any recommendations, but we decided that we wanted to run ahead and make sure that we were ready to operate in that fashion. And I believe a lot of my colleagues did the same. You know, there's a conversation going on right now just around productivity hits that people may be taking because they really weren't prepared. It sounds like you're pretty comfortable with the productivity impact that you're achieving. Oh, I'm totally comfortable with the productivity. In fact, I will tell you that while we've gone down this path we've realized that in some cases the productivity is actually going to be better when people are working from home and they're able to focus a lot more on the work aspect. You know, and this one's the gamut from the nature of the job, where somebody who basically needs to be in the front of a computer and is remotely taking care of operations, you know, if they don't have to come in their productivity is going to go up. Somebody like myself who had a long drive into work, you know, which I would use on phone calls, but that entire time can be used a lot more productivity in a lot more productive manner. So there is, we realized that that there's going to be some aspects of productivity that'll actually be helped by the situation, provided you're able to deliver the services that you deliver with the same level of quality and satisfaction that you've always done. Now, there was certain other aspects where, you know, the productivity is going to be effective. So, you know, my team, there's a lot of whiteboarding that gets done. There are lots of informal conversations that spark creativity, but those things are much harder to replicate in a remote environment. So we've got a sense of, you know, where we have to do some work to put things together versus where we're actually going to be more productive. But all in all, we are very comfortable that we can pull this off. No, that's great. I want to stay on COVID for a moment. And in the context of just data and data ops and, you know, why now? Obviously, with a crisis like this, it increases the imperative to really have your data act together. But I want to ask you both specifically as it relates to COVID, why data ops is so important? And then just generally, why at this point in our time? So, I mean, you know, the journey we've been on. When I joined, our data strategy centered around cloud data and AI, mainly because IBM's business strategy was around that. And because there wasn't the notion of AI in enterprise, right? Everybody understood what AI means for the consumer, but for the enterprise, people didn't really understand what it meant. So our data strategy became one of actually making IBM itself into an AI enterprise. And then using that as a showcase for our clients and customers who look a lot like us to make them into AI enterprise. And in a nutshell, what that translated to was that one had to infuse AI into the workflow of the key business processes of enterprise. So if you think about that, workflow is very demanding, right? You have to be able to deliver data and insights on time, just when it's needed. Otherwise you can essentially slow down the whole workflow of a major process within an enterprise. But to be able to pull all that off, you need to have your own data arcs, very, very streamlined so that a lot of it is automated and you're able to deliver those insights as the people who are involved in the workflow needed. So we've spent a lot of time while we were making IBM into an AI enterprise and infusing AI into our key business processes, into essentially a data ops pipeline that was very, very streamlined, which then allowed us to very quickly adapt to the COVID-19 situation. And I'll give you one specific example that will go to how one could essentially leverage that capability that I just talked about to do this. So one of the key business processes that we had taken aim at was our supply chain. You know, we're a global company and our supply chain is critical. We have lots of suppliers and they are all over the globe and we have different types of products so that has a multiplicative factors, because for each of those you have additional suppliers and you have events, you have weather events, you have calamities, you have political events. So we have to be able to very quickly understand the risk associated with any of those events with regard to our supply chain and make appropriate adjustments on the fly. So that was one of the key applications that we built on our central data and AI platform. And as part of our data ops pipeline, that meant the ingestion of those several hundred sources of data had to be blazingly fast and also refresh very, very quickly. Also we had to then aggregate data from the outside and from external sources that had to do with weather related events that had to do with political events, social media feeds, et cetera. And overlay that on top of our map of interest with regard to our supply chain sites and also where they were supposed to deliver. We'd also weaved in our capabilities here to track those shipments as they flowed and have that data flow back as well so that we would know exactly where things were. This was only possible because we had a streamlined data ops capability and we had built this central data and AI platform for IBM. Now you flip over to the COVID-19 situation. When COVID-19 emerged and we began to realize that this was going to be a significant, significant pandemic, what we were able to do very quickly was to overlay the COVID-19 incidents on top of our sites of interest as well as pick up what was being reported about those sites of interest and provide that over to our business continuity. So this became an immediate exercise that we embarked but it wouldn't have been possible if you didn't have the foundation of the data ops pipeline as well as that central data and AI platform in place to help you do that very, very quickly and adapt to it very quickly. So what I really like about this story and something that I want to drill into is that essentially a lot of organizations have a real tough time operationalizing AI and fusing it to use your word. And the fact that you're doing it is really a good proof point that I want to explore a little bit. So you're essentially, there was a number of aspects of what you just described. There was the data quality piece with your data quality in theory anyway is going to go up with more data if you can handle it. And the other was speed, time to insight so you can respond more quickly. If it's think about this COVID situation if your days behind or weeks behind which is not uncommon, sometimes even worse, you just can't respond. I mean, the things change daily, sometimes certainly within the day. So is that right? That's kind of the business outcome and objective that you guys were after. Yes. So from an infused AI into your business processing, the overarching outcome metric that one focuses on is end to end cycle time reduction. So you take that process, the end to end process and you're trying to reduce the end to end cycle time by several factors, several orders of magnitude. And there are some examples of things that we did. For instance, in my organization that had to do with the generation of metadata is data about data. And that's usually a very time consuming process. And we reduced that by over 95% by using AI to actually help in the metadata generation itself. And that's applied now across the board for many different business processes that IBM has. That's the same kind of principle that was you to be able to do that. So that foundation essentially enables you to go after that cycle time reduction right off the bat. So when you get to a situation like a COVID-19 situation which demands urgent action, your foundation is already geared to deliver on that. So I think actually we might have a graphic and then the second graphic guys, if you bring up the second one, I think this is, Inderpal, what you're talking about here, that sort of 95% reduction guys, if you could bring that up, we'll take a look at it. So this is maybe not a COVID use case. Yeah, here it is. So that 95% reduction in cycle time, improvement in data quality, what we talked about there's actually some productivity metrics, right? This is what you're talking about here in this metadata example, correct? Yeah, yes, the metadata, right? It's so central to everything that one does with data. I mean, it's basically data about data. And this is really the business metadata that we're talking about, which is once you have data in your data lake, if you don't have business metadata describing what that data is, then it's very hard for people who are trying to do things to determine whether they can even, whether they even have access to the right data on them. And typically this process is being done manually because somebody looks at the data, they looks at the fields and they describe it and it could easily take months. And what we did was we essentially used a deep learning and a natural language processing approach, looked at all the data that we've had historically over at IBM and we've automated the metadata generation. So whether it was, you know, you were talking about the data relevant for COVID-19 or for our supply chain or for our accounts receivable process, any one of our business processes, this is one of those fundamental steps that one must go through to be able to get your data ready for action. And if you were able to take that cycle time for that step and reduce it by 95%, you can imagine the acceleration across. Yeah, and I like what you were saying before, you're talking about the end-to-end concept. You're applying system thinking here, which is very, very important because, you know, a lot of clients that I talked to, they're so focused on one metric, maybe optimizing one component of that end-to-end, but it's really the overall outcome that you're trying to achieve. You may sometimes, you know, be optimizing one piece, but not the whole. So that systems thinking is very, very important, isn't it? The systems thinking is extremely important overall, no matter, you know, where you're involved in the process of designing the system. But if you're the data guy, it's incredibly important because not only does that give you an insight into the cycle time reduction, but it also, it clues you in into what standardization is necessary in the data so that you're able to support an eventual outcome. You know, a lot of people will go down the path of data governance and the creation of data standard, and you can easily boil the ocean trying to do that. But if you actually start with an end-to-end view of your key processes, and then by extension, the outcomes associated with those processes as well as the user experience at the end of those processes, and kind of then work backwards as to what are the standards that you need for the data that's going to feed into all that. That's how you arrive at, you know, a viable practical data standards effort that you can essentially push forward with. So there are multiple aspects when you take that end-to-end system view that helps the chief leader. One of the other tenets of data ops is really the ability across the organization for everybody to have visibility. Communications is very key. We've got another graphic that I want to show around the organizational, you know, in the right regime. And this is a complicated situation for a lot of people, but it's imperative. Guys, if you bring up the first graphic, it's imperative that organizations, you know, find, bring in the right stakeholders and actually identify those individuals that are going to participate so that there's full visibility. Everybody understands what their roles are. They're not in silo. So guys, if you could show us that first graphic, that would be great. But talk about the organization and the right regime there, Inderpal. Yes, yes. I believe you're going to, what you're going to show up is actually my organization, but I think it's very illustrative of what one has to set up to be able to pull off the kind of impact that I talked about, you know. So let's say we talked about that central data and AI platform that's driving the entire enterprise and you're infusing AI into key business processes like the supply chain to then create applications like the operational risk insights that we talked about and then extended over to a fast emerging and changing situation like the COVID-19. You need an organization that obviously reflects the technical aspects of the platform, right? So you have to have the data engineering arm and an AI arm as, you know, in my case, there's a lot of emphasis around deep learning because that's one of those skillset areas that's really quite rare and but also very, very powerful. So, you know, they're the major technology arms of that. There's also the governance arm that I talked about where you have to produce a set of standards and implement them and enforce them so that you're able to make this end-to-end impact. And then there's also, there's an adoption arm where there's a group that reports into me and a very, very, you know, empowered group which essentially has to convince the rest of the organization to adopt. But the key to their success has been empowered in the sense that they're empowered to find like-minded individuals in our key business processes who are also empowered. And if they agree, they just move forward and go ahead and do it because, you know, we've already provided the central capabilities. By central, I don't mean they're all in one location. In fact, we're completely global and, you know, it's a hybrid multi-cloud setup but it's central in the sense that it's one source to come for trusted data as well as the key expertise that you need from an AI standpoint to be able to move forward and deliver the business outcome. So when these business teams come together with the adoption team, that's where the magic happens. So that's another aspect of the organization that's critical. And then we've also got a data officer council that I chaired. And that has to do with the people who are the chief data officers of the individual business units that we have. And they're kind of my extended team into the rest of the organization. And we leverage them both from a adoption of the platform standpoint, but also in terms of defining an enforcing standard that helps us do both. I want to come back to COVID, talk a little bit about business resiliency. People, I think you've probably seen the news that IBM's providing supercomputer resources to the government to fight coronavirus. You've also just announced that some RTP folks are helping first responders and nonprofits and providing capabilities for no charge, which is awesome. I mean, it's the kind of thing, look, I'm sensitive to companies like IBM. You don't want to appear to be ambulance chasing in these times. However, IBM and other big tech companies, you're in a position to help. And that's what you're doing here. So maybe you could talk a little bit about what you're doing in this regard. And then we'll tie it up with just business resiliency and the importance of data. Right, right. So, you know, I'd explained the operational risk insights application that we had, which we were using internally when we COVID-19 even we were using it. We were using it primarily to assess the risk to our supply chain from various events and then essentially react very, very quickly to those events so you could manage the situation. Well, we realized that this is something that, you know, several non-government NGOs that they could essentially use the same capability because they have to manage many of these situations like natural disasters and so forth. And so we've given that same capability to the NGOs to you and to help them streamline their planning and their thinking. By the same token, when you talked about COVID-19, that same capability with the COVID-19 data overlaid on top of that essentially becomes a business continuity planning and resilience because let's say I'm a supply chain person, right? Now I can look at the incidents of COVID-19 and I know where my suppliers are and I can see the incidents and I can say, oh, yes, no, this supplier and I can see that the incidents is going up. This is likely to be affected. Let me move ahead and start making plans, backup plans, just in case it reaches a crisis level. On the other hand, if you're somebody in our revenue planning, on the finance side and you know where your key clients and customers are located, again, by having that information overlaid with those sites, you can make your own judgments and you can make your own assessment to do that. So that's how it translates over into a business continuity and resilience planning tool. We are internally doing that now to every department. You know, that's something that we are actually providing them this capability because we could build rapidly on what we had already done to be able to do that. And then as we get inside into what each of those departments do with that data, because, you know, once they see that data, once they overlay it to their sites of interest and this is, you know, anybody and everybody in IBM because no matter what department they're in, there are going to be sites of interest that are going to be affected and they have an understanding of what those sites of interest mean in the context of the planning that they're doing and so they'll be able to make judgments. But as we gain a better understanding of that, we will automate those capabilities more and more for each of those specific areas. And now you're talking about a comprehensive approach, an AI approach to business continuity and resilience planning in the context of a large, complicated organization like IBM, which obviously would be of great interest to our enterprise clients and customers as well. Right. One of the things that we're researching now is trying to understand, you know, what about this crisis is going to be permanent? You know, some things won't be, but we think many things will be. There's a lot of learnings. Do you think that organizations will rethink business resiliency in this context that they might sub-optimize profitability, for example, to be more prepared for crises like this with better business resiliency and what role would data play in that? So, no, it's a very good question and timely question, Dave. So, I mean, clearly people have understood that with regard to such a pandemic, the first line of defense, right, is not going to be so much on the medicine side because the vaccine is not even available. It won't be available for a period of time. It has to go into development. So, the first line of defense is actually to take a quarantine-like approach like we've seen play out across the world here. And then that, in effect, results in an impact on the businesses, right? In the economic climate and on the businesses, there's an impact. So, I think people have realized this now. They will obviously factor this into how they do business. It will become one of those things, this is the one talking about how this becomes permanent. I think it's going to become one of those things that if you're a responsible enterprise, you are going to be planning forward, you're going to know how to implement this on the second go round. So, obviously you'll put those frameworks and structures in place and there will be a certain cost associated with them. And one could argue that that could eat into the profitability. On the other hand, what I would say is because these are the two points really that these are fast emerging fluid situations, you have to respond very, very quickly to those. You will end up laying out a foundation pretty much like we did, which enables you to really accelerate your pipeline, right? So the data ops pipelines we talked about, there's a lot of automation so that you can react very quickly. Data ingestion very, very rapidly that you're able to do that kind of thing, the metadata generation, just the entire pipeline that we're talking about that you're able to respond and very quickly bring in new data and then aggregated at the right levels infuse it into the workflows and then deliver it to the right people at the right time. And that will become a must. Now, but once you do that, you could argue that there is a cost associated with doing that, but we know that the cycle time reductions on things like that, they can run, I mean, I gave you the example of 95%. You know, on average, we see like a 70% end to end cycle time reduction where we've implemented the approach and that's been pretty pervasive within IBM across this process. So that in a sense then actually becomes a driver for profitability. So yes, it might, you know, this might back people into doing that, but I would argue that that's probably something that's going to be very good long-term for the enterprises involved and they'll be able to leverage that in their business. And I think that just the competitive pressure of having to do that will force everybody down that path anyway. But I think it'll be eventually a good thing. That end to end cycle time compression is huge. And I like what you're saying because it's not just a reduction in the expected loss during a crisis. There's other residual benefits to the organization. Inderpal, thanks so much for coming on theCUBE and sharing this really interesting and deep case study. I know there's a lot more information out there so really appreciate your time. My pleasure. All right, take care everybody. Thanks for watching. And this is Dave Vellante for theCUBE and we will see you next time.