 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 Jatesh Guy. He is the Senior Vice President and General Manager Data Quality, Security and Governance at Informatica. Thank you so much for returning to the show, Jatesh. 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 Davos. 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 this 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 and what is critical data. And while it was born to give the regulators comfort, what organizations have 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 we've 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 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 KPIs they're getting. One of the things that we talked last year, you had a couple customers on, you had Toyo had a great story. You guys have had the benefit as a longstanding company 25 years, but private before, large customer base. But the markets changed, you mentioned governance. I mean, we're in the one year anniversary of GDPR, and I think everyone's kind of like, okay, 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, access to data, making data addressable because AI needs data sets. Cloud has proven that SaaS business models using data, winning formula, that's clear if you're born in the cloud. Enterprises now want that same kind of SaaS-like execution on the application side, whether it's SaaS or using AI, for instance. So when you have more regulation, inherent nature, it's like, oh, more complexity. How are customers dealing with the complexity of this? Because they want to free it up. 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, there's a balance there. What's the strategy? And there analyze 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. It'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 their sensitive information? How is their 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're giving us as individuals rights to go to the organizations we transact with and ask them, what are you doing with our data? Forget my data. Or at least tell me how you're processing it and get my consent for the data. Policy and business models are certainly driving that and with regulation, I see that. But the question is that when you move to impact to the enterprise, you got storage drives. You store them in drives as a storage administrator. You 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 and 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 may start knowing where their sensitive data is, but they 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, you know, of a particular organization blame the organization for the breach, not the hackers, right? So it's a mind shift in all of us where you're the custodian of my data. I'm counting on you, whoever, whatever organization I'm transacting with to ensure and preserve my privacy, ensure my data is protected. So that's a big shift that's happened. So whether you're doing it for regulatory reasons, DCPA in North America, there's several other statewide 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 us about the news around yesterday around the shift of the trust pieces. That's a huge deal, because trust is shifting and expectations are shifting. So when you have shifting expectations with users and buyers, customers, the experience has to shift. So to take us through, what's the new things? 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 it is in the organization, and how an organization is using that data. Having ensuring, enabling an organization to know that they're processing data with the appropriate consent. It was interesting to me, and what I was impressed with yesterday is also the addition of how the cloud players are coming on board. Because, you know, one constituent that's not mentioned in that statement that you guys are kind of keeping an eye on that are impacted by this is developers. Because, you know, developers, you know, like infrastructure as code with DevOps don't want to be provisioning networks and storage. They just write to the APIs. Data's kind of going through that similar experience where, from a developer doing an IoT app, I'm just going to use the cloud. I'm going to put the data there. I don't need to have a mishmash 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. Explain that. What is built in by design? Well, you need to have privacy built into how you as a business operate. How you as a DevOps team or a development team build products. If that's built into 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 customer's trust there. We used data or engaged with them in a manner that they weren't comfortable with. So don't retrofit 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 retrofitting. 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 the constraints are actually liberating. Right. That's what you're saying. You can't democratize data without giving the consumers of that data and understanding of the quality of that data, the trust worthiness 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'm about the data business right now is that it's exciting because there's real specific examples of impact, security, you know, national security to hackers, to just general security, privacy, other laws. But obviously the development angle is interesting too. So when you've got these two things moving, customers can't ignore this. It's not like backup 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, that's certainly there's reasons why you do that in cases of 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 scale 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 on theCUBE, you've probably heard it many times. I say it all the time. Scale is the new competitive advantage. Lock-in, value is the new lock-in. It was no proprietary software anymore, but technology is needed. I want to ask you, we talked about this with some of your customers last year around data, is that if you need more scale, because AI needs more access to data, because more visibility into data, the smarter machine learning and AI application can become. So scale is real. What is the, what are you, I mean, you guys have some scale, I see customers and you got the end to end, you got the catalog, everything kind of looking good. But you have competition. How are you compared to the competition? When people say, hey, you test, you know, the startup 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 of the problem is founded on governance as 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 a room, 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, the practices of the past, which were typically manual, laborious, but hey, at the risk of non-compliance, you just had to deal with them. You 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 comes in. So 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. The customer should store their data. What we see is 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, yeah. So where are they going? Is what you're saying? So you're saying, where are they going? 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 more of an on-premise stance, but those regulations are also involving. And so we see 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. The good news is we're a global organization. My teams are globally distributed. I have teams in Europe, North America, in 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. First, take your Informatica hat off, put your expertise hat on, data, guru hat. Knowing that data's going to continue to grow, continue to have more impact across the board from coding to society, ethics or whatever. What are some of the key skills in training classes or courses or areas of expertise that people can dial up or dig into that might be beneficial to them that may or may not be on the radar curriculum or as part of, say, school curriculum? You know, well, 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 from a 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, you know, philosophy? I mean, is there a certain kind of a pattern that you're seeing that as the makeup of just the killer data person? You know, it's interesting you mentioned 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 skill set 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 spending 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, I like that. Chetesh, 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.