 ServiceNow Knowledge 14 is sponsored by ServiceNow. Here are your hosts, Dave Vellante and Jeff Frick. Good day everybody from San Francisco, awesome city. We heard Frank Slutman talking in this keynote about the area, the San Francisco, the gold rush of California. We're seeing a gold rush of innovation. We're in the heart of innovation here in San Francisco and close to Silicon Valley. And we've been chatting all week and we're going through tomorrow through Wednesday at ServiceNow Knowledge. Walker White of Ceres, the Chief Technology Officer at BDNA, who's a partner of ServiceNow and experts in ITSM and CMDB and all the cool buzzwords in this space. Welcome to theCUBE, great to see you. Thank you very much, Dave. We appreciate the opportunity to be here today. So Walker, tell us a little bit about BDNA, what you guys do and maybe a little bit about your role. Sure, absolutely. So I'm the Chief Technology Officer for BDNA. I've been with BDNA basically since we began. And BDNA is basically a data platform. We serve the role of consolidating and standardizing information from the various operational tools that are in the environment. And systems like ServiceNow, which are working to standardize and consolidate processes where they have these common data models, we feed in common data into those systems. So it's a very core platform. If you have to look for an analogy, it would be, we do for data what Java did for cross-platform development. We hide the complexity of all the information. We standardize it to a reference model and feed it into systems that require it. So how does that relate to this notion of a single source of record, CMDB? Are you ultimately that single source of record? It's a great question, Dave. But in fact, what BDNA is doing is ServiceNow is providing the service-oriented enterprise. They're consolidating and standardizing processes. They have a common data model. The problem is we want to get common data into that model. So the problem that they face, they're kind of threefold. You've got vendors without good information, good standards about their data. The data that they're providing is cryptic and very hard to understand. And then you've got the market velocity on top of it. So BDNA brings a catalog, an engine to normalize data to that catalog and processes that keep that information in sync with one another. So our customers get the benefit of basically improved efficiency and lowering costs, every process, improved decision-making in those processes, and improving decision-making. We're really resolving the garbage-in-garbage-out problems. So we're giving people really good data to drive these processes. So let me give you a couple numbers. 50% of our customers achieve full ROI with our solution in the first month. 75% of our customers achieve full ROI in the first six months. First month. First month, 50%. Raise your rates. So 75% in the first six months, 90% in the first year. 66% of our customers get a more than 200% ROI on our solution in the first year. And these are audited numbers, not taken by us by a third party that interviews our customers. What's the biggest driver of the ROI? Improving the decision, the quality of the information, right? So I mean, imagine the problem you would have, which is we have data, think about all the processes you're implementing in ServiceNow. You've got incident, problem, change, asset, configuration. We're feeding information in there, but if they don't have good information to act upon, all of those things start to break down. So imagine like the legacy systems where you had, some data over here for assets, some over here for configuration. ServiceNow's consolidate all that, and that's great, right? But now, if you get bad data in one place, it's going to percolate through everything. I love this discussion. It's like I said, it's an information quality. It's a data quality. You guys are like the data czar, if you will, right? And it's not something that we've talked a lot about here. We talk about it a lot at other conferences, particularly big data conferences, right? Because big data, this is so much data, you're now we're further away from the single version of the truth than we ever were. But despite the fact that you've got a single data model, you've got multiple data sources. And even, I mean, what percent of the customers that you talk to really truly have a single CMDB? Well, they will often have a single CMDB, but the data in there is not, they know what it is, right? They understand, kind of take it for what it is. So again, it's that problem. The vendors have no standards, right? The data that they do provide is cryptic, right? In the market velocity. I mean, this is the three horsemen of the bad data apocalypse, right? They just, you can't get good information on this way. So by filtering this information, eliminating the duplication, aligning it to a reference catalog, we're basically solving the garbage in, garbage out problem for ServiceNow and other processes and platforms that demand it. You know, this again leads to an interesting discussion of roles. I called you guys the data czar, but really there's a role emerging within organizations, certainly within regulated industries like financial services and healthcare and government and even big pharma, where there's a chief data officer emerging, independent of the CIO, often, more often than not, at least in those industries. But broadly, that's not the case. It's sort of part of the CIO's, you know, purview or maybe it's even a governance or audit type of thing. What do you see there is that role? Do you see that chief data officer, data czar emerging? I mean, it's obviously something that you guys are advocating. Yeah, absolutely. We obviously see, you know, it's something, I've read the studies, it's like 25% of Fortune 500 will have a CDO at the end of this year, this calendar year. I mean, that's a great target for us, it's a great person to talk to. And I think that, you know, from our perspective, being able to gain access to that individual and make them aware of the fact that the data that already exists in their environment, if we can be cleaned, if it can be filtered, if it can be duplicated, is likely all the information they need to be successful. So that's really where we're, it's a great opportunity for us. I mean, I wish every organization had a CDO because it'd make calling them easy. MIT does a lot of work in this area. In fact, we in July will be at the MIT Chief Data Officer Conference in Information Quality. And it's really, they're doing a lot of interesting research there. You should, you know, check that out. So, okay, so the problem you're solving is really a data quality one, but you've still got all these disparate systems, right? So even with the disparate systems, can you take us through sort of an example as to how you, from a data standpoint, attack that problem? Sure, so let's give you one quick example, which is the Heartbleed Vulnerability, which just came out. So if you look into our solution, Jeff, for example, for the Heartbleed, we represent or have in our catalog 2,087 distinct ways that OpenSSL has been represented across these 40 different tools. So what do you think the chances are for all those entities that are out there that have tried to figure out what their OpenSSL exposure is, that they've caught all 2,087, right? The chances are slim to none, right? And Slim just got on a bus leaving the Moscone Center, right? There's no chance, there's no chance they're getting that information, so. So by basically having seen literally hundreds of millions and billions of rows of data and deterministically being able to say, this thing that your operational tool is handing me is actually this thing in our catalog and then being able to enrich that with nondiscoverable market information, that's a really powerful solution. Who's not a prospect for what you do? Who doesn't need a better day to call me? Someone asked this question, it's like what's the best ITIL process to use this for? And it's kind of like asking ServiceNow, the question of which process do you really want to automate? And the answer is, yes, right? There really is, what is wonderful about being this data platform is everyone has the same problem, right? They're all using the same tools to collect the information. They all want to feed that information into the same tools on the other side, but the garbage and Carbachow problem is just following them everywhere they go. And if you don't solve this problem upstream of where the issues are, it's going to percolate into every process, right? And that's going to drive down the ROI of all those solutions. What point do you get involved in ITSM projects? So people come to BDNA when they have one of three problems. They either come to us where they have a process problem, they have a project problem, or they have a data problem. And to be perfectly honest, the ones in the worst situation are those that arrive with a process problem, right? My CMDB has bad data, we're getting outages because we can't track what's going on. A little bit more mature customers will catch us at the project problem, right? I'm trying to do an audit and I can't get a sense of what products are installed and what those, you know, I need to be able to understand what my Oracle distribution is or my Adobe distribution. I just can't see it. I've got eight tools collecting data and it's all over the map. But the really mature customers come to us when they see the data problem. And this is why the chief data officer is going to be so valuable to BDNA because you'll have somebody whose curview will be looking at the Software Asset Management Initiative is going on and these guys are trying to clean up this data. Over here in Enterprise Architecture, I got another guy who's trying to build life cycle information. And over here in the CMDB, I'm trying to clean up, you know, other data still, right? And someone with that curview could be like, this is all the same problem, right? In various manifestations. So let's industrialize the solution to that and that's where we come in. That's where you get a data platform. Yes, that's interesting. So process, project, data, process, it's like, oh, that's a mess and it's broken. Projects sort of tactical, right? Yeah, yeah. I'm risking an Oracle audit. Yeah, yeah, yeah. Exactly. Help me. Yeah, exactly. We get a lot of those goals. You should have talked to Benioff. He had to spend about 900 million to solve his audit. Yeah, exactly. There's a lot of threat problems. That's 900 million between friends, right? Yeah, that's true. And when those friends are billionaires. Yeah, exactly. But the data problem is really the opportunity. Oh yeah. Right, because that's where all the value creation comes from. Can you share with us some examples of folks that are that far down the maturity curve that are actually, we're hearing a big theme this week at the conference about turning IT cost into a value generation. Can you talk about concrete examples that you mentioned ROI before, people driving sort of that type of value with that single data platform? Yeah, so a good example of this. We have a, this is a federal customer. So we started with this customer with enterprise architects, right? So IT planners. And their idea was like, we need a catalog. And so we provided our catalog and they started using our nomenclature for planning tools. Like what are we going to deploy? What are we going to use and so on? Their software asset management team got wind of that and said, hey, that's a great way to represent your data. So let's normalize our data to this same language. Therefore, from an EA perspective, what you guys are planning is what we're deploying, right? And then finally, the procurement team got a hell of it and said, well, now that we know you're buying it in this manner, let's, or excuse me, now that we know you're deploying it with this language, let's rationalize all of our purchases to the same language, right? But then the CIO sees these three projects all in one organization and they're like, wait a minute, if we get every project to use this language, right? We're going to get rid of the frictional cost in the entity, which is that the EA guys having a different language than the SAM guys, than the procurement guys and on down the line. So once they bring that up to an enterprise level, you really get the accelerators. There's the force multiplier basically of using it across many, many more processes. Now, what do you actually sell? You sell a combination of technology and services? We have a solution which is our data platform, which is either an on-premise solution or a hosted or a hosted cloud solution. The only services we provide are for the implementation of our technology and the integration of that technology into things like ServiceNow and so on. Okay, so you're essentially a software company? We are a software company and Frank, you know, we're, you know, here at, well at Knowledge 13 here, 14, you know, we're a partner of ServiceNow, obviously as you mentioned at the beginning of this, but we're also a design partner in the design partner program with ServiceNow and the only ISV that's ever been in that program, which is typically allocated solely for customers. And the reason is that we're solving such a fundamental problem that we're really down in the fabric of the ServiceNow platform and the other platforms that we do this for. Now, yeah, so you're, I guess the concept, platform agnostic, right? You sort of have to be. Yes, we are. All right, but do you have like a favorite child that you don't want to tell the others about? Not when I'm sitting at the Knowledge 14. You know, you don't have to answer that. Walker, it strikes me, this sounds like a massively giant problem, far outside the scope of just the ServiceNow implementation. Oh yeah, yeah, it's a huge problem. I think it, you know, the integration with ServiceNow, obviously, is just very elegant, right? They've got a brilliant, you know, common data model, you know, they just need common data for it, so the integration is very elegant, but I think the problem, it goes far beyond IT and this is where, from a trajectory standpoint, the medium A looks at the world, is that you can take this problem and it is a very small subset of the problem faced in medical devices. It is a very small subset of the problem faced in industrial equipment and then onto into the Internet of Things. Right, yeah, as I said, you know, big data and then, oh my God, the Internet of Things is right around the corner. Absolutely, and for us, it's just, it's all ones and zeros. They all have to do the same thing. I got a bunch of stuff, I got a reference architecture, I want to enrich it with, you know, in medical devices, it would be, you know, country of origin, FDA compliance as opposed to end-of-life dates and, you know, not a little wattage, but it's the exact same problem all over again, it's just ones and zeros, so. Can you talk about your technology a little bit, Fred, I love Fred Lutty, he talks about how, you know, technology should be like magic, you know. Yeah. So what's your magic, what's your secret sauce? Because the problem you're solving is very difficult. Yeah, so, you know, primarily, you have to look at kind of the history of BDNA is that the first thing we did was to develop this catalog, right? So we said, look, let's figure out what the world, what's the locus of the problem? So we enumerated, basically, every piece of hardware inversion in addition and we put that into a taxonomy and we shared that with customers, they said, wow, this is great, this is really, this is fantastic and I would tell you that as our Chief Technology Officer the greatest piece of intellectual property that we have is our ability to create, manage and curate that content at the rate of market change, right, we make about a thousand changes every day to that catalog. So. Actual hardware devices and software. Net new devices, new attributes about devices, M&A activity, you know, IBM pushes out a new release of a new piece of software that has to be enumerated and captured in technopedia in our catalog. So. It's like the master catalog. Yeah, exactly. Okay. And then, so after building that catalog, the customers were like, look, I love this catalog. Problem is, I've got all these tools and I can't align those tools to that catalog. So, how can you help me? And we said, well, we have this normalization solution that will basically take your data and translate it to this language. And then once it's translated to that language it's available to be consumed by virtually any downstream system. So, yeah, that's kind of the secret. Okay, and then when you implement it does a discovery. No, well we actually take data from existing solutions. From existing discovery solutions, okay. And then normalize it. So you're tapping into the cost, you're tapping into the cost and the effort and the skills and you know, all the vested interests that they have in the existing tools. So we just take that data out, clean it up and send it on its way. And where does it go? Anywhere. It can go into a CMDB. Exactly. Okay. So if you look again at our most. So you're a filter. Yeah, exactly. If you look at our most mature customers they'll have, you know, 10, 12 sources. Then they'll have our data platform and then they'll have that data will be going out into EA solutions, software asset management solutions, CMDBs. It will be go basically anywhere it needs to go but it all runs through this, right? It's a data platform just like Java is for, you know. Yeah, okay. Multilingual translator. Exactly. It's a Rosetta Stone. Rosetta, you're a Rosetta Stone of data. Beautiful. All right Walker, we have to leave it there. We've got the next guest is here. Plains are backing up as they say. We've been going wall to wall here for two days. Thanks very much. Great, we appreciate the time. I appreciate the time. And congratulations. Thank you very much. Good luck. All right, keep it right there everybody. Jeff Frick and I will be right back after this.