 I'm Peter Burris, welcome again to another CUBE Conversation from our wonderful studios here in Palo Alto, California. Great conversation today, branching out into the world of data governance. A lot of things going on in the industry around data and what does it mean for digital business and how do we treat data increasingly as an asset and that obviously raises a lot of questions about how we govern those assets, improve their value, share them appropriately, same time privatize and make sure that they aren't corrupted. And to have this conversation, we've got two great guests. First off, we've got Kelly O'Neill, who's the CEO of First-San Francisco Partners, which is an information management consultancy here in the Bay Area. And Satyan Singani, welcome back to the CUBE, CEO of Elation, welcome. Thank you. So let's get started. I kind of said in the preamble that this notion of data governance becomes especially acute, especially important because we're now trying to treat data as an asset. So we're not governing the resources that manage data. We're actually trying to govern data itself, utilizing resources. So Satyan, why don't we start with you? What does data governance mean from a tool and process standpoint? And then Kelly, I'll ask you and let's go deeper into that process part. Yeah, I mean, I think, so there's lots of different definitions of data governance that a wide variety of experts have put out. And I'm not sure that I want to sort of put a new definition in the debate. Very generically, it's a set of processes that institutions use to manage the data that's at their disposal. And if you think about that generically in terms of where the problem is broadly stated, how do I manage my information and then the consumption and the production and the storage of that information? That is a super hard problem to deal with when you have hundreds of thousands of data sources, potentially millions of different data sets and thousands of people who are constantly consuming that information. And limited resources. And so the process of data governance now in a world to your point where every business is trying to become a digital business and where the monetization of data is a huge part of that business is the fundamental problem, right? How do we have people discover the data? How do we have people understand the data that they're seeing? How do we have people trust the data that they're seeing? That is the sort of problems of data governance. And that is what people are coming to realize today. So we do have tooling now that is specifically being built including Elation, which is a great catalog for performing some of these or to facilitate some of these governance activities. So there's enough of a standard set of definitions that we actually can put tooling in place which means now we can really liberate the power and the talent of people to appropriately govern data and use data. So Kelly, what are you doing with your clients to help them take the tools for data governance and turn it into ideally a strategic capability that really drives the digital business forward? Yeah, absolutely. So as a services organization, we really focus on ensuring that the people and the process are in place so that they can take advantage of the technology, right? So you've got accountability around who is responsible to ensure that data is of a certain sort of quality or a certain sort of standard as well as who has the ability to access that data and use that data. And I think one of the things that Satyan brought up is there's just this onslaught of data that's coming in. So if you think about that as a construct, it's entirely overwhelming. There's too much data to be able to say this person owns this data field. This person defines that data field. It has to be much more organic. It has to be much more shared. And tools- Communal. Much more communal, yeah. And so it really is this concept of how do we have a certain level of trust in the data and what does trust mean to the organization to take advantage of that data and to use it as an asset and to use it in business context. And so our services help organizations to see what that means to them, to right size that investment in the sense of how much effort do we put towards this? And then also how do we make sure that those tools are used, that they're adopted and that they're embedded into work processes? That it's not a standalone repository that never gets used. We had Aaron Calbon not too long ago to talk about trust check. And I know that's one of the things that's bringing you together is this notion of a communal approach to putting, to imbuing data with trust. So let's talk a bit about trust check and in particular how your companies are working together to accelerate the processes that you so accurately described. Satya, I want to start with you. What does trust check and what does it mean for you guys? Yeah, so trust check is a very simple, it's a very simple capability, although very complicated to implement. The idea behind trust check is that as and when somebody is consuming data, whether that's in a Salesforce dashboard or in a Tableau report or conversely even inside of a Lation, that immediately as they're consuming that information they're presented with context around that data talking about the appropriateness of that data for the use that they particularly have. Now that could be about timeliness of the data, that could be about the availability of the data, that could be about the quality of the data, that could be about the privacy regulations or the security surrounding that data. There are lots of reasons why one might not trust the data, but often that information is off to the side, right? And often that information is in a place where the consumer of the data has no awareness that the policy even exists, much less where to go get that information. And so what trust check is saying, look, this notion of governance has to actually be actionable, immediate and available in order for it to be valuable to the person that's using the information. And again, you might say that also that it might be trusted in this context, but not in another context as well. So how does that inform, or how does that facilitate, how does that accelerate implementing these processes to make sure that communities of data in an evidence-based world are better able to apply data, use data and share information about that data with each other? Yeah, absolutely. So it provides number one, just automation, right? So fundamentally that's a value add. It means that it's more available, it's more shared, it's faster. And that can make the governance organization more relevant to the business so that the data is actually used in a more appropriate and higher value way. So first thing's automation. And then the second thing is that as we start to automate, there's this concept of kind of learning and expanding. And so being able to leverage a tool within a services practice and methodology, it means that we can kind of start within one area and to leverage that learning and extend and extend and extend because fundamentally data is pervasive, right? It's everywhere. And which makes governance really intimidating and hard. So that idea of focusing, learning, doing something well and being agile, right? And growing over time. A tool really helps you to do that because it is a place where people can get focused for that learning and then repeat, rinse and repeat, rinse and repeat. So in many respects, it is a reflection, a manifestation of some of these good processes. Absolutely. You guys obviously have an enormous amount of knowledge about data governments, about the tooling for data to governments, about where this all goes. But ultimately a lot of your customers are still very much in the formative stages of putting this in place. So how are, other than just having them license a Lations toolkit, how are you coming together to put in place services or training or something else to help diffuse your knowledge into the organization? I just want to come back to one point that you mentioned because I think there's been a shift in the tooling market place. So I wouldn't say that the tooling has not that there's never been any tooling to deal with the problem of data governance. In fact, I think there's been lots of tooling that hasn't worked particularly very well. So let me put some context on that. So when I say tooling, as I said kind of upfront, to my mind it's tooling for the resources that handle the data, not tooling for the data. But keep going. If I'm wrong, I want to hear it. Well, no, I think even tooling for the resources that handle the data has largely been the province of, there is a category of software that one would traditionally describe in the realm of data stewardship and data governance. And broadly speaking, it allows you to create forms and to administer workflows with those forms. So that is a highly unautomated capability that focuses very deeply on the process of governance. And so what a traditional governance regime might include would be the development of policies and the enactment of those policies through a set of people who have to very manually check the data at their disposal. It is generally speaking disconnected from the data. When you have small sets of data, when you have limited quantities of data, that could be a perfectly fine solution. When you have a very small set of policies that you need to interact with, because you have to have a set of goals that are maybe regulatory in nature, that is an okay thing to go do. When you have petabytes of data across hundreds of thousands of data sets, it's an impossible thing to go do, right? And so I think that sort of inundation that Kelly was referring to is born out of this massive volume of data coming up where the traditional methods just don't work, right? And so are you talking about Sachin essentially that we're adding that metadata directly to the data itself and creating trusted objects that the organization can use and apply as assets wherever it needs to be. So that is exactly the solution. And the analogy that I think will let inform most of the people who are sort of listening to us today would be the sort of shift from Yahoo to Google, right? So if you think about Yahoo, Yahoo relied upon every single webmaster tagging every single web page to make sure that the search engine knew which web pages to go look up, right? That required a whole bunch of trust in your webmasters, first of all, some of whom were bad actors, right? You may not have those in the stewardship regime inside of enterprise, but you could, right? People have their own perspectives and it also required for people to have enough knowledge to tag things, right? So you'd have to know what to tag and that a tag would have to be right. For anybody who's developed a folder system, you know that those folder systems are constantly changing, right? And so then Google comes along and says, look, if we just watch what people are doing with this information and we know what people are linking to, then we can say, hey, what's more valuable, what's more useful by watching the behaviors, right? And I think that's the sort of shift of a bottoms up approach, which is different from sort of that top down declarative approach that's come in the technology for governance, unfortunately. And I think that's what people have to understand, which is that the problem's always been there, but what's happened is the volumes and the relevance and the timeliness of the information have just been so critical that now we have to change the way we do things. And that's what we're working together on. Yeah, it's more queuing. It has scale issues, but also the underlying technology has gotten to the point where we can actually do more automated discovery about how people are using things. Which means you have to change the process and the people. Right, great. So let's come back to that question. What are you guys doing together to ensure that you can in fact defuse this knowledge and defuse these insights in organizations faster so they can pick up on some of these changes better? Yeah. So I'll start. Yeah, please. Okay, sure. So First San Francisco is taking some of our methodologies and ensuring that they are right sized and fit for the elation suite of products, especially the trust check suite of products. And so what we're starting with is the data acquisition process. And that's important because the supply chain for data is what has become inordinately complex. It's no longer primarily internally created data. Most data is actually acquired. And so if we start with that ingestion process and the data acquisition process, that's a huge value both to the customers that are using it as well as to the mutual organizations. So right, focusing on that as a case. And then we'll move on to the concept of information stewardship itself. So stewardship across the supply chain, not just the data acquisition supply chain. So we are adapting our methodologies to be specific and unique to elation to help their existing customer base and obviously potentially new customers together. Yeah. So and a great example of that, I was just talking to a chief data officer of a very large financial institution in North America. You know, this individual was contending with the problem of making good data available to their end, you know, end business audience for analytical purposes, right? To solve exactly this problem. He said, we acquire companies all the time. We're acquiring companies constantly. And we're getting all of this data in and I have to figure out what this data is. And do I already have this data in-house? Do I have systems that store this sort of data? Do I have systems that duplicate the data but incorrectly? And are there multiple of these sets of data inside the company that I'm acquiring? Because they've got data duplication just like we do. And how do we figure all of that out, right? So this would be a perfect example where the data acquisition problem is critical to solve in the process of being able to create available useful governed data, right? And so this would be a perfect example for the two of our companies to be able to work together because we don't speak to the implementation and the process. We speak to the technical capability of simply providing the inventory so that somebody can then figure out what to do or do with that information. But there are practices that are probably going to do better or will generate greater value out of the elation toolkit than others would. Absolutely. Yeah, and so in many respects, we're looking into companies like yours to help define what those practices are, diffuse them more broadly through some package consulting and through a really good partnership that you guys have been working on. Yeah, because I mean, you know, I mean, I think Larry Ellison's a controversial character, right? Who? But you know, I'll quietly say that I worked at Oracle at one point in time. One of the things that Larry said is, you know, people when they buy software are constantly asking the question of how do I figure out how to take my existing business process and fit it on top of the software that exists out there? And he's like, no, that's exactly wrong. What people should be doing is figuring out what should my business process be given the capability that I've got, right? And so we now have a new capability and we're enabling people to have more or less superpowers relative to what they would have had to do by hunting and pecking through every data set and tagging it manually, right? And what, you know, Kellyanne for San Francisco are bringing to the table is the ability to have a new process that would allow them to do that at scale and faster. So that's why we're super excited. Excellent. So in a data-first world, data governance becomes more important to thought leaders helping to make that happen. Satchin Sugani, CEO of Elation. Kelly O'Neill for San Francisco Partners. Thanks very much for being on the queue. Thank you, Peter. Thank you. And once again, this has been a CUBE Conversation from our Palo Alto studios. Thank you very much for watching. Until next time.