 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 2019. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We are joined by Sadeer Hasby. He is the Director of Product Management at Google Cloud. Thank you so much for coming on theCUBE. You're a CUBE veteran. Thank you for inviting me. This morning we saw Thomas Kurian up on the main stage to announce the expanded partnership, big story in Wall Street Journal, Google Cloud and Informatica team up to tame data. Tell us more about this partnership. So if you take a look at the whole journey of data within organizations, a lot of data is still siloed in different systems within different environments. Could be a hybrid on-prem, it could be multi-cloud and all. And customers need this whole end-to-end experience where you can go ahead and take that data, move it to cloud, do data cleansing on it, do data preparation. You want to be able to go ahead and govern the data, know what data you have with like a catalog. So Informatica provides all of those capabilities. And if you look at Google Cloud, we have some highly differentiated services like Google BigQuery, which customers love across the globe to go ahead and use for analytics. We can do large-scale analytics. We have customers from few terabytes to 100 plus petabytes storing that amount of data in BigQuery, analyzing, getting value out of it. And from there, all the AI capabilities that we have built on top of it, this whole journey of taking data from wherever it is, moving it, cleansing it, and then actually getting value out of it with BigQuery as well as our AI capabilities, that whole end-to-end experience is what customers need. And with this partnership, I think we are bringing all the key components that our customers need together for a perfect fit. So here, first of all, great to see you. It's been since Google Next, we just got a great event by the way this year. Congratulations. Thanks. A lot of great momentum in the enterprise. Explain for a minute what is the relationship? What is the partnership? Just take a quick minute to describe what it is with Informatica that you're doing. Yeah, that's great. I think if you take a look at it, you can bring two key areas together in this partnership. There's data management. How do you get data into cloud? How do you govern it, manage it, understand it? And then there is analyze the data and AI. So the main thing that we're bringing together is these two capabilities. What do I mean by that? The two key components that will be available for our customers is the intelligent cloud services from Informatica, which will be available on GCP. It will run on GCP. This will basically make sure that the whole end-to-end capability for that platform, like data pipelines and data cleansing and preparation, everything is now available natively on GCP. That's one thing. What that will also do is Informatica team has actually optimized the execution as part of this migration. What that means is now you'll be able to use products like Data Cloud, Data Proc. You'll be able to use some of our AI capabilities and BigQuery to actually go do the data cleansing and preparation in- So you say, excuse me, running the software. Yeah, just running software. Executing good market, but executing software. Executing software. So if you have a data pipeline, you can literally leverage Data Proc underneath to go ahead and run some of the... And so the value to the customer is seamless. Seamless integration. Okay, so as you guys get more enterprise savings, clear that you guys are doing a good work and obviously Thomas has got the chops there. We've covered that on theCUBE many times. As you go forward, this cloud formula seems to be taking shape. Amazon, Azure, Google coming in, providing onboarding to cloud and vice versa. So those relationships. The customers are scratching their heads going, okay, where do I fit on that? So when you talk to customers, how do you explain that? Because unlike the old days in computer science and the computer industry, there was kind of known practices. You built a data center, you provisioned some servers, you did some things. It was kind of the general purpose formula. But every company's different. Their journey's different. Their software legacy makeup's different. You could be born in the cloud with on-prem compliance needs. So how do customers figure this out? What's the playbook? I think the big thing is this, right? There's a trend in the industry across the board to go ahead and be more data-driven, build a culture that is data-driven culture. And as customers are looking at it, what they're saying is, hey, traditionally I was doing a lot of stuff, managing infrastructure. Let me go build a data center. Let me buy machines. That is not adding that much value. It is cost. I need to go do that. That's why they did that. But the real value is if I can get the data, I can go analyze it, I can get better decisions from it. If I can use machine learning to differentiate my services, that's where the value is. So most customers are looking at it and saying, hey, so I know what I need to do in the industry now is basically go ahead and focus more on insights and less on infrastructure. But as doing this, the most important thing is, data is still, as you mentioned, siloed, it's different applications, different data centers still sitting in different places. And so I think what is happening with what we have announced today is making it easy to get that data into Google Cloud and then leveraging that to go ahead and get insights. That's where the focus is for us. And as you get more of these capabilities in the cloud as native services from Informatica and Google, customers can now focus more on how to derive value from the data and pulling the data into cloud, cleansing it and data preparation and all of that, that becomes easier. Okay, so that brings the solution question to the table. So with the solutions that you see with Informatica, because again, they have a broad space of horizontal across on-prem and cloud, and they have a huge customer base within the price of 25 years and big data is their thing. What use cases are kind of low-hanging fruit right now? Where are people putting their toe in the water? Where are they jumping full in? Where do you see the spectrum of solutions? Great question. There are two or three key scenarios that I see across the board with talking to a lot of customers. Even today, I spoke to a lot of customers at this show. And the first main thing I hear is this whole thing, modernization of the data warehousing and analytics infrastructure. Lot of data is still siloed and stuck into these different data systems that are there within organizations. And if you want to go ahead and leverage that data to build on top of the data, democratize it with everybody within the organization, or to leverage AI and machine learning on top of it, you need to unwind what you've done and just take that data and put it into cloud and all. So I think modernization of data warehouses and analytics infrastructure is one key play across the IT systems and IT. Before you go on to the next one, I want to just drill down on that because one of the things we're hearing obviously here and all over the place is that if you constrain the data machine learning and AI application ultimately fails. So legacy silos, you mentioned that, but also regulatory things. I got to have privacy now. I got to forget my customer GDPR, first year anniversary, new regulatory things around all kinds of data. And if it never mind outside the United States. So, but the cloud is appealing just throwing it in there as one thing. It's an agility lag issue because lagging is not good for AI. You want real time data. You need to have it fast. How does a customer do that? Is it best to store it in the cloud first, on-premise with mechanisms? What's your take on this? I think it's different in different scenarios. I talked to a lot of customers on this. Not all data is restricted from going anywhere, right? I think there are some data sets you want to be, like you know, have a good governance in place. For example, if you have PII data, if you have important customer information, you want to make sure that you take the right steps to govern it. You want to anonymize it. You want to make sure that the right amount of data per the policies within the organization only gets into the right systems. And I think this is where also the partnership is helpful because with Informatica, the tooling that they've provided, or as you mentioned over 25 years, allows customers to understand what these data sets are, what value they're providing. And so you can do anonymization of data before it lands into cloud and all of that. So I think one thing is the tooling around that, which is critical. And the second thing is if you can identify data sets that are in real time and they don't have like, you know, business critical or PII critical data that you're fine as a business process to be there, then you can get derived a lot of value in real time from all the data sets. Well, talk about Google's big capabilities because you know, you guys have a lot of internal power platform features. BigQuery's one of them. Is BigQuery the secret weapon? Is that the big, you know, power source for managing the data? I would just say our customers love BigQuery primarily because of the capability it provides. There are different capabilities. So let me just list a few. So one is we can do analytics at scale. So as organizations grow, even if data sets are small within organization, what I've seen is over a period of time when you derive a lot of value from data, you will start collecting more data within organization. So you have to think about scale. Whether you're starting with one terabyte or one petabyte or 100 petabytes, it doesn't matter. So like analyzing data at scale is what we are really good at, at different types of scale. Second is democratizing data. So we have done a good job of making data available through different tooling, existing tooling that customers have invested in, and our tooling to make it available to everybody. AirAsia is a good example. They've been able to go ahead and give right insights to everybody within the organization, which has helped them go, say, five to 10% in their operational cost. So that's one great example of just democratizing access to insights. The third big thing is machine learning and AI. We all know there are just lack of resources to do advanced analytics with AI and machine learning in the industry. And so our goal has been democratize it, make it easy within organization. So investments that we have done with BigQuery ML, where you can do machine learning with just simple SQL statements or auto ML tables, which basically allows you to just within a UI map and say, that's a table in BigQuery. Here are the columns. Here's a column that I want to predict and just automatically figure out what model you want to create. And then we can use neural networks to go do that. I think that kind of investments is what customers love about it from the platform side. What about the partnership from a particular functional part of the company marketing? There's the old adage, 50% of my marketing budget is wasted. I just don't know what's going on. This one could really change that because. Exactly right. So talk a little bit about the impact of it. I think the main thing is, if you think about the biggest challenge that CMOs have within organization is, how do you do better marketing analytics and optimize the spend? And so one of the things that we're doing with the partnership is not just breaking the silos, getting the data in BigQuery, all of that side and data governance. But another thing is with master data management capability that Informatica brings to table, now you can have all of your data in BigQuery. You leverage the customer 360 that MDM provides and now CMOs can actually say, hey, I have a complete view of my customer. I can do better segmentation. I can do better targeting. I can give them better service. And so that is actually going to derive a lot of value with our customers. I want to just touch on that. I want to see if I get this right. So what you just said, I think might be the question I was about to ask which is what is unique about Google's analytical portfolio with Informatica specifically? Because these are the cloud deals they have. They have Azure and AWS. What's unique about you guys and Informatica? Was it that piece? Yeah, I think there are a few things, right? One is the whole end to end experience of basically getting data, breaking the silos, doing data governance, this tight integration between our product portfolio where now you can get like a great experience within the native GCP environment. That's one. And then on the other side, Cloud for Marketing is a big, big initiative for us. How do you go ahead and, we work with like hundreds of thousands of customers across the globe on their marketing spend and optimizing their marketing. And this is one of the areas where we work and work together to go ahead and help those CMOs to get more value from their marketing investments. One of the conversations we're having here on theCUBE and really we're having in the technology industry is about the skills gap. I want to hear what you're doing at Google to tackle this problem. I think one of the big things that we're doing is just trying to, I have this theme internally in planning, I use radical simplicity. And radical simplicity is, how do we take things that we are doing today and make it extremely simple for the next generation of innovation that we're doing? So all the investments and BigQuery ML, like use SQL for mostly everything. We, one of the other things that we announced that next was this SQL for Dataflow SQL Pipelines. What that means is, instead of writing Beam or Java code to build Dataflow Pipelines, now you can write SQL commands to go ahead and create a whole pipeline. Similarly, machine learning with SQL. This whole aspect of simplifying, like you know, capabilities so that you can use SQL and then with AutoML, that's one part of it. And the second, of course, we are working with different partners to go ahead and have a lot of training that is available online where customers don't have to go take classes, like you know, traditional classes, but just go online. All the assets are available, examples are available. One of the big things in BigQuery we have is we have 70 plus public data sets where you can go with BigQuery Sandbox without credit card. You can start using it. You can start trying it out. You can use 70 plus data sets that are already available and start learning the products. So I think that should help drive more. Google's a real cultural tech company, so you guys obviously base that from Stanford. Very academic field, so you do hire a lot of smart people. But there's a lot of people graduating, middle school, high school, college. Berkeley just graduated their first inaugural class in data, science and analytics. What's the skill specifically that young kids or people who are either retraining should either reboot, hone or dial up? Is there any things that you see from people that are successful inside Google? I mean, sometimes you don't have to have that traditional math background or computer science, although math does help. It's key, but what is your observation? What's your personal view on this? I think the biggest thing I have noticed is the passion for data. I fundamentally believe that in next three to five years, most organizations will be driven with data and insights. Machine learning and AI is going to become more and more important. So this understanding and having the passion for understanding data, answering questions based on data is the first thing that you need to have. And then you can learn the technologies and everything else. They will become simpler and easier to use, but the key thing is this passion for data and having this data-driven decision making is the biggest thing. So my recommendation to everybody who is going to college today and learning is go learn more about how to make better decisions with data, learn more about tooling around data, focus on data, and then I think everything is going to- It's like an athlete. If you're not at the gym shooting hoops, if you don't love it, if you're not living it, you're probably not going to be any. It's kind of like that. City, thank you so much for coming on theCUBE. It's a pleasure talking to you. Thank you, thanks a lot for having me. I'm Rebecca Knight for John Furrier. You are watching theCUBE.