>> Live from Las Vegas, it's theCUBE. Covering Informatica World 2019, brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World here in Las Vegas. I'm your host, Rebecca Knight along with my co-host John Furrier, we are joined by Jitesh Ghai, he is the Senior Vice President and General Manager Data Quality, Security and Governance at Informatica. Thank you so much for coming or returning to the show Jitesh. >> My pleasure, happy to be here. >> So, this is a real moment for data governance, we have the anniversary of GDPR and the California Privacy Act it's a topic at Dabos, there is growing concern among the public and lawmakers over security and privacy, give us the lay of the land from your perspective. >> Right, you know it is a moment for data governance, what's exciting in the space is governance was born out of risk and compliance and managing for risk and compliance, but really what it was mandating was healthy data management practices, how do we give the regulators comfort that our data is of high quality, that we know the lineage of where data is coming from that we know how the business relies on the data what is critical data? And while it was born to give the regulators comfort, what organizations very quickly realized is well when you democratize data, you need to give everybody that comfort, you need to give your data scientists, your data analysts, that same level of contextual understanding of their data right, where did it come from? What's the quality of it? How does the business use it, rely on it? And so that has been a tremendous opportunity for us, we've supported organizations, financial services from a BCBS 239 CCAR, counterparty credit risk, but what's happened is from a data democratization, data scale perspective, self-service analytics perspective, is what moved from terabytes to petabytes. We've moved from data warehouses, to data lakes and you can't democratize data unless there's a governed framework. I don't know, it sounds kind of like wait, democratizing data is supposed to be free data everywhere, but without some governed framework, it's a bit of a mess, and so what we're enabling organizations is the effective consumption and understanding of where their data is, discovering it, so that the right people can consume the data that they care about, the right data scientists can build the right models, the right analysts can build the right reports and the executives get the right confidence on what reports they're getting, what KPI's they're getting. >> One of the things that we talked last year, you had a couple customers on, you had told a great story, you guys had had the benefit as a long-standing company, 25 years in the private for large-customer base, but the markets changed, you mentioned governance I mean we're in the one year-anniversary of GDPR. >> Right. >> And I think everyone's kind of like OK what happened last year? More privacy laws are coming and one of the themes this year is clarity with data, but also in the industry you know access to data, making data addressable, because AI needs data sets, cloud has proven that, SAS business models, using data winning formula, that's clear if you're born in the cloud. Enterprises now want that same kind of SAS-like execution on the applications side, whether it's SAS or using AI for instance, >> Right. >> So when you have more regulation, inherent nature is to oh like more complexity, how are customers dealing with the complexity of this, because they want to free it up, but at the same time they want to make sure that they can respect the laws for individuals, but also governments aren't that smart either so you know, the balance there, what's the strategy? >> And therein lies the challenges with privacy specifically, it's not just about quality counterparty credit risk in like five or seven systems in a data warehouse, it's all the data in your enterprise, it's the data in production, there's the data in your DevOps environment, it's all your data literally, structured all the way to unstructured data like Word, PDFs, Powerpoints. And you need a governing framework around it, you need to enable organizations to be able to discover where is there sensitive information, how is there sensitive information proliferating through the organization? Is it protected? Is it not protected? And what's particularly, you know, we're all consumers, I'm pretty confident some or all of our data has been breached at some point, enabling organizations, what these privacy regulations are doing is they are giving us, as individuals, rights to go to the organizations we transact with and ask them, what are you doing with out data? Forget my data or at least tell me how you're processing it and get my consent for the data. >> Yeah, I mean policy and business models are certainly driving that and with regulation, I see that, but the question is that when you move the impact to the enterprise, you got storage drives. You store it on drives as a storage administrator you've got software abstractions with data, like you guys do. So, it's complicated, so the question is, for you, is what are customers doing now? What's the answer to all this? >> The answer really comes down to you need to scale to the scope of the problem, it's a thousand x-increase, you're going from terabytes to petabytes right? And so, you need an AI, an ML, an intelligent solution that can discover all of this information, but it can map it to John Furrier, this is where John Furrier's information is, it's in the human capital management system, the CRM system, organizations know, may start knowing whether sensitive data is, but hey don't know who it belongs to, so when you go to invoke your right to be forgotten or portability, today, what we're enabling organizations with is hey, we'll help you discover the sensitive information, but we'll also tell you who it belongs to, so that when John shows up or Rebecca, you show up, you just have to punch in their name and we'll tell you all the systems, that it's in. That is something that requires teams of database administrators, lawyers, system administrators that needs to be automated, to truly realize the potential of these privacy regulations, while enabling organizations to continue to innovate and disrupt with data. >> What's your take on whether or not consumers truly understand the scope of these privacy regulations, I mean talking about GDPR and you get the pop-ups that say do you consent and you just say yes, I just need to get to this site and so you blithely, just press yes, yes, yes so you are technically giving your consent, but do you, I mean what's your take, do consumers truly understand what they're doing here? >> You know, I think historically, we've all said yes, yes, yes, over the last, I would say two years with growing regulations and significant breaches, there is a change in customer expectations, you know, there's a stat out there in the event of a data breach, two-thirds of consumers of a particular organization blame the organization for the breach, not the hackers, right, so it's a mindshift in all of us, where you're the custodian of my data I'm counting on you, whatever organization I'm transacting with ,to ensure and preserve my privacy, ensure my data's protected. So, that's a big shift that's happened, so whether you're doing it for regulatory reasons, CCPA North America, there's several other state-wide regulations coming out or GDPR, the consumer expectation, forget regulations, it's brand preservation, it's customer trust, it's customer experience, that organizations are really having to solve for from a privacy standpoint. >> Tell what the news around yesterday around the shift of the trust pieces, because that's a huge deal. Because trust is shifting, expectations are shifting, so when you have shifting expectations, with users and buyers, customers, the experience has to shift. So, take us through what's the new things? >> Well, the new things are, you know, you look at we're enabling organizations to be data-driven, we're enabling organizations to transform, build new products, new services, be more efficient and for that, you need to enable them to get access to data. The counter, the tension on the other end is how do we get them broad-based access while ensuring privacy, right, and that's the balance. How do we enable them to be customer-centric and optimal in engaging with their customers while preserving the privacy of their customers and that really comes down to having a detailed understanding of what your critical data is, where is it in the organization and how an organization is using that data. Enabling an organization to know that they're processing data with the appropriate consent. >> What's interesting to me, when I was with press yesterday, is also the addition of how the cloud players are coming onboard, because you know, one constituent that's not mentioned in that statement is that you guys are kind of keeping an eye on, that are impacted by this, is developers, because you know developers like infrastructures coded with DevOps. Don't want to be provisioning networks and storage, they just write to the API's. Data is kind of going through that similar experience where, if I'm a developer doing an IOT app, I'm just going to use the cloud. I put the data there, I don't need to have a mismatch of mechanisms to deal with some governance compliance rules. >> Correct and that's why it needs to be built-in by design. And you know there's this connotation that- >> Explain that, what does built-in by design mean. >> Well you need to have privacy built-into how you as a business operate, how you as a DevOps team or development team, build products, if that's built-in to how you operate, you enable the innovation without falling into the pitfalls of oh you know what we broke some privacy regulations there we breached our customers trust there, we used data or engaged with them in manner that they weren't comfortable with. >> So, don't retro-fit after the fact? Think holistically on the front-end of the transformation in architecture. >> It's an enabler, in that if you do it right to begin with, you can continue to innovate and engage effectively, versus bolting it on as an afterthought and retro-fitting. >> It really seems like it is this evolution in thinking from this risk and compliance, overdoing this to check all the boxes, versus here are our constraints, but our constraints are actually liberating, is what you're saying. >> Right, but you can't democratize data, without giving the consumers of that data an understanding of the quality of that data, the trustworthiness of that data, the relevance of the data to the business, you give them that and now you're enabling your analytics, your data scientists, your analytics organizations to innovate with that data with confidence and if you do it within a framework of privacy, you're ensuring that you're preserving customer trust while you're automating and building intelligent and engaging customer experiences. >> What I love about the data business right now, is it's exciting because it's real specific examples of impact, security, you know, national security, to hackers, to just general security, privacy of the laws, But, I've seen the development angles interesting too, so when you got these two things moving, customers can ignore this, it's not like back-up and recovery where same kind of ethos is there, you don't want to think about it after the fact, you want to build it in, you know, there's certainly reasons why you do that, in case there's a disaster, but data is highly impactful all the time. This is a challenge, you guys can pull this off. >> Well you know, it's a, with privacy, it's no longer about a few systems, it's all your data and so the scope is the challenge and the scale applies for privacy, the scale applies for making data available enterprise wide and that's where you need and you know we spoke about AI needs data, well data also needs AI. And that's where we're leveraging AI and ML. Building out intelligence, to help organizations solve that problem and not do it manually. >> You know, I've said it on theCUBE, you've probably heard it many times, I say it all the time, scale is the new competitive advantage. Value is the new lock-in. No proprietary software anymore, but technology is needed. I want to ask you, you've been talking about this with some of your customers last year around data is that you need more scale, because AI needs more access to data, because the more visibility into data, the smarter, machine learning and AI applications can become. So Scale is real. What is the, what are you, you guys have some scalosity in your customers, you got the end-to-end, got the catalog and everything is kind of looking good, but you have competition How would you compare to the competition, when people say hey Jitesh, a start-up just popped out or XYZ company's got the solution, why should I go with them or you? What's the difference, what's the competitive angle? >> You know, the way we're thinking the problem is founded on governance is an enabler it's not about locking things down for risk and compliance, because you know, the regulators want to know that this particular warehouse is highly tightly controlled, it's about getting the data out there, it's about enabling end-users to have a contextual understanding when you're doing that for all of your data, within around, that's a thousand X-increase in the data, it's a thousand X-increase in your constituents, you're not supporting, the risk and compliance portions of the organization, you're supporting marketing, you're supporting sales, you're supporting business operations, supply chain, customer-onboarding and so with the problem of scale, practices of the past, which were typically manual laborious, but hey at the risk of non-compliance, we just had to deal with them, don't practically in any way scale, to the requirements of the future which is a thousand X-increase in consumers and that's where intelligence and AI and ML come in. >> The question I have for you is, where should customers store their data? Is there an answer to that on premises or in the cloud? What are they doing? >> The answer is yes, (Knight laughs) the customer should store their data, what we see, the world is going to be hybrid, mainframes are still here, on-premise will still be here many years from now. >> So you're taking the middle of the road here, so >> There's Switzerland. >> You're saying whatever they want on-premise or cloud, is there a preference you see with customers? >> Well, you know it depends on the applications , depends on regulations, historically regulations especially in financial services, have mandated a more on-premise stance, but those regulations, are also evolving and so we see, the global investment banks all of a sudden, we're having all sorts of conversations about enabling them to move select portions of their data estate to the cloud, enabling them to be more agile, so the answer is yes and it will be for a very long time to come. >> Final question, one of the most pressing problems in the technology industry is the skills gap. I want to hear your thoughts on it, how as a Senior Executive at Informatica, how worried are you about finding qualified candidates for your open-roles? >> You know, it is a challenge, good news is, we're a global organization, my teams are globally-distributed. I have teams in Europe, North America and Asia and the good part about that is if you can't find it in the valley, you can certainly find the talent elsewhere, and so while, it is a challenge, we're able to find talented engineers, software developers, data scientists, to help us innovate and build the intelligence capabilities to solve the productivity challenges, the scale challenges of data consumption. >> Jitesh, talk about the skills required for people coming out of school, take your Informatica hat off, put your expertise hat on, data guru hat, knowing that data is going to continue to grow, continue to have more impact across the board, from coding to society affix, whatever, what are some of the key skills in training, classes or courses or areas of expertise that people an dial-up or dig into that might be beneficial to them that may or may not be on the radar curriculum or, say is, part of school curriculum, >> you know we engage with universities in North America, in Europe, in Asia, we have a large development center in India and we're constantly, engaging with them. We're on various boards at various universities, advisory standpoint, big data standpoint and what we're seeing is as we engage with these organizations, we're able to feed back on where the market is going, what the requirements are, the nature of data science, the enabling technologies such as platforms like Spark, languages like Python and so we're working with these schools to share our perspectives, they in turn, are incorporating this into their curriculums and how they train future data scientists. >> When you see a young gun out there that's kicking butt and taking names and data, what are some of the backgrounds? Is it math, is it philosophy, is there a certain kind of pattern that you've seen as the makeup of just the killer data person? >> You know, it's interesting, you mention philosophy, I'm a big, I've hired many philosophy majors that have been some of the best architects, having said that, from a data science perspective, it's all about stats, it's all about math and while that's an important skillset to have, we're also focused on making their lives easier, they're spending 70% of their time, doing data engineering versus data science and so while they are being educated from a stats, from a data science foundation, when they come into the industry, they end up spend 70% of their time doing data engineering, that's where we're helping them as well. >> So study your Socrates and study your stats. >> I like that. (Knight and Furrier laugh) >> Jitesh, thank you so much for coming on theCUBE. >> My pleasure, happy to be here, thank you. >> I'm Rebecca Knight for John Furrier, you are watching theCUBE.