>>from the Cube Studios in >>Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. Everybody welcome this special digital presentation where we're covering the topic of data ops and specifically how IBM is really operationalize ing and automating the data pipeline with data office. And with me is Interpol Bhandari, who is the global chief data officer at IBM and Paul. It's always great to see you. Thanks for coming on. >>My pleasure. >>So, you know the standard throwaway question from guys like me And you know what keeps the chief data officer up at night? Well, I know what's keeping you up that night. It's coverted 19. How you >>doing? It's keeping keeping all of us. >>Yeah, for sure. Um, So how are you guys making out as a leader I'm interested in, You know, how you have responded would whether it's communications. Obviously you're doing much more stuff you remotely You're not on airplanes. Certainly like you used to be. But 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 wants to test the ability of my organization, You work remotely. This was well before the the recommendations came in from the government just so that we wanted to be sure that this is something that we could pull off if there were extreme circumstances where even everybody was. And so that was one of the first things we did along with that. I think another major activity that's being boxed off is even that we have created this Central Data and AI platform for idea using our hybrid, multi cloud approach. How could that the adaptive very, very quickly help them look over the city? But those were the two big items that my team and my embarked on and again, like I said, this is before there was any recommendations from the government or even internally, within IBM. Have any recommendations be? We decided that we wanted to run ahead and make sure that we were ready to ready to operate in that fashion. And I believe a lot of my colleagues did the same. Yeah, >>there's a 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 politics. I mean, in fact, I will tell you that while we've gone down this spot, 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, you know, And this could. This one's the gamut from the nature of the jaw, where you know somebody who basically needs to be in the front of the 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 a phone calls, but that that entire time it can be used a lot more productivity, locked in a lot more productive manner. So there is. We realized that there's going to be some aspect of productivity that will actually be helped by the situation. Why did you are able to deliver the services that you deliver with the same level of quality and satisfaction that you want Now there were certain other aspect where you know the whole activity is going to be effective. So you know my team. There's a lot off white boarding that gets done there lots off informal conversations that spot creativity. But those things are much harder to replicate in a remote and large. So we've got a sense off. You know where we have to do some work? Well, things together. This is where we're actually going to be mobile. But all in all, they're very comfortable that we can pull this off. >>That's great. I want to stay on Cove it 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 covert, why Data office is so important. And then just generally, why at this this point in time, >>So, I mean, you know, the journey we've been on. Thank you. You know, when I joined our data strategy centered around cloud data and ai, mainly because IBM business strategy was around that, and because there wasn't the notion off AI and Enterprise, right, there was everybody understood what AI means for the consumer. But for the enterprise, people don't really understand. Well, what a man. So our data strategy became one off, actually making IBM itself into an AI and and then using that as a showcase for our clients and customers who look a lot like us, you make them into AI. And in a nutshell, what that translated to was that one had two in few ai into the workflow off the key business processes off enterprise. So if you think about that workflow is very demanding, right, you have to be able to deliver. They did not insights on time just when it's needed. Otherwise, you can essentially slow down the whole workflow off a major process within an end. But to be able to pull all that off you need to have your own data works 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 work floor needed. So we've spent a lot of time while we were making IBM into any I 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 do very quickly adapt do the over 19 situation and I'll give you one specific example that will go to you know how one would someone would essentially leverage that capability that I just talked about to do this. So one of the key business processes that we have taken a map, it was our supply chain. You know, if you're a global company and our supply chain is critical, you have lots of suppliers, and they are all over the globe. And we have different types of products so that, you know, has a multiplication factors for each of those, you have additional suppliers and you have events. You have other events, you have calamities, you have political events. So we have to be able to very quickly understand the risks associated with any of those events with regard to our supply chain and make appropriate adjustments on the fly. So that was one off the key applications that we built on our central data. And as Paul about data ops pipeline. That meant we ingest the ingestion off those several 100 sources of data not to be blazingly fast and also refresh very, very quickly. Also, we have to then aggregate data from the outside from external sources that had to do with weather related events that had to do with political events. Social media feeds a separate I'm overly that on top off our map of interest with regard to our supply chain sites and also where they were supposed to deliver. We also leave them our capabilities here, track of those shipments as they flowed and have that data flow back as well so that we would know exactly where where things were. This is only possible because we had a streamline data ops capability and we have built this Central Data and AI platform for IBM. Now you flip over to the Coleman 19 situation when Corbyn 19 merged and we began to realize that this was going to be a significant significant pandemic. What we were able to do very quickly wants to overlay the over 19 incidents on top of our sites of interest, as well as pick up what was being reported about those sites of interests and provide that over to our business continuity. So this became an immediate exercise that we embark. But it wouldn't have been possible if you didn't have the foundation off the data office pipeline as well as that Central Data and AI platform even plays to help you do that very, very quickly and adapt. >>So what I really like about this story and something that I want to drill into is it Essentially, a lot of organizations have a really tough time operational izing ai, infusing it to use your word and the fact that you're doing it, um 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 talk about this Covic situation. If you're days behind for weeks behind, which is not uncommon, sometimes even worse, you just can't respond. I mean, the things change daily? Um, sometimes, Certainly within the day. Um, so is that right? That's kind of the the business outcome. An objective that you guys were after. >>Yes, you know, So Rama Common infuse ai into your business processes right over our chain. Um, don't come metric. That one focuses on is end to end cycle time. 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 you know, there are some examples off things that we did. For instance, in my organ organization that has to do with the generation of metadata is data about data. And that's usually a very time consuming process. And we've reduced that by over 95%. By using AI, you actually help in the metadata generation itself. And that's applied now across the board for many different business processes that, you know IBM has. That's the same kind of principle that was you. You'll 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 over 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 a 2nd 1 I think this is Interpol. What you're talking about here, that sort of 95% reduction. Ah, guys, if you could bring that up, would take a look at it. So, um, this is maybe not a cove. It use case? Yeah. Here it is. So that 95% reduction in the cycle time improvement in data quality. What we talked about this 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. I mean, it's basically data about data, and this is really the business metadata that you'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. And typically this process is being done manually because somebody looks at the data that looks at the fields and describe it. And it could easily take months. And what we did was we essentially use a deep learning and natural language processing of road. Look at all the data that we've had historically over an idea, and we've automated metadata generation. So whether it was, you know, you were talking about the data relevant for 19 or for supply chain or far receivable process any one of our business processes. This is one of those fundamental steps that one must go through. You'll 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. >>Yeah, and I like you were saying before you talk about the end to end concept, you're applying system thinking here, which is very, very important because, you know, a lot of a lot of clients that I talk 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 off 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 give clues U N into what standardization is necessary in the data so that you're able to support an eventual out. You know, a lot of people will go down the part of data governance and the creation of data standards, and you can easily boil the ocean trying to do that. But if you actually start with an end to end, view off your key processes and that 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 one of 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 so that there are multiple aspect when you take that end to end system view that helps the chief legal. >>One of the other tenants 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 it's a complicated situation for a lot of people. But it's imperative, guys, if you bring up the first graphic, it's a heritage that organizations, you know, find bringing the right stakeholders and actually identify those individuals that are going to participate so that this full visibility everybody understands what their roles are. They're not in silos. So, guys, if you could show us that first graphic, that would be great. But talk about the organization and the right regime there. Interpol? >>Yes, yes, I believe you're going to know what you're going to show up is actually my organization, but I think it's yes, it's very, very illustrative what one has to set up. You'll be able to pull off the kind of impact that I thought 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. Then create applications like the operational risk in size that we talked about that extended over. Do a fast emerging and changing situation like the over 19. You need an organization that obviously reflects the technical aspects of the right, so you have to have the data engineering on and AI on. You know, in my case, there's a lot of emphasis around deep learning because that's one of those skill set areas that's really quite rare, and it also very, very powerful. So uh huh you know, the major technology arms off that. There's also the governance on that I talked about. You have to produce the set off standards and implement them and enforce them so that you're able to make this into an impact. But then there's also there's a there's an adoption there. There's a There's a group that reports into me very, very, you know, Empowered Group, which essentially has to convince the rest of the organization to adopt. Yeah, yeah, but the key to their success has been in power in the sense that they're on power. You find like minded individuals in our key business processes. We're also empowered. And if they agree that just move forward and go 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. You're completely global and you know it's it's It's a hybrid multi cloud set up, but it's a central in the sense that it's one source to come for for trusted data as well as the the expertise that you need from an AI standpoint to be able to move forward and deliver the business out. So when these business teams come together, be an option, that's where the magic happens. So that's another another aspect of the organization that's critical. And then we've also got, ah, Data Officer Council that I chair, and that has to do with no people who are the chief data officers off the individual business units that we have. And they're kind of my extended teams into the rest of the organization, and we levers that bolt from a adoption off the platform standpoint. But also in terms of defining and enforcing standards. It helps them stupid. >>I want to come back over and talk a little bit about business resiliency people. I think it probably seen the news that IBM providing supercomputer resource is that the government to fight Corona virus. You've also just announced that that some some RTP folks, um, are helping first responders and non profits and providing capabilities for no charge, which is awesome. I mean, it's the kind of thing. Look, I'm sensitive companies like IBM. You know, 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. Um, and then we'll tie it up with just business resiliency and importance of data. >>Right? Right. So, you know, I explained that the operational risk insights application that we had, which we were using internally, we call that 19 even we're using. We're using it primarily to assess the risks to our supply chain from various events and then essentially react very, very quickly. Do those doodles events so you could manage the situation. Well, we realize that this is something that you know, several non government NGOs that they could essentially use. There's a stability because they have to manage many of these situations like natural disaster. And so we've given that same capability, do the NGOs to you and, uh, to help that, to help them streamline their planning. And there's thinking, by the same token, But you talked about over 19 that same capability with the moment 19 data over layed on double, essentially becomes a business continuity, planning and resilience. Because let's say I'm a supply chain offers right now. I can look at incidents off over night, and I can I know what my suppliers are and I can see the incidents and I can say, Oh, yes, no, this supplier and I can see that the incidences going up this is likely to be affected. Let me move ahead and stop making plans backup plans, just in case it reaches a crisis level. On the other hand, if you're somebody in revenue planning, you know, on the finance side and you know where you keep clients and customers are located again by having that information over laid that 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 business continuity and resolute resilience planning. True, we are internally. No doing that now to every department. You know, that's something that we're actually providing them this capability because we build rapidly on what we have already done to be able to do that 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 with their sights of interest. And this is, you know, anybody and everybody in IBM, because no matter what department they're in, there are going to decide the interests that are going to be affected. And they haven't understanding what those sites of interest mean in the context off the planning that they're doing and so they'll be able to make judgments. But as we get 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 the comprehensive approach and AI approach to business continuity and resilience planning in the context of a large IT organization like IBM, which obviously will be of great interest to our enterprise, clients and customers. >>Right? One of the things that we're researching now is trying to understand. You know, what about this? Prices is going to be permanent. 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 crises like this with better business resiliency? And what role would data play in that? >>So, you know, it's a very good question and timely fashion, Dave. So I mean, clearly, people have understood that with regard to that's such a pandemic. Um, the first line of defense, right is is not going to be so much on the medicine side because the vaccine is not even available and will be available for a period of time. It has to go through. So the first line of defense is actually think part of being like approach, like we've seen play out across the world and then that in effect results in an impact on the business, right in the economic climate and on the business is there's an impact. I think people have realized this now they will honestly factor this in and do that in to how they do become. One of those things from this is that I'm talking about how this becomes a permanent. I think it's going to become one of those things that if you go responsible enterprise, you are going to be landing forward. You're going to know how to implement this, the on the second go round. So obviously you put those frameworks and structures in place and there will be a certain costs associated with them, and one could argue that that would eat into the profitability. On the other hand, what I would say is because these two points really that these are fast emerging fluid situations. You have to respond very, very quickly. 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, you know, data injection very, very rapidly that you're able to do that kind of thing, that meta data generation. That's the entire pipeline that you're talking about, that you're able to respond very quickly, bring in new data and then aggregated at the right levels, infuse it into the work flows on the delivery, do the right people at the right time. Well, you know that will become a must. But once you do that, you could argue that there's a cost associated with doing that. But we know that the cycle time reductions on things like that they can run, you know? I mean, I gave you the example of 95% 0 you know, on average, we see, like a 70% end to end cycle time where we've implemented the approach, and that's been pretty pervasive within IBM across the business. So that, in essence, 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 and world, and they'll be able to leverage that in their in their business and I think that just the competitive director off having to do that will force everybody down that path. But I think it'll be eventually ago >>that end and cycle time. Compression is huge, and I like what you're saying because it's it's not just a reduction in the expected loss during of prices. There's other residual benefits to the organization. Interpol. Thanks so much for coming on the Cube and sharing this really interesting and deep case study. I know there's a lot more information out there, so really appreciate your done. >>My pleasure. >>Alright, take everybody. Thanks for watching. And this is Dave Volante for the Cube. And we will see you next time. Yeah, yeah, yeah.