(gentle music) >> Okay. We're back at AWS re:Invent. You're watching theCUBE's continuous coverage. This is day four. Think it's the first time, at re:Invent, we've done four days. This is our ninth year covering re:Invent. Tom Miller is here. He's the senior vice president of alliances. And he's joined by Ankur Jain, who's the global cloud practice lead at Merkle. Guys, good to see you. Thanks for coming on. >> Good to see you. Thank you. >> Thank you. >> Tom, tell us about Merkel, for those who might not be familiar with you. >> Yeah. So, Merkle is a customer experience management company that is under the dentsu umbrella. Dentsu is a global media agency. We represent one of the pillars, which is customer experience management. And they also have media and creative. And what Merkle does is provide that technology to help bring that creative and media together. >> So you're a tech company? >> Yes. >> Right? Okay. So there's some big tailwinds, changes, trends going on in the market. Obviously, the pandemic, the forced march to digital, there's regulation. What are some of the big waves that you guys are seeing, that you're trying to ride? >> So what we're seeing is, as a start, we've got a lot of existing databases with clients that are on-prem, that we manage today, within a SQL environment or so forth. And they need to move that to a cloud environment. To be more flexible, more agile, provide them with more data, be able to follow that customer experience that they want with their clients, that they're all realizing they need, to be in a digital environment. And so, that's a big push for us working with AWS and helping move our clients into that cloud environment. >> And you're relatively you new to the AWS world, right? Maybe you can talk about that, Ankur. >> Well, actually, as a partner, we may be new. But Merkle has been working with AWS for over five years. >> Dave Vellante: As a customer? >> As a customer. >> Yeah. >> So what we did was, last year, we formalized the relationship with AWS to be an advanced partner now. So we are part of the re:Stack program, basically, which is a pool of very select partners. And Merkel comes in with the specialization of marketing. So, as Tom said, you know, we are part of a dentsu umbrella. Our core focus is on customer experience transformation. And how we do that customer experience transformation is through digital transformation, data transformation. And that's where we see AWS being a very good partner to us, to modernize the solutions that Merkle can take to the market. >> So, I mean, your on-prem databases, there's probably a lot of diversity on-prem. (laughs) A lot of tech... When the cloud, you know, more agility, infinite resources. Do you have a tech stack? Are you more of an integrator? Right tool for the right job? Maybe you could describe your technical philosophy. >> Yeah, I could take that. What Tom just described... So let me give you some perspective on what these databases are. These databases are, essentially, Merkle helping big brands, Fortune 100, Fortune 500 brands to modernize their marketing ecosystem. Especially, MarTech ecosystem. So these databases, they house customer touchpoints, customer data from disparate sources. And they, basically, integrate that data in one central place. And then bolt-on analytics, data science, artificial intelligence, machine learning, on top of it. Helping them with those email campaigns or direct mail campaigns, social campaigns. So that's what these databases are all about. And these databases, currently, sit on-prem, on Merkle's own data center. And we have a huge opportunity to kind of take those databases and modernize them. Give all these AI, ML type of capabilities, advanced analytic capabilities, to our customers by using AWS as the platform to kind of migrate that. >> Dave Vellante: And you do that as a service? >> We do that as a service. >> Yes. >> Yes. >> Strategically, >> Yes. >> you're sort of transforming your business- >> Yes. >> to help your customers transform their business. >> Right. >> Right? Take away, it's classic. I mean, it's happening. This theme of, you know, AWS started with taking away the undifferentiated heavy lifting for infrastructure. Now you're seeing Nasdaq, Goldman Sachs, you guys in the media world, essentially building your own clouds, right? That's the strategy. >> Yes. >> Yes. >> Right? >> Absolutely. >> Superclouds, we call 'em. >> Superclouds, yeah. (Dave laughs) It's about helping our clients understand what is it they're trying to accomplish. And, for the most part, they're trying to understand the customer journey, where that customer is, how they're driving that experience with them, and understanding that experience through the journey. And doing that in the cloud makes it tremendously easier and more economical for 'em. >> Yeah, I was listening to the Snowflake earnings call from last night. And they were talking about, you know, a couple of big verticals, one being media. And all they keep talking about was direct-to-consumer, right? You're hearing that a lot. >> Ankur Jain: Yes. >> Media companies want to interact and build community directly. They don't want to necessarily, I mean, you don't want to go through a third-party anymore, if you don't have to. Technology's enabling that, right? Is that, kind of, the play here? >> Yes. Direct-to-consumer is a huge play. Companies which were traditionally brick-and-mortar-based, or relied on a supply chain of dealers and distributors, are now, basically, transforming themselves to be direct-to-consumer. They want to sell directly to the consumer. Personalization becomes a big theme, especially in D2C type of environment. Because, now, those customers are expecting brands to know what's their like, what's their dislike, which products, which services are they interested in. So that's all kind of advanced analytics, machine learning powered solutions. These are big data problems, that all these brands are kind of trying to solve. That's where Merkle is partnering with AWS, to bring all those technologies, and build those next generation solutions for our customers. >> So what kind of initiatives are you working on with AWS? >> So, there are, like, three, four areas that we are working very closely with AWS. Number one, I would say, think about our marketer's friend. You know, and they have a transformation like direct-to-consumer, omnichannel, e-commerce, these type of capabilities in mind. But they don't know where to start. What tools, what technologies will be part of that ecosystem. So that's where Merkle provides consulting services. To give them a roadmap, give them recommendations on how to structure these big, large strategic initiatives. That's number one, we are doing in partnership with AWS. To reach out to our joint customers and help them transform those ecosystems. Number two, as Tom mentioned, migrations. You know, helping chief data officers, chief technology officers, chief marketing officers modernize their environment, by migrating them to cloud. Number three, Merkle has a solution called Merkury, which is essentially all about customer identity. How do we identify a customer across multiple channels? We are modernizing all that solution, making that available on AWS Marketplace for customers to, actually, easily use that solution. And number four, I would say is, helping them set up data foundation. That's through intelligent marketing data lake. You know, leveraging AWS technologies like Glue, Redshift, and actually modernize their data platforms. And number four is more around clean rooms. Which is, bring on your first-party data, join it with Amazon data, to see how those customers are behaving when they are making a purchase on Amazon.com. Which gives insight to these brands, to reshape their marketing strategy to those customers. So those are, like, four, five focus areas. >> No, it's good. So, I was going to ask you about the data and the data strategy. Like, who owns the data? You're kind of alchemists, that... Your clients have first-party data. >> Ankur Jain: Yes. >> And then you might recommend bringing in other data sources. >> Yes. >> And then you're sort of creating this new cocktail. Who owns the data? >> Well, ultimately, client owns the data, because that's their customer's data. To your point on, we help them enrich that data by bringing in third-party data, which is what we call as... So Merkle has a service called DataSource, which is essentially a collection of data that we acquire about customers. Their likes, their dislikes, their buying power, their interests. So we monetize all that data. And the idea is, to take those data assets and make them available on AWS Data Exchange. So that it becomes very easy for brands to use their first-party data, take this third-party data from Merkle, and then, segment their customers much more intelligently. >> And the CMO is your sort of ideal customer profile? >> Yeah. CMO is our main customer profile. And we'll work with the chief data officer, or we'll work with the chief technology officer. We bridge both sides. We can go technology and marketing, and bring them both together. So you have a CMO who's trying to solve for some type of issue. And you have a chief technology officer who wants to improve their infrastructure. And we know how to bring them together into a conversation and help both parties get what they want. >> And I suppose the chief digital officer fits in there too? >> Tom Miller: Yeah, he fits in there too. >> CGO, chief dig. officer, CMO. Sometimes, they're one in the same. Other times, they're mixed. >> Yep. Yep. >> I've seen CIOs and CDOs together. >> Yes. >> Sure. >> It's all data. >> It's all data. (Dave laughs) >> Yeah. Some of the roles that come into play, as Tom mentioned, and you mentioned, CIO, CTO, chief information officer, chief technology officer, chief data officer, more from the IT side. And then we have the CMOs, chief digital officers, from the marketing side. So the secret sauce that Merkle brings to the table is that we know the language, what IT speaks and what business speaks. So when we talked about the business initiatives, like direct-to-consumer, omnichannel, e-commerce, those are more business-driven initiatives. That's where Merkle comes in, to kind of help them with our expertise over the last 30 years, on how to run these strategic initiatives. And then, at the same time, how do we translate those strategic initiatives into IT transformation? Because it does require a lot of IT transformation to happen underneath. That's where AWS also helps us. So we kind of span across both sides of the horizon. >> So you've got data, you've got tools, you've got software, you've got expertise, that now, you're making that available as a service. Is that right? >> That's right. Yes. >> Yes. >> How far are you into that journey, of saasfying your business? >> Well, the cloud journey started almost, I would say, five to seven years ago at Merkle. >> Yeah. Where you began leveraging the cloud? >> That's right. >> Dave Vellante: And then the light bulb went off and- >> So cloud, again, we use cloud in multiple aspects. From general computing perspective, leveraging, you know, fully managed services that AWS offers. So that's one aspect, which is to bring in data from disparate sources, house it, analyze it, and derive intelligence. The second piece, on the cloud side, is SaaS offering, Software as a Service offerings, like Adobe, Salesforce, and other CDP platforms. So Merkle covers a huge spectrum, when it comes to cloud. >> And you got a combination, you have a consulting business, and also- >> So Merkle has multiple service lines. Consulting business is one of them. Where we can help them on how to approach these transformational initiatives, and give them blueprints and roadmaps and strategy. Then we can also help them understand what the customer strategy should be, so that they can market very intelligently to their end customers. Then we have a technology business, which is all about leveraging cloud and advanced analytics. Then we have a data business, the data assets that I was talking about, that we monetize. We have promotions and loyalty, we have media. So we cover multiple services. >> Dave Vellante: Quite a portfolio. >> Yes. >> You mentioned analytics a couple of times, how do you tie that back to the sales function? I would imagine your clients are increasingly asking for analytics, so they can manage their dashboards and make sure they're above the line. How is that evolving? >> Yeah. So that's a very important line. Because, you know, data is data, right? You bring in the data, but what you do with the data, how you ask questions and how you derive intelligence from it, because that's the actionable part. So, few areas. I'll give you one or two examples on how those analytics kind of come into picture. Let's imagine a brand which is trying to sell a particular product or a particular service to a set of customers. Now, who those set of customers are, you know, where they should target this, who their target customers are, what their demographics are, that's all done through analytics. And what I gave you is a very simple example. There are so many advanced examples, you know, that come into artificial intelligence, machine learning, those type of aspects as well. So analytics definitely play a huge role on how these brands need to sell, and personalize the offerings that they want to offer to the customers. >> Used to be, really, pure art, right? It's really becoming- >> Not any more, it's all data driven companies. (Tom laughs) >> It's "Moneyball." >> Yes. Exactly. (Dave laughs) >> Tom Miller: Exactly. >> There's, maybe, still a little bit of art in there, right? It doesn't hurt to have a little creative flair, still. >> Yes. >> But you got to go with the data. >> And that's where the expertise comes in, right? That's where the experience comes in. And how you take that science and combine it with the art, to present it to a end customer, that's exactly, you know, it's a combination. >> And we also take the time to educate our clients on how we're doing it. So it's not done in a black box, so they can learn and grow themselves. Where they may end up developing their own group to handle it, as opposed to outsourcing with Merkle. >> You got to teach 'em how to fish. Last question. Where do you see this in two to three years? Where do you want to take? >> I think future is cloud, AWS being the market leader. I think AWS has a huge role to play. We are very excited to be partners with AWS, I think it's a match made in heaven. AWS sales in, majority of the sales happen in IT. Our focus is marketing. I think if we can bring both the worlds together, I think that will be a very powerful story for us to tell. >> Yeah, that's good news for AWS. If a little of your DNA could rub off on them, it'd be good. >> Tom Miller: Yeah. >> Guys, thanks so much for coming to theCUBE. >> Thanks, Dave. >> It was great to see you. >> Thank you, Dave. >> Appreciate it. >> All right. Thank you for watching everybody. This is Dave Vellante, for theCUBE. Day four, AWS re:Invent. We're theCUBE, the global leader in high-tech coverage. Be right back. (gentle music)

>>Okay, We're back at AWS Re. Invent. You're watching the >>cubes. Continuous coverage >>coverage. This is Day four. I think it's the first time it reinvent. We've done four days. This is our ninth year covering Reinvent. Tom Miller is here is the senior vice president of Alliances. And he's joined by Anchor Jane. Who's the global cloud? Practically practise lead at Merkel. Guys, good to see you. Thanks for coming on. Thank you, Tom. Tell us about Merkel. For those who might not be familiar with you. >>So Merkel is a customer experience management company. That is, um, under the Dentsu umbrella. Dense. Who is a global media agency? We represent one of the pillars which is global, our customer experience management. And they also have media and creative. And what Merkel does is provide that technology to help bring that creative and media together. They're a tech company. Yes. >>Okay, so there's some big big tail winds, changes, trends going on in the market. Obviously the pandemic. You know, the force marched to digital. Uh, there's regulation. What are some of the big waves that you guys are seeing that you're trying to ride? >>So what we're seeing is, uh we've got, uh, as a start. We've got a lot of existing databases with clients that are on Prem that we manage today within a sequel environment or so forth. And they need to move that to a cloud environment to be more flexible, more agile, provide them with more data to be able to follow that customer experience that they want with their clients, that they're all realising they need to be in a digital environment. And so that's a big push for us working with AWS and helping move our clients into that cloud environments. >>And you're relatively new to the ws world, right? Maybe you can talk >>about that anchor actually, as a partner. We may be new, but Merkel works with AWS has been working with AWS for over five years as a customer as a customer. So what we did was last year we formalise the relationship with us to be, uh, an advanced partner now. So we were part of the restock programme, basically which is a pool of very select partners. And Merkel comes in with the specialisation of marketing. So as Tom said, you know, we're part of, uh Dentsu umbrella are our core focuses on customer experience, transformation and how we do that Customer experience. Transformation is through digital transformation, data transformation. And that's where we see AWS being a very good partner to us to modernise the solutions that Martin can take to the market. >>So your on Prem databases is probably a lot of diversity on a lot of technical that when the cloud more agility, infinite resources do you have a tech stack? Are you more of an integrator? Right tool for the right job? Maybe you could describe >>your I can take that what time just described. So let me give you some perspective on what these databases are. These databases are essentially Markle, helping big brands 1400 Fortune 500 brands to organise their marketing ecosystem, especially Martek ecosystem. So these databases, they house customer touchpoints customer customer data from disparate sources, and they basically integrate that data in one central place and then bolt on analytics, data science, artificial intelligence, machine learning on top of it, helping them with those email campaigns or direct mail campaigns, social campaigns. So that's what these databases are all about, and and these databases currently set on Prem on Merkel's own data centre. And we have a huge opportunity to kind of take those databases and modernise them. Give all these ai ml type of capabilities advanced analytic capabilities to our customers by using AWS is the platform to kind of migrate. And you do that as a service. We do that as a service. >>Strategically, you're sort of transforming your business to help your customers transform their business right? Take away. It's it's classic. I mean, you really it's happening. This theme of, you know a W started with taking away the undifferentiated heavy lifting for infrastructure. Now you're seeing NASDAQ. Goldman Sachs. You guys in the media world essentially building your own clouds, right? That's the strategy. Yes, super clouds. We call >>them Super Cloud. Yeah, it's about helping our clients understand What is it they're trying to accomplish? And for the most part, they're trying to understand the customer journey where the customer is, how they're driving that experience with them and understanding that experience through the journey and doing that in the cloud makes it tremendously easier and more economical form. >>I was listening to the, uh, snowflake earnings call from last night and they were talking about, you know, a couple of big verticals, one being media and all. I keep talking about direct direct to consumer, right? You're hearing that a lot of media companies want to interact and build community directly. They don't want to necessarily. I mean, you don't want to go through a third party anymore if you don't have to, Technology is enabling that is that kind of the play here? >>Yes, Director Consumer is a huge player. Companies which were traditionally brick and mortar based or relied on a supply chain of dealers and distributors are now basically transforming themselves to be direct to consumer. They want to sell directly to the consumer. Personalisation comes becomes a big theme, especially indeed to see type of environment, because now those customers are expecting brands to know what's there like. What's their dislike? Which products which services are they interested in? So that's that's all kind of advanced analytics machine learning powered solutions. These are big data problems that all these brands are kind of trying to solve. That's where Merkel is partnering with AWS to bring all those technologies and and build those next generation solutions for access. So what kind >>of initiatives are you working >>on? So there are, like, 34 areas that we are working very closely with AWS number one. I would say Think about our marketers friend, you know, and they have a transformation like direct to consumer on the channel e commerce, these types of capabilities in mind. But they don't know where to start. What tools? What technologies will be part of that ecosystem. That's where Merkel provides consulting services to to give them a road map, give them recommendations on how to structure these big, large strategic initiatives. That's number one we are doing in partnership with AWS to reach out to our joint customers and help them transform those ecosystems. Number two as Tom mentioned migrations, helping chief data officers, chief technology officers, chief marketing officers modernise their environment by migrating them to cloud number three. Merkel has a solution called mercury, which is essentially all about customer identity. How do we identify a customer across multiple channels? We are Modernising all that solution of making that available on AWS marketplace for customers to actually easily use that solution. And number four, I would say, is helping them set up data foundation. That's through intelligent marketing Data Lake leveraging AWS technologies like blue, red shift and and actually modernise their data platforms. And number four is more around clean rooms, which is bring on your first party data. Join it with Amazon data to see how those customers are behaving when they are making a purchase on amazon dot com, which gives insights to these brands to reshape their marketing strategy to those customers. So those are like four or five focus areas. So I was >>gonna ask you about the data and the data strategy like, who owns the data? You're kind of alchemists that your clients have first party data and you might recommend bringing in other data sources. And you're sort of creating this new cocktail. Who owns the data? >>Well, ultimately, client also data because that that's their customers' data. Uh, to your point on, we helped them enrich that data by bringing in third party data, which is what we call is. So Merkel has a service called data source, which is essentially a collection of data that we acquire about customers. Their likes, their dislikes, their buying power, their interests so we monetise all that data. And the idea is to take those data assets and make them available on AWS data exchange so that it becomes very easy for brands to use their first party data. Take this third party data from Merkel and then, uh, segment their customers much more intelligently. >>And the CMO is your sort of ideal customer profile. >>Yeah, CMO is our main customer profile and we'll work with the chief data officer Will work with the chief technology officer. We kind of we bridge both sides. We can go technology and marketing and bring them both together. So you have a CMO who's trying to solve for some type of issue. And you have a chief technology officer who wants to improve their infrastructure. And we know how to bring them together into a conversation and help both parties get both get what they want. >>And I suppose the chief digital officer fits in there too. Yeah, he fits in their CDOs. Chief Digital officer CMO. Sometimes they're all they're one and the same. Other times they're mixed. I've seen see IOS and and CDOs together. Sure, you sort of. It's all data. It's all >>day. >>Yeah, some of the roles that come into play, as as Tom mentioned. And you mentioned C I o c T. O s chief information officer, chief technology officer, chief data officer, more from the side. And then we have the CMOS chief digital officers from the marketing side. So the secret sauce that Merkel brings to the table is that we know the language, what I t speaks and what business speaks. So when we talk about the business initiatives like direct to consumer Omni Channel E commerce, those are more business driven initiatives. That's where Merkel comes in to kind of help them with our expertise over the last 30 years on on how to run these strategic initiatives. And then at the same time, how do we translate translate those strategic initiatives into it transformation because it does require a lot of idea transformation to happen underneath. That's where AWS also helps us. So we kind of span across both sides of the horizon. >>So you got data. You've got tools, you've got software. You've got expertise that now you're making that available as a as a service. That's right. How far are you into that? journey of satisfying your business. >>Well, the cloud journey started almost, I would say, 5 to 7 years ago at Merkel, >>where you started, where you began leveraging the cloud. That's right. And then the light bulb went off >>the cloud again. We use clouds in multiple aspects, from general computing perspective, leveraging fully managed services that AWS offers. So that's one aspect, which is to bring in data from disparate sources, house it, analyse it and and derive intelligence. The second piece on the cloud side is, uh, SAS, offering software as a service offerings like Adobe Salesforce and other CDP platforms. So Merkel covers a huge spectrum. When it comes to cloud and you got >>a combination, you have a consulting business and also >>so Merkel has multiple service lines. Consulting business is one of them where we can help them on how to approach these transformational initiatives and give them blueprints and roadmaps and strategy. Then we can also help them understand what the customer strategy should be, so that they can market very intelligently to their end customers. Then we have a technology business, which is all about leveraging cloud and advanced analytics. Then we have data business that data assets that I was talking about, that we monetise. We have promotions and loyalty. We have media, so we recover multiple services portfolio. >>How do you mentioned analytics a couple times? How do you tie that? Back to the to the to the sales function. I would imagine your your clients are increasingly asking for analytics so they can manage their dashboards and and make sure they're above the line. How is that evolving? Yes, >>So that's a very important line because, you know, data is data, right? You bring in the data, but what you do with the data, how you know, how you ask questions and how you derive intelligence from it? Because that's the actionable part. So a few areas I'll give you one or two examples on how those analytics kind of come into picture. Let's imagine a brand which is trying to sell a particular product or a particular service to the to a set of customers Now who those set of customers are, You know where they should target this, who their target customers are, what the demographics are that's all done through and analytics and what I gave you is a very simple example. There are so many advanced examples, you know, that come into artificial intelligence machine learning those type of aspects as well. So analytics definitely play a huge role on how these brands need to sell and personalised the offerings that they're going to offer to. The customers >>used to be really pure art, right? It's really >>not anymore. It's all data driven. Moneyball. Moneyball? >>Yes, exactly. Exactly. Maybe still a little bit of hard in there, right? It doesn't hurt. It doesn't hurt to have a little creative flair still, but you've got to go with the data. >>That's where the expertise comes in, right? That's where the experience comes in and how you take that science and combine it with the art to present it to the end customer. That's exactly you know. It's a combination, >>and we also take the time to educate our clients on how we're doing it. So it's not done in a black box, so they can learn and grow themselves where they may end up developing their own group to handle it, as opposed to outsourcing with Merkel, >>teach them how to fish. Last question. Where do you see this in 2 to 3 years. Where do you want to take it? >>I think future is Cloud AWS being the market leader. I think aws has a huge role to play. Um, we are very excited to be partners with AWS. I think it's a match made in heaven. AWS cells in, uh, majority of the sales happen in our focus is marketing. I think if we can bring both the worlds together, I think that would be a very powerful story for us to be >>good news for AWS. They little your DNA can rub off on them would be good, guys. Thanks so much for coming to the Cube. Thank you. All right. Thank you for watching everybody. This is Dave Volonte for the Cube Day four aws re invent. Were the Cube the global leader in high tech coverage? Right back. Mhm. Mhm. Mhm.

>>from >>around the globe, >>it's the >>Cube with digital coverage of IBM. Think 2021 brought to you by IBM >>Well, good to have you here on the cube. We continue our conversations here as part of the IBM think initiative. I'm john Walsh your host here on the cube, joined today by Marco Novakovich, who is the co founder and Ceo of an stana, which is an IBM company they specialize in enterprise observe ability for cloud native applications. And Merkel joins us all the way from Germany near cologne Germany. Merkel good to see you today. How are you doing >>and good. Hi john nice to be here, >>you bet, thank you for taking the time today. Well, first off, let's just let's just give some definitions here. Enterprise observe ability. Um what is that? What are we talking about here? >>Yes. So observe ability is basically the next generation of monitoring, which means it provides data from a system from an application to the outside so that people from the outside can basically judge what's happening inside of an application. So think about your a big e commerce provider and you are, you have your shop application and it doesn't work, observe ability, gives you the ability to really deep dive and see all the relevant metrics, logs, uh, and, and application flows to understand why something is not working as you would expect. >>So if I'm just listening to this, I think, okay, I'm I'm monitoring my applications already right. I've got a PM and force and, and I kind of know what things are going on, what's happening, where the hiccups are all that, how, what is the enhancement here than in terms of observe ability taking it sounds like you're kind of taking a P. M. To a much higher level. >>Absolutely. I mean that's essentially how you can think about it and, and, and we see three things that really make us and stana and enterprise observe ability different. And number one is automation. So the way we gather this information is fully automated, so you don't have to configure anything. We get inside of your code, we analyze the flow up the application, we get the errors, the logs and the metrics fully automatic. And the second is getting context. One of the problems with monitoring is that you have all these monitoring data silos. So you have metrics on the one side locks in a different tool. What we build is a real context. So we tie those data automatically together so that you get real information out of all the data. And and and the third is that we provide actions. So basically use ai to figure out what the problem is and then automate things. Is it a problem resolution restarting a container or resizing your cloud? That's what we suggest automatically out of all the contacts and data that we've gathered. >>So you talk about automation context intelligence, you combine all that in one big bundle here, then basically um that's a big bundle, right? I'm not a giant vacuum if you will. You're ingesting all this information, you're looking for performance metrics. So you're trying to find problems um what's the complexity of tying all that together instead of keeping those functions separate? Um you know what or and what's the benefit to having all that kind of under one roof then? >>Yeah. So from the complexity point of view for the end customer, it's really easy because we do it automated for us as a vendor building this, it's super complex but we wanted to make it very easy for the user and I would say the benefit is that you get, we call it the mean time to repair, like the time from a problem to resolve the problem gets significantly reduced because normally you have to do that correlation of data manually and now with that context you get this automated by a machine and we even suggest you these intelligent actions to fix the problem. >>So so I'm sorry go ahead. >>Yeah. And by the way, one of the things why IBM acquired us and why we are so excited working together with IBM is the combination of that functionality with something like what's in a I ops because as I said, we are suggesting an action and the next step is really fully automating uh this action with something like what's new Ai Ops and the automation functionality that IBM has so that the end users are not only gets the information what to do the machine even does and fix the problem automatically. >>Mm Well, I'm wondering to just about about the kind of the volume that we're dealing with these days in terms of software capabilities and data, uh you've got obviously a lot more inputs, right, a lot more interaction going on, a lot more capabilities. Uh You've got apps uh they're kind of broken down the microservices now, so I mean you've got you got a lot more action basically, right, You've got a lot more going on and and um and what's the challenge to not only keeping up with that, but also building for the future for building for different kinds of capabilities and different kinds of interactions that maybe we can't even predict right now. >>Absolutely, yeah. So uh I'm 20 years in that space. And when I started, as you said, it was a very simple system. Right? You had an application server like web sphere, maybe a DB two database. So that was your applications like today. Applications are broken down and hundreds of little services that communicate with each other. And you can imagine if, if something breaks down in a system where you have two or three components, it's maybe not easy, but it's handled by a human to figure out what the problem is, if you have 1000 pieces that are somehow interconnected and something is broken. It is really hard to figure that out. And that's essentially the problem uh that we have to solve with the contacts with the automation, with ai to figure out how all these things are tied together and then analyze automatically for the user where issues are happening. And and and by the way, that's that's also when you look into the future, I think things will get more and more complicated. You can see now that people break down from micro service into functions. We get more serverless. We got to get more into a hybrid cloud environment where you operate on premise and in multiple clouds. So things get more complex, not less complex. From an architectural perspective, >>you bring up clouds to is this diagnostic I mean or do you work with a an exclusive cloud provider or you open for business? Basically >>we are open for business but but we have to support the different cloud technologies. So we support all the big public cloud vendors from, from IBM to amazon google Microsoft. But on the other hand, we see with enterprises Maybe there is 10 20 of the workload in the public cloud, but the rest is still on premises. And there's also a lot of legacy. So you have to bring all this together in one view and in one context. And that's one of the things we do. We not only support the modern cloud native applications, we also support the legacy on premise world, so that we can bring that together and that helps customer to migrate. Right? Because if they understand the workload in the on premise world, it's easier to transform that into a cloud native world. But it also gives an end to end view from the end user to we we always say from mobile to mainframe, right from a mobile app down to the mainframe application. We can give you an end to end view. >>Yeah, you talk about legacy uh in this case it may be cloud services that people use but there but you know, a lot of these legacy applications right to that are running that that are, they're still very useful and still highly functional, but at some point they're not going to be so would it be easier for you or what do you do in terms of talking with your clients in terms of what do they leave behind? What do they bring with them? How what kind of transition time frames should they be thinking about? Because I don't think you want to be supporting forever. Right. I mean, you you want to be evolving into newer, more efficient services and solutions and so you've got to bring them along too. I would think. Right. >>Yeah. But to be really honest, I think there are two ways of thinking. One is as as a vendor, you would love to support only the new technologies and don't have to support all the legacy technologies. But on the other hand, the reality is especially in bigger enterprises, you will find everything in every word. Right? And so if you want to give a holistic D view into the application stacks, you have to support also the older legacy parts because they are part of the business critical systems of the customer. And yes, we suggest to upgrade and go into a cloud native world. But being realistic, I think for the next decade We will have to live with a world where you have legacy and new things working together. I think that's just the reality. And in 10 years, what is new today is legacy then? Right. So we'll always, we will always live in a kind of hybrid world between legacy and and new things. >>Yeah, you got this technological continuum going on right. That you know that you know what's new and shiny today is going to be, you know, old hat in five years. But that's the beauty of it all. Obviously you talked about Ai Ops. Um, I mean let's go into that relationship a little bit if you would. I mean eventually what is observe ability set you up to do in terms of uh your artificial intelligence operations and what are the capabilities now that you're providing in terms of the observe ability solutions that Ai Ops can benefit from? >>So the way I think about these two categories is that observe abilities, the system of record. That's where all the data is collected and and put into context. So that's what we do as in stana is we take all the data metrics, locks, traces, profiles and put it into a system of record by the way in in in very high granularity. It's very important. So we, we do not sample. We have second granularity metrics. So very high quality data in that system of record where Ai ops is the system of action. This is a system where it takes the data that we have applies machine learning, statistical analytics etcetera on it to figure out for example root cause of problems or even predict problems in the future and then suggests actions. Right? What the next thing that AI does is it suggests or automates an action that you need to do to for example scale up the system, scale down the system scaling down because you want to safe cost for example these are all things that are happening in the system of action which is the IOP space >>when I think about what you're talking about in terms of observe ability. I think well who needs it? Everybody is probably the answer to that. Um Can you give us maybe just a couple of examples of some clients that you've worked with in terms of of particular needs that they had and then how you applied your observe ability platform to provide them with these kinds of solutions? >>Yeah I I remember a big e commerce vendor in the U. S. Approaching us. Uh last october they were approaching the black friday right where where they sell a lot of goods and and they had performance issues but they only had issues with certain types of customers and with their existing APM solution. They couldn't figure out where the problem is because existing solutions sample, which means if you have 1000 customers you only see one of them as an example because the other 999 are not in your in your sample. And so they used us because we don't sample with us. If you have they have more than a billion requests today. You see every of the one billion requests and offer a few days they had all the problems figure out. And that's what that was. One of the things that we really do differently is providing all the needed data, not sampling and then giving the context around the problem so that you can solve issues like performance issues on your e commerce system easily. So they switched and you can imagine switching the system before black friday, you only do that if it's really needed. So they were really under pressure and so they switched their A P. M. Tool to in stana to be able to to fulfill the big demand they have on these black friday days. >>All right. So uh I I before I let you go you were just saying they had a high degree of confidence. How are you sweating? That went out because that was not a small thing at all. I would I >>assume. Uh Yes, it's not a small thing. And to be honest also it's very hard to predict the traffic on black Fridays. Right? Uh And and in this case I remember our SRE team, they had almost 20 times the traffic of the normal day during that black friday. And we because we don't sample, we need to make sure that we can handle and process all these traces. But we did, we did pretty well. So I have high confidence in our platform that we can really handle big amounts of data. We have >>one >>of the biggest companies in the world, the biggest companies in these worlds. They use our tool to monitor billions of requests. So I think we have proven that it works. >>You know, I say you're smiling to about it. So I think it obviously it did work. It >>did work. But yeah, I'm sweating still. Yeah. >>Never let them see you sweat merkel. I think you're very good at that and obviously very good at enterprise observe ability. It's an interesting concept, certainly putting it well under practice and thanks for the time today to talk about it here as part of IBM think to, to share your company's success story. Thank you. Marco. >>Thanks for having me, john >>All right. We're talking about enterprise observe ability here. I P. M. Thank the initiative continues here on the cube. I'm john Walton. Thank you for joining us. >>Yeah. Mhm. >>Yeah.

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Welcome back to the queue from Las Vegas. We are live at AWS reinvent 19 Lisa Martin with John furrier. We've been having lots of great conversations. John, we're about to have another one cause we always love to talk about customer proof in the putting. Please welcome a couple of guests. We have Rafael, director of analytics and data management from triple a life. Welcome. Thanks for having me. Really appreciate it. Our pleasure. And from Burkle anchor Jane, the SVP of cloud platforms. Welcome. Thank you. Thank you so much. Pleasure to be here. So here we are in this, I can't see of people around us as, as growing exponential a by the hour here, but awkward. Let's start with you give her audience an understanding of Merkel, who you are and what you do. >>Yeah, absolutely. So Marco is a global performance marketing agency. We are part of a dental agent network and a, it's almost about 9,000 to 10,000 people worldwide. It's a global agency. What differentiates Merkel from rest of the other marketing agencies is our deep roots and data driven approach. We embrace technology. It's embedded in all our, all our solutions that we take to market. Um, and that's what we pride ourselves with. So, um, that's basically a high level pitch about Merkel. What differentiates us, my role, uh, I lead the cloud transformation for Merkel. Um, uh, basically think of my team as the think tanks who bring in the new technology, come up with a new way of rolling out solutions product I solutions, uh, disruptive solutions, which helps our clients and big fortune brands such as triple life insurance, uh, to transform their marketing ecosystem. >>So let's go ahead and dig. A lot of folks probably know AAA life, but, but Raphael, give us a little bit of an overview. This is a 50 year old organization. >>So we celebrate our 50th 50 year anniversary this year. Actually, we're founded in 1969. So everybody life insurance, we endeavor to be the provider of choice for a AAA member. Tell them to protect what matters most to them. And we offer a diverse set of insurance products across just about every channel. Um, and um, we engage with Merkel, uh, earlier, the, um, in 2018 actually to, to, uh, to build a nice solution that allows us to even better serve the needs of the members. Uh, my role, I am the, I lead our analytics and data management work. So helping us collect data and manage better and better leverage it to support the needs of members. >>So a trip, I can't even imagine the volumes of data that you're dealing with, but it's also, this is people's data, right? This is about insurance, life insurance, the volume of it. How have you, what were some of the things that you said? All right guys, we need to change how we're managing the data because we know there's probably a lot more business value, maybe new services that we can get our on it or eyes >>on it. >>So, so that was, that was it. So as an organization, uh, I want to underscore what you said. We make no compromises when it comes to the safety of our, of our members data. And we take every step possible to ensure that it is managed in a responsible and safe way. But we knew that on, on the platform that we had prior to this, we weren't, we weren't as italics. We wanted to be. We would find that threaten processes would take spans of weeks in order to operate or to run. And that just didn't allow us to provide the member experience that we wanted. So we built this new solution and this solution updates every day, right? There's no longer multi-week cycle times and tumbler processes happen in real time, which allows us to go to market with more accurate and more responsive programs to our members. >>Can you guys talk about the Amazon and AWS solution? How you guys using Amazon's at red shift? Can he says, you guys losing multiple databases, give us a peek into the Amazon services that you guys are taking advantage of that anchor. >>Yeah, please. Um, so basically when we were approached by AAA life to kind of come in and you know, present ourselves our credentials, one thing that differentiated there in that solution page was uh, bringing Amazon to the forefront because cloud, you know, one of the issue that Ravel and his team were facing were scalability aspect. You know, the performance was, was not up to the par, I believe you guys were um, on a two week cycle. That data was a definition every two weeks. And how can we turn that around and know can only be possible to, in our disruptive technologies that Amazon brings to the forefront. So what we built was basically it's a complete Amazon based cloud native architecture. Uh, we leveraged AWS with our chip as the data warehouse platform to integrate basically billions and billions of rows from a hundred plus sources that we are bringing in on a daily basis. >>In fact, actually some of the sources are the fresh on a real time basis. We are catching real time interactions of users on the website and then letting Kimberly the life make real time decisions on how we actually personalize their experience. So AWS, Redshift, you know, definitely the center's centerpiece. Then we are also leveraging a cloud native ELT technology extract load and transform technology called. It's a third party tool, but again, a very cloud native technology. So the whole solution leverage is Python to some extent. And then our veil can talk about AI and machine learning that how they are leveraging AWS ecosystem there. >>Yeah. So that was um, so, uh, I anchor said it right. One thing that differentiated Merkel was that cloud first approach, right? Uh, we looked at it what a, all of the analysts were saying. We went to all the key vendors in this space. We saw the, we saw the architecture is, and when Merkel walked in and presented that, um, that AWS architecture, it was great for me because if nausea immediately made sense, there was no wizardry around, I hope this database scales. I was confident that Redshift and Lambda and dynamo would this go to our use cases. So it became a lot more about are we solving the right business problem and less about do we have the right technologies. So in addition to what Ankur mentioned, we're leveraging our sort of living RNR studio, um, in AWS as well as top low frat for our machine learning models and for business intelligence. >>And more recently we've started transition from R to a Python as a practitioner on the keynote today. Slew a new thing, Sage maker studio, an IDE for machine learning framework. I mean this is like a common set. Like finally, I couldn't have been more excited right? That, that was my Superbowl moment. Um, I was, I was as I was, we were actually at dinner yesterday and I was mentioning Tonker, this is my wishlist, right? I want AWS to make a greater investment in that end user data scientists experience in auto ML and they knocked it out of the park. Everything they announced today, I was just, I was texting frat. Wow, this is amazing. I can't wait to go home. There's a lot of nuances to, and a lot of these announcements, auto ML for instance. Yeah. Really big deal the way they did it. >>And again, the ID who would've thought, I mean this is duh, why didn't we think about this sooner? Yeah. With auto ML that that focus on transparency. Right. And then I think about a year ago we went to market and we ended up not choosing any solutions because they hadn't solved for once you've got a model built, how do you effectively migrated from let's say an analyst who might not have the, the ML expertise to a data science team and the fact that AWS understood out of the gate that you need that transparent all for it. I'm really excited for that. What do you think the impacts are going to be more uptake on the data science side? What do you think the impact of this and the, so I think for, I think we're going to see, um, that a lot of our use cases are going to part a lot less effort to spin up. >>So we're going to see much more, much faster pilots. We're going to have a much clearer sense of is this worth it? Is this something we should continue to invest in and to me we should drive and I expect that a lot, much larger percentage of my team, the analysts are going to be involved in data and data science and machine learning. So I'm really excited about that. And also the ability to inquire, to integrate best practices into what we're doing out of the gate. Right? So software engineers figured out profiling, they figured out the bugging and these are things that machine learners are picking up. Now the fact that you're front and center is really excited. Superbowl moment. You can be like the new England Patriots, 17 straight AFC championship games. Boston. Gosh, I could resist. Uh, they're all Seattle. They're all Seattle here and Amazon. I don't even bring Seattle Patriots up here and Amazon, >>we are the ESPN of tech news that we have to get in as far as conversation. But I want to kind of talk a little bit, Raphael about the transformation because presumably in, in every industry, especially in insurance, there are so many born in the cloud companies that are a lot, they're a lot more agile and they are chasing what AAA life and your competitors and your peers are doing. What your S establishing with the help of anchor and Merkel, how does this allow you to actually take the data that you had, expand it, but also extract insights from maybe competitive advantages that you couldn't think about before? >>Yeah, so I think, uh, so as an organization, even though we're 50 years old, one of the things that drew me to the company and it's really exciting is it's unrelated to thrusting on its laurels, right? I think there's tremendous hunger and appetite within our executive group to better serve our members and to serve more members. And what this technology is allowed is the technology is not a limiting factor. It's an enabling factors. We're able to produce more models, more performant models, process more of IO data, build more features. Um, we've managed to do away with a lot of the, you know, if you take it and you look at it this way and squeeze it and maybe it'll work and systematize more aspects of our reporting and our campaign development and our model development and the observability, the visibility of just the ability to be agile and have our data be a partner to what we're trying to accomplish. That's been really great. >>You talked about the significant reduction in cycle times. If we go back up to the executive suite from a business differentiation perspective, is the senior leadership at AAA understanding what this cloud infrastructure is going to enable their business to achieve? >>Absolutely. So, so our successes here I think have been instrumental in encouraging our organization to continue to invest in cloud. And uh, we're an active, we're actively considering and discussing additional cloud initiatives, especially around the areas of machine learning and AI. >>And the auger question for you in terms of, of your expertise, in your experience as we look at how cloud is changing, John, you know, educate us on cloud cloud, Tuto, AI machine learning. What are, as, as these, as businesses, as industries have the opportunity to for next gen cloud, what are some of the next industries that you think are really prime to be completely transformed? >>Um, I'm in that are so many different business models. If you look around, one thing I would like to actually touch upon what we are seeing from Merkel standpoint is the digital transformation and how customers in today's world they are, you know, how brands are engaging with their customers and how customers are engaging with the brands. Especially that expectations customer is at the center stage here they are the ones who are driving the whole customer engagement journey, right? How all I am browsing a catalog of a particular brand on my cell phone and then I actually purchased right then and there and if I have an issue I can call them or I can go to social media and log a complaint. So that's whole multi channel, you know, aspect of this marketing ecosystem these days. I think cloud is the platform which is enabling that, right? >>This cannot happen without cloud. I'm going to look at, Raphael was just describing, you know, real time interaction, real time understanding the behavior of the customer in real time and engaging with them based on their need at that point of time. If you have technologies like Sage maker, if you have technologies like AWS Redship you have technologies like glue, Kinesis, which lets you bring in data from all these disparate sources and give you the ability to derive some insights from that data in that particular moment and then interact with the customer right then and there. That's exactly what we are talking about. And this can only happen through cloud so, so that's my 2 cents are where they are, what we from Merkel standpoint, we are looking into the market. That's what we are helping our brands through to >>client. I completely agree. I think that the change from capital and operation, right to no longer house to know these are all the sources and all the use cases and everything that needs to happen before you start the project and the ability to say, Hey, let's get going. Let's deliver value in the way that we've had and continue to have conversations and deliver new features, new stores, a new functionality, and at the same time, having AWS as a partner who's, who's building an incremental value. I think just last week I was really excited with the changes they've made to integrate Sage maker with their databases so you can score from the directly from the database. So it feels like all these things were coming together to allow us as a company to better off on push our aims and exciting time. >>It is exciting. Well guys, I wish we had more time, but we are out of time. Thank you Raphael and anchor for sharing with Merkel and AAA. Pleasure. All right. Take care. Or John furrier. I am Lisa Martin and you're watching the cube from Vegas re-invent 19 we'll be right back.

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome back to downtown Miami. Everybody. We're here at the Intercontinental hotel covering the IBM data AI form hashtag data AI forum. My name is Dave Volante and you're watching the cube, the leader in live tech coverage. Ritika gunner is here. She's the vice president of data and AI expert labs and learning at IBM. Ritika, great to have you on. Again, always a pleasure to be here. Dave. I love interviewing you because you're a woman executive that said a lot of different roles at IBM. Um, you know, you've, we've talked about the AI ladder. You're climbing the IBM ladder and so it's, it's, it's, it's awesome to see and I love this topic. It's a topic that's near and dear to the cubes heart, not only women in tech, but women in AI. So great to have you. Thank you. So what's going on with the women in AI program? We're going to, we're going to cover that, but let me start with women in tech. It's an age old problem that we've talked about depending on, you know, what statistic you look at. 15% 17% of, uh, of, of, of the industry comprises women. We do a lot of events. You can see it. Um, let's start there. >>Well, obviously the diversity is not yet there, right? So we talk about women in technology, um, and we just don't have the representation that we need to be able to have. Now when it comes to like artificial intelligence, I think the statistic is 10 to 15% of the workforce today in AI is female. When you think about things like bias and ethicacy, having the diversity in terms of having male and female representation be equal is absolutely essential so that you're creating fair AI, unbiased AI, you're creating trust and transparency, set of capabilities that really have the diversity in backgrounds. >>Well, you work for a company that is as chairman and CEO, that's, that's a, that's a woman. I mean IBM generally, you know, we could see this stuff on the cube because IBM puts women on a, we get a lot of women customers that, that come on >>and not just because we're female, because we're capable. >>Yeah. Well of course. Right. It's just because you're in roles where you're spokespeople and it's natural for spokespeople to come on a forum like this. But, but I have to ask you, with somebody inside of IBM, a company that I could say the test to relative to most, that's pretty well. Do you feel that way or do you feel like even a company like IBM has a long way to go? >>Oh, um, I personally don't feel that way and I've never felt that to be an issue. And if you look at my peers, um, my um, lead for artificial intelligence, Beth Smith, who, you know, a female, a lot of my peers under Rob Thomas, all female. So I have not felt that way in terms of the leadership team that I have. Um, but there is a gap that exists, not necessarily within IBM, but in the community as a whole. And I think it goes back to you want to, you know, when you think about data science and artificial intelligence, you want to be able to see yourself in the community. And while there's only 10 to 15% of females in AI today, that's why IBM has created programs such as women AI that we started in June because we want strong female leaders to be able to see that there are, is great representation of very technical capable females in artificial intelligence that are doing amazing things to be able to transform their organizations and their business model. >>So tell me more about this program. I understand why you started it started in June. What does it entail and what's the evolution of this? >>So we started it in June and the idea was to be able to get some strong female leaders and multiple different organizations that are using AI to be able to change their companies and their business models and really highlight not just the journey that they took, but the types of transformations that they're doing and their organizations. We're going to have one of those events tonight as well, where we have leaders from Harley Davidson in Miami Dade County coming to really talk about not only what was their journey, but what actually brought them to artificial intelligence and what they're doing. And I think Dave, the reason that's so important is you want to be able to understand that those journeys are absolutely approachable. They're doable by any females that are out there. >>Talk about inherent bias. The humans are biased and if you're developing models that are using AI, there's going to be inherent bias in those models. So talk about how to address that and why is it important for more diversity to be injected into those models? >>Well, I think a great example is if you took the data sets that existed even a decade ago, um, for the past 50 years and you created a model that was to be able to predict whether to give loans to certain candidates or not, all things being equal, what would you find more males get these loans than females? The inherent data that exists has bias in it. Even from the history based on what we've had yet, that's not the way we want to be able to do things today. You want to be able to identify that bias and say all things being equal, it is absolutely important that regardless of whether you are a male or a female, you want to be able to give that loan to that person if they have all the other qualities that are there. And that's why being able to not only detect these things but have the diversity and the kinds of backgrounds of people who are building AI who are deploying this AI is absolutely critical. >>So for the past decade, and certainly in the past few years, there's been a light shined on this topic. I think, you know, we were at the Grace Hopper conference when Satya Nadella stuck his foot in his mouth and it said, Hey, it's bad karma for you know, if you feel like you're underpaid to go complain. And the women in the audience like, dude, no way. And he, he did the right thing. He goes, you know what, you're right. You know, any, any backtrack on that? And that was sort of another inflection point. But you talk about the women in, in AI program. I was at a CDO event one time. It was I and I, an IBM or had started the data divas breakfast and I asked, can I go? They go, yeah, you can be the day to dude. Um, which was, so you're seeing a lot of initiatives like this. My question is, are they having the impact that you would expect and that you want to have? >>I think they absolutely are. Again, I mean, I'll go back to, um, I'll give you a little bit of a story. Um, you know, people want to be able to relate and see that they can see themselves in these females leaders. And so we've seen cases now through our events, like at IBM we have a program called grow, which is really about helping our female lead female. Um, technical leaders really understand that they can grow, they can be nurtured, and they have development programs to help them accelerate where they need to be on their technical programs. We've absolutely seen a huge impact from that from a technology perspective. In terms of more females staying in technology wanting to go in the, in those career paths as another story. I'll, I'll give you kind of another kind of point of view. Um, Dave and that is like when you look at where it starts, it starts a lot earlier. >>So I have a young daughter who a year, year and a half ago when I was doing a lot of stuff with Watson, she would ask me, you know, not only what Watson's doing, but she would say, what does that mean for me mom? Like what's my job going to be? And if you think about the changes in technology and cultural shifts, technology and artificial intelligence is going to impact every job, every industry, every role that there is out there. So much so that I believe her job hasn't been invented yet. And so when you think about what's absolutely critical, not only today's youth, but every person out there needs to have a foundational understanding, not only in the three RS that you and I know from when we grew up have reading, writing and arithmetic, we need to have a foundational understanding of what it means to code. And you know, having people feel confident, having young females feel confident that they can not only do that, that they can be technical, that they can understand how artificial intelligence is really gonna impact society. And the world is absolutely critical. And so these types of programs that shed light on that, that help bridge that confidence is game changing. >>Well, you got kids, I >>got kids, I have daughters, you have daughter. Are they receptive to that? So, um, you know, I think they are, but they need to be able to see themselves. So the first time I sent my daughter to a coding camp, she came back and said, not for me mom. I said, why? Because she's like, all the boys, they're coding in their Minecraft area. Not something I can relate to. You need to be able to relate and see something, develop that passion, and then mix yourself in that diverse background where you can see the diversity of backgrounds. When you don't have that diversity and when you can't really see how to progress yourself, it becomes a blocker. So as she started going to grow star programs, which was something in Austin where young girls coded together, it became something that she's really passionate about and now she's Python programming. So that's just an example of yes, you need to be able to have these types of skills. It needs to start early and you need to have types of programs that help enhance that journey. >>Yeah, and I think you're right. I think that that is having an impact. My girls who code obviously as a some does some amazing work. My daughters aren't into it. I try to send them to coder camp too and they don't do it. But here's my theory on that is that coding is changing and, and especially with artificial intelligence and cognitive, we're a software replacing human skills. Creativity is going to become much, much more important. My daughters are way more creative than my sons. I shouldn't say that, but >>I think you just admitted that >>they, but, but in a way they are. I mean they've got amazing creativity, certainly more than I am. And so I see that as a key component of how coding gets done in the future, taking different perspectives and then actually codifying them. Your, your thoughts on that. >>Well there is an element of understanding like the outcomes that you want to generate and the outcomes really is all about technology. How can you imagine the art of the possible with technology? Because technology alone, we all know not useful enough. So understanding what you do with it, just as important. And this is why a lot of people who are really good in artificial intelligence actually come from backgrounds that are philosophy, sociology, economy. Because if you have the culture of curiosity and the ability to be able to learn, you can take the technology aspects, you can take those other aspects and blend them together. So understanding the problem to be solved and really marrying that with the technological aspects of what AI can do. That's how you get outcomes. >>And so we've, we've obviously talking in detail about women in AI and women in tech, but it's, there's data that shows that diversity drives value in so many different ways. And it's not just women, it's people of color, it's people of different economic backgrounds, >>underrepresented minorities. Absolutely. And I think the biggest thing that you can do in an organization is have teams that have that diverse background, whether it be from where they see the underrepresented, where they come from, because those differences in thought are the things that create new ideas that really innovate, that drive, those business transformations that drive the changes in the way that we do things. And so having that difference of opinion, having healthy ways to bring change and to have conflict, absolutely essential for progress to happen. >>So how did you get into the tech business? What was your background? >>So my background was actually, um, a lot in math and science. And both of my parents were engineers. And I have always had this unwavering, um, need to be able to marry business and the technology side and really figure out how you can create the art of the possible. So for me it was actually the creativity piece of it where you could create something from nothing that really drove me to computer science. >>Okay. So, so you're your math, uh, engineer and you ended up in CS, is that right? >>Science. Yeah. >>Okay. So you were coded. Did you ever work as a programmer? >>Absolutely. My, my first years at IBM were all about coding. Um, and so I've always had a career where I've coded and then I've gone to the field and done field work. I've come back and done development and development management, gone back to the field and kind of seen how that was actually working. So personally for me, being able to create and work with clients to understand how they drive value and having that back and forth has been a really delightful part. And the thing that drives me, >>you know, that's actually not an uncommon path for IBM. Ours, predominantly male IBM, or is in the 50 sixties and seventies and even eighties. Who took that path? They started out programming. Um, I just think, trying to think of some examples. I know Omar para, who was the CIO of Aetna international, he started out coding at IBM. Joe Tucci was a programmer at IBM. He became CEO of EMC. It was a very common path for people and you took the same path. That's kind of interesting. Why do you think, um, so many women who maybe maybe start in computer science and coding don't continue on that path? And what was it that sort of allowed you to break through that barrier? >>No, I'm not sure why most women don't stay with it. But for me, I think, um, you know, I, I think that every organization today is going to have to be technical in nature. I mean, just think about it for a moment. Technology impacts every part of every type of organization and the kinds of transformation that happens. So being more technical as leaders and really understanding the technology that allows the kinds of innovations and business for informations is absolutely essential to be able to see progress in a lot of what we're doing. So I think that even general CXOs that you see today have to be more technically acute to be able to do their jobs really well and marry those business outcomes with what it fundamentally means to have the right technology backbone. >>Do you think a woman in the white house would make a difference for young people? I mean, part of me says, yeah, of course it would. Then I say, okay, well some examples you can think about Margaret Thatcher in the UK, Angela Merkel, and in Germany it's still largely male dominated cultures, but I dunno, what do you think? Maybe maybe that in the United States would be sort of the, >>I'm not a political expert, so I wouldn't claim to answer that, but I do think more women in technology, leadership role, CXO leadership roles is absolutely what we need. So, you know, politics aside more women in leadership roles. Absolutely. >>Well, it's not politics is gender. I mean, I'm independent, Republican, Democrat, conservative, liberal, right? Absolutely. Oh yeah. Well, companies, politics. I mean you certainly see women leaders in a, in Congress and, and the like. Um, okay. Uh, last question. So you've got a program going on here. You have a, you have a panel that you're running. Tell us more about. >>Well this afternoon we'll be continuing that from women leaders in AI and we're going to do a panel with a few of our clients that really have transformed their organizations using data and artificial intelligence and they'll talk about like their backgrounds in history. So what does it actually mean to come from? One of, one of the panelists actually from Miami Dade has always come from a technical background and the other panelists really etched in from a non technical background because she had a passion for data and she had a passion for the technology systems. So we're going to go through, um, how these females actually came through to the journey, where they are right now, what they're actually doing with artificial intelligence in their organizations and what the future holds for them. >>I lied. I said, last question. What is, what is success for you? Cause I, I would love to help you achieve that. That objective isn't, is it some metric? Is it awareness? How do you know it when you see it? >>Well, I think it's a journey. Success is not an endpoint. And so for me, I think the biggest thing I've been able to do at IBM is really help organizations help businesses and people progress what they do with technology. There's nothing more gratifying than like when you can see other organizations and then what they can do, not just with your technology, but what you can bring in terms of expertise to make them successful, what you can do to help shape their culture and really transform. To me, that's probably the most gratifying thing. And as long as I can continue to do that and be able to get more acknowledgement of what it means to have the right diversity ingredients to do that, that success >>well Retika congratulations on your success. I mean, you've been an inspiration to a number of people. I remember when I first saw you, you were working in group and you're up on stage and say, wow, this person really knows her stuff. And then you've had a variety of different roles and I'm sure that success is going to continue. So thanks very much for coming on the cube. You're welcome. All right, keep it right there, buddy. We'll be back with our next guest right after this short break, we're here covering the IBM data in a AI form from Miami right back.

>> Announcer: From San Jose, it's theCUBE. (upbeat music) Presenting Big Data, Silicon Valley, brought to you by Silicon Angle Media and its ecosystem partners. >> Welcome back to theCUBE. We are live on our first day of coverage of our event, Big Data SV. This is our tenth Big Data event. We've done five here in Silicon Valley. We also do them in New York City in the fall. We have a great day of coverage. We're next to where the Startup Data conference is going on at Forger Tasting Room and Eatery. Come on down, be part of our audience. We also have a great party tonight where you can network with some of our experts and analysts. And tomorrow morning, we've got a breakfast briefing. I'm Lisa Martin with my co-host, Peter Burris, and we're excited to welcome to theCUBE for the first time the CEO of Kinetica, Paul Appleby. Hey Paul, welcome. >> Hey, thanks, it's great to be here. >> We're excited to have you here, and I saw something marketer, and terms, I grasp onto them. Kinetica is the insight engine for the extreme data economy. What is the extreme data economy, and what are you guys doing to drive insight from it? >> Wow, how do I put that in a snapshot? Let me share with you my thoughts on this because the fundamental principals around data have changed. You know, in the past, our businesses are really validated around data. We reported out how our business performed. We reported to our regulators. Over time, we drove insights from our data. But today, in this kind of extreme data world, in this world of digital business, our businesses need to be powered by data. >> So what are the, let me task this on you, so one of the ways that we think about it is that data has become an asset. >> Paul: Oh yeah. >> It's become an asset. But now, the business has to care for, has to define it, care for it, feed it, continue to invest in it, find new ways of using it. Is that kind of what you're suggesting companies to think about? >> Absolutely what we're saying. I mean, if you think about what Angela Merkel said at the World Economic Forum earlier this year, that she saw data as the raw material of the 21st century. And talking about about Germany fundamentally shifting from being an engineering, manufacturing centric economy to a data centric economy. So this is not just about data powering our businesses, this is about data powering our economies. >> So let me build on that if I may because I think it gets to what, in many respects Kinetica's Core Value proposition is. And that is, is that data is a different type of an asset. Most assets are characterized by, you apply it here, or you apply it there. You can't apply it in both places at the same time. And it's one of the misnomers of the notion of data as fuels. Because fuel is still an asset that has certain specificities, you can't apply it to multiple places. >> Absolutely. >> But data, you can, which means that you can copy it, you can share it. You can combine it in interesting ways. But that means that the ... to use data as an asset, especially given the velocity and the volume that we're talking about, you need new types of technologies that are capable of sustaining the quality of that data while making it possible to share it to all the different applications. Have I got that right? And what does Kinetica do in that regard? >> You absolutely nailed it because what you talked about is a shift from predictability associated with data, to unpredictability. We actually don't know the use cases that we're going to leverage for our data moving forward, but we understand how valuable an asset it is. And I'll give you two examples of that. There's a company here, based in the Bay Area, a really cool company called Liquid Robotics. And they build these autonomous aquatic robots. And they've carried a vast array of senses and now we're collecting data. And of course, that's hugely powerful to oil and gas exploration, to research, to shipping companies, etc. etc. etc. Even homeland security applications. But what they did, they were selling the robots, and what they realized over time is that the value of their business wasn't the robots. It was the data. And that one piece of data has a totally different meaning to a shipping company than it does to a fisheries companies. But they could sell that exact same piece of data to multiple companies. Now, of course, their business has grown on in Scaldon. I think they were acquired by Bowing. But what you're talking about is exactly where Kinetica sits. It's an engine that allows you to deal with the unpredictability of data. Not only the sources of data, but the uses of data, and enables you to do that in real time. >> So Kinetica's technology was actually developed to meet some intelligence needs of the US Army. My dad was a former army ranger airborne. So tell us a little bit about that and kind of the genesis of the technology. >> Yeah, it's a fascinating use case if you think about it, where we're all concerned, globally, about cyber threat. We're all concerned about terrorist threats. But how do you identity terrorist threats in real time? And the only way to do that is to actually consume vast amount of data, whether it's drone footage, or traffic cameras. Whether it's mobile phone data or social data. but the ability to stream all of those sources of data and conduct analytics on that in real time was, really, the genesis of this business. It was a research project with the army and the NSA that was aimed at identifying terrorist threats in real time. >> But at the same time, you not only have to be able to stream all the data in and do analytics on it, you also have to have interfaces and understandable approaches to acquiring the data, because I have a background, some background in that as well, to then be able to target the threat. So you have to be able to get the data in and analyze it, but also get it out to where it needs to be so an action can be taken. >> Yeah, and there are two big issues there. One issue is the inter-offer ability of the platform and the ability for you to not only consume data in real time from multiple sources, but to push that out to a variety of platforms in real time. That's one thing. The other thing is to understand that in this world that we're talking about today, there are multiple personas that want to consume that data, and many of them are not data scientists. They're not IT people, they're business people. They could be executives, or they could be field operatives in the case of intelligence. So you need to be able to push this data out in real time onto platforms that they consume, whether it's via mobile devices or any other device for that matter. >> But you also have to be able to build applications on it, right? >> Yeah, absolutely. >> So how does Kinetica facilitate that process? Because it looks more like a database, which is, which is, it's more than that, but it satisfies some of those conventions so developers have an afinity for it. >> Absolutely, so in the first instance, we provide tools ourselves for people to consume that data and to leverage the power of that data in real time in an incredibly visual way with a geospatial platform. But we also create the ability for a, to interface with really commonly used tools, because the whole idea, if you think about providing some sort of ubiquitous access to the platform, the easiest way to do that is to provide that through tools that people are used to using, whether that's something like Tablo, for example, or Esri, if you want to talk about geospatial data. So the first instance, it's actually providing access, in real time, through platforms that people are used to using. And then, of course, by building our technology in a really, really open framework with a broadly published set of APIs, we're able to support, not only the ability for our customers to build applications on that platform, and it could well be applications associated with autonomous vehicles. It could well be applications associated with Smart City. We're doing some incredible things with some of the bigger cities on the planet and leveraging the power of big data to optimize transportation, for example, in the city of London. It's those sorts of things that we're able to do with the platform. So it's not just about a database platform or an insights engine for dealing with these complex, vast amounts of data, but also the tools that allow you to visualize and utilize that data. >> Turn that data into an action. >> Yeah, because the data is useless until you're doing something with it. And that's really, if you think about the promise of things like smart grid. Collecting all of that data from all of those smart sensors is absolutely useless until you take an action that is meaningful for a consumer or meaningful in terms of the generational consumption of power. >> So Paul, as the CEO, when you're talking to customers, we talk about chief data officer, chief information officer, chief information security officer, there's a lot, data scientist engineers, there's just so many stakeholders that need access to the data. As businesses transform, there's new business models that can come into development if, like you were saying, the data is evaluated and it's meaningful. What are the conversations that you're having, I guess I'm curious, maybe, which personas are the table (Paul laughs) when you're talking about the business values that this technology can deliver? >> Yeah, that's a really, really good question because the truth is, there are multiple personas at the table. Now, we, in the technology industry, are quite often guilty of only talking to the technology personas. But as I've traveled around the world, whether I'm meeting with the world's biggest banks, the world's biggest Telco's, the world's biggest auto manufacturers, the people we meet, more often than not, are the business leaders. And they're looking for ways to solve complex problems. How do you bring the connected card alive? How do you really bring it to life? One car traveling around the city for a full day generates a terabyte of data. So what does that really mean when we start to connect the billions of cars that are in the marketplace in the framework of connected car, and then, ultimately, in a world of autonomous vehicles? So, for us, we're trying to navigate an interesting path. We're dragging the narrative out of just a technology-based narrative speeds and feeds, algorithms, and APIs, into a narrative about, well what does it mean for the pharmaceutical industry, for example? Because when you talk to pharmaceutical executives, the holy grail for the pharma industry is, how do we bring new and compelling medicines to market faster? Because the biggest challenge for them is the cycle times to bring new drugs to market. So we're helping companies like GSK shorten the cycle times to bring drugs to market. So they're the kinds of conversations that we're having. It's really about how we're taking data to power a transformational initiative in retail banking, in retail, in Telco, in pharma, rather than a conversation about the role of technology. Now, we always needs to deal with the technologists. We need to deal with the data scientists and the IT executives, and that's an important part of the conversation. But you would have seen, in recent times, the conversation that we're trying to have is far more of a business conversation. >> So if I can build on that. So do you think, in your experience, and recognizing that you have a data management tool with some other tools that helps people use the data that gets into Kinetica, are we going to see the population of data scientists increase fast enough so our executives don't have to become familiar with this new way of thinking, or are executives going to actually adopt some of these new ways of thinking about the problem from a data risk perspective? I know which way I think. >> Paul: Wow, >> Which way do you think? >> It's a loaded question, but I think if we're going to be in a world where business is powered by data, where our strategy is driven by data, our investment decisions are driven by data, and the new areas of business that we explored to creat new paths to value are driven by data, we have to make data more accessible. And if what you need to get access to the data is a whole team of data scientists, it kind of creates a barrier. I'm not knocking data scientists, but it does create a barrier. >> It limits the aperture. >> Absolutely, because every company I talk to says, "Our biggest challenge is, we can't get access to the data scientists that we need." So a big part of our strategy from the get go was to actually build a platform with all of these personas in mind, so it is built on this standard principle, the common principles of a relational database, that you're built around anti-standard sequel. >> Peter: It's recognizable. >> And it's recognizable, and consistent with the kinds of tools that executives have been using throughout their careers. >> Last question, we've got about 30 seconds left. >> Paul: Oh, okay. >> No pressure. >> You have said Kinetica's plan is to measure the success of the business by your customers' success. >> Absolutely. >> Where are you on that? >> We've begun that journey. I won't say we're there yet. We announced three weeks ago that we created a customer success organization. We've put about 30% of the company's resources into that customer success organization, and that entire team is measured not on revenue, not on project delivered on time, but on value delivered to the customer. So we baseline where the customer is at. We agree what we're looking to achieve with each customer, and we're measuring that team entirely against the delivery of those benefits to the customer. So it's a journey. We're on that journey, but we're committed to it. >> Exciting. Well, Paul, thank you so much for stopping by theCUBE for the first time. You're now a CUBE alumni. >> Oh, thank you, I've had a lot of fun. >> And we want to thank you for watching theCUBE. I'm Lisa Martin, live in San Jose, with Peter Burris. We are at the Forger Tasting Room and Eatery. Super cool place. Come on down, hang out with us today. We've got a cocktail party tonight. Well, you're sure to learn lots of insights from our experts, and tomorrow morning. But stick around, we'll be right back with our next guest after a short break. (CUBE theme music)