(upbeat music) >> Welcome back to theCUBE's continuous coverage of AWS re:invent 2021. I'm here with my co-host John Furrier and we're running one of the largest, most significant technology events in the history of 2021. Two live sets here in Las Vegas, along with our two studios. And we are absolutely delighted. We're incredibly delighted to welcome a returning alumni. It's not enough to just say that you're an alumni because you have been such a fixture of theCUBE for so many years. Annie Weinberger. And Annie is head of product marketing for applications at AWS. Annie, welcome. >> Thank you so much, it's great to be back. >> It's wonderful to have you back. Let's dive right into it. >> Okay. >> Talk to us about Connect. What does that mean when I say Connect? >> Yes, well, I think if we talk about Amazon Connect, we have to go back to the beginning of the origin story. So, over 10 years ago, when Amazon retail was looking for a solution to manage their customer service and their contact center, we went out and we looked at different solutions and nothing really met our needs. Nothing could kind of provide the scale that we needed at Amazon, or could really be as flexible as we needed to ensure that we're our customer obsession could come through in our customer service. So we built our own solution. And over the years, customers were coming to us and asking, you know, what do you use for your customer service technology? And so we launched Amazon Connect, our omni-channel cloud contact center solution just over four years ago. And it is the one of the fastest growing services at AWS. We have tens of thousands of customers using it today, like Capital One into it, Bank of Omaha, Mutual of Omaha, Best Western, you know, I can go on and on. And they're using it to have over 10 million interactions with customers every day. So it's, you know, growing phenomenally and we just couldn't be more proud to help our customers with their customer service. >> So, yeah. Talk about some of the components that go into that. What are the sort of puzzle pieces that make up AWS Connect? Because obviously connecting with a customer can take a whole bunch of different forms with email, text, voice. >> Yeah >> What's included in that? >> So it's an omni-channel cloud contact center. It provides, you know, any way you want to talk to your customers. There's traditional methods of voice. There's automated ways to connect. So IVRs or interactive voice responses where you call with voice prompts, there's chat, you know. We have Lex Bots that use the same technology that powers Alexa for natural language understanding. And I think customers really like it for a few reasons. One is that unlike kind of other contact center solutions, you can set it up in minutes. You know, American Preparatory Academy had to set up a contact center, they did it in two days. And then it's very, very easy to customize and use. So another example is, you know, when Priceline was going through COVID and they realized their call volume went up 300% overnight, and everybody was just sitting near the queue waiting to talk to an agent. So in 20 minutes, we were able to go in and very easily with a drag and drop interface, customize the ad flow so that people who had a reservation in the next 72 hours were prioritized. So very, very easily. >> You just jumped the gun on me. I was going to ask this because we never boarding that Connect during the pandemic was a huge success. >> Annie: Yes. >> It was many, many examples where people were just located, disrupted by the pandemic. And you guys had tons of traction from government public sector to commercial across the board. Adam Solecki told me in person a couple weeks ago that it was on fire, Connect was on fire. So again, a tailwind, one of those examples with the pandemic, but it highlights this idea or purpose built, ready to go. >> Pre-built the applications. >> Pre-built application. This is a phenomenon. >> It's moving up the stack for AWS. It's very exciting. I think, yeah, we had over 5,000 new contact centers stood up in March and April of 2020 alone. >> Dave: Wow. >> Give it some scale, just go back to the scale piece. Cause this is like, like amazing to stand up a call center like hours, days. Like this is like incredible to, give us some stats on some examples of how fast people were standing up Connect. >> Yeah, I mean, you could stand it up overnight. American Preparatory Academy, as I mentioned did it in two days, we had, you know, this county of Los Angeles did theirs I think at a day. You could go and right now you don't need any technical expertise, even though you have some. >> theCUBE call center, we don't need people calling. >> We had everyone from a Mexican restaurant needed to take to go orders. Cause now it's COVID and they don't have a call. They've been able to set that up, grab a phone number and start taking takeout orders all the way to like capital one, you know, with 40,000 agents that need to move remote overnight. And I think that it's because of that ease to set up, but also the scale and the way that we charge. So, you know, it's AWS consumption-based pricing. You only pay for the interactions with customers. So the barrier to entry is really, really low. You don't have to migrate everything over and buy a bunch of new licenses. You can just stand it up and you're only charged for the interactions with customers. And then if you want to scale down like into it, obviously tax season they're bringing on a lot more agents to handle calls, when those agents aren't really needed for that busy time, you're not paying for those seats. >> You're flex. Take me through the, okay, that's a win, I get that. So home run, great success. Now, the machine learning story is interesting too, because you have the purpose-built platform. There's some customizations that can happen on top of it. So it's not just, here's a general purpose piece of software. People are using some customizations. Take us through the other things. >> Well, the exciting thing is they're not even real customizations because we're AWS, we can leverage the AML services and built pre-built purpose-built features. So there it's embedded and you know, Amazon Connect has been cloud native and AI born since the very beginning. So we've taken a lot of the AI services and built them into you don't need any knowledge. You don't have to know anything about AIML. You can just go in and start leveraging it. And it has huge powerful effects for our customers. We launched three new features this year. One was Amazon Wisdom. That's part of Amazon Connect. And what that does is, you know, if you're an agent and you're on the phone and customer's asking questions, today what they have to do is go in and search across all these different knowledge repositories to find the answer or, you know, how do I issue a refund? You know, we're hearing about this feature that's broken on our product. We're listening behind the scenes to that call and then just automatically providing the knowledge articles as they're on the call saying, this is what you should do, giving them recommendations so we can help the customer much more quickly. >> I love them moving up the stack. Again, a huge fan of Connect. We've highlighting in all of our stories. It's a phenomenon that's translating to other areas, but I want to tie back in where it goes next cause on these keynotes, Adam Solecki's and today was Swami, the conversations about a horizontal data plane. And so as customers would say, use Connect, I might want, if I'm a big customer I want to integrate that into my data because it's voice data, it's call centers, customer data, but I have other databases. So how do you guys look at that integration layer snapping it together with say, a time series database, or maybe a CRM system or retail e-commerce because again, it's all data but it's connected call center. Some may think it's silo, but it's not really siloed. So, I'm a customer. How do I integrate call center? >> Yeah and it's, you know, we have a very strong partner with Salesforce. They're actually a reseller of Connect. So we work with them very, very closely. We have out of the box integrations with Salesforce, with your other, you know, analytics databases with Marketo with other services that you need. I think again, it's one of the benefits of being AWS, it's very extensible, very flexible, and really easy to bring in and share the data that we have with other systems. >> John: So it's not an issue then. >> One of the conversation points that's come up is the, this idea that a large majority of IT Spend is still on premises today. In other words, the AWS total addressable market hasn't been tapped yet. And, you imagine going through the pandemic, someone using AWS Connect to create a virtual call center, now as we hopefully come out and people some return to the office, but now they have the tools to be able to stay at home and be more flexible. Those people, maybe they weren't in the cloud that much before. But to John's point, now you start talking about connecting all of those other data sources. Well, where do those data sources belong? They belong in AWS. So, from your perspective, on the surface it looks like, well, wait, you have these products, but really those are gateways to everything else that AWS does. Is that a fair statement? >> I think it's very, yeah. Absolutely. >> Yeah. >> The big thing I want to get into is okay, we're, I mean, we don't have a lot of people calling for theCUBE but I mean, we wouldn't use the call center, but there's audio involved. Are people more going back to the old school phones for support now with the pandemic? Cause you've mentioned that earlier about the price line, having more- >> I think it's, you know, when we talk to our customers too, it's about letting, letting any customer contact you the way they want to. You know, we, you know, I was talking to Delta, spoke with us yesterday in the business application leadership session. And she said, you know, when someone has a flight issue, I'm sure you can attest to this. I did the same thing. They call, you know, if your, if your flight got canceled or it's looking like it's going to keep pushing, you don't necessarily want to go, you know, use a chat bot or send an email or a text, but there's other use cases where you just want a quick answer, you know, if you contact, I haven't received my product yet, you know, it said it was shipped, I didn't get it. I don't necessarily want to talk to someone, but so, it's just about making that available. >> On the voice side, is it other apps are integrating voice? So what's the interface to call center? Is it, can I integrate like an app voice integrated through the app or it's all phone? >> Because for the agents, there's an agent UI. So they'll see kind of calls that they have in their queue coming up, they'll see the tasks that they have to issue or refund. They'll see the kind of analytics that they have. The knowledge works. There's a supervisor view, so they could go see, you know, we with contact lens for Amazon Connect, we had a launch this, you know, this week, every event around contact lens, it lets you see the trends and sentiment of what's going on the call. It gives them like those training moments. If people aren't using the standard sign-off or the standard greeting on the call, it's a training moment and they can kind of see what's happening and get real-time alerts. If two keywords of a customer saying they cancel into the call, that can get a flag and they can go in and help the agent if necessary. So. >> All kinds of metadata extraction going on in real time. >> Yeah. >> How do you, how would AWS to go through the process of determining what should be bespoke solution hearing versus something that can be productized? And we know there are 475 different kinds of instances. However, you can come up with a package solution where people could pick features and get up and running really quickly. How is that decision making process? >> Well, I mean, you know, 90% at least of what we do build, it comes from what our customers ask for. So we don't, it's the onus is not on us. We listen to our customers, they tell us what they want us to build. Contact center solutions are their line of business applications are purchased by business decision makers and they're used to doing more buying than building. So they wanted to be more out of the box, more like pre-built, but we still are AWS. We make it very, very extensible, very easy to customize, like pull in other data sources. But when we look at how we are going to move up the stack and other areas, we just continue to listen to our customers. >> What's the biggest thing you learned in the pandemic from the team? What's the learnings coming out of the pandemic as hybrid world is upon us? >> I mean, I think a few things with, you know, starting, as you mentioned with the cloud, that the kind of idea of a contact center being a massive building, usually in the middle of America where, you know, people go and they sit and they have conversations. If that was really turned on its head and you can have very secure and accessible solutions through the cloud so that you can work from anywhere. So that was really fantastic to see. >> That's going to be interesting to see moving forward. How that paradigm shifts some centralized call centers, but a lot of this aggregated work that can be done. >> I mean, who knows the, you know, gig economy could be in the contact center, you know. >> Yeah, absolutely >> Yeah >> Maybe get some CUBE hosts, give us theCUBE Connect. We get some CUBE hosts remote. >> That's important work, yeah. >> We need, we need to talk. I got to got my phone number in that list. Annie, it's been fantastic to have you. >> Thank you guys so much. I really appreciate it. >> For John Furrier, this is Dave Nicholson telling you, thank you for joining our continuous coverage of AWS reinvent 2021. Stick with theCUBE for the best in hybrid event coverage. (upbeat music)

(upbeat music) >> From around the globe, it's theCUBE with digital coverage of Postgres Vision 2021 brought to you by EDB. >> Well, hi everybody John Walls here on theCUBE and we're now welcoming Jeremy Wilmot who is the chief product officer at ACI Worldwide part of the Postgres movement, you might say or certainly benefiting from the great value that Postgres is providing a number of enterprises across the globe. Jeremy good to see you today and first off, congratulations you are the first guest I've talked to maybe in a year and a half in their office. So good for you. >> Thanks (chuckles) John that's very kind of you John and great to see you and thanks for having me here. Yeah, it's great to be in the office, it really is. I'm here in Miami in South Florida and getting some sort of normalcy back is great for all of us and I'm certainly enjoying it. So thank you before (indistinct) has been. >> I'm sure you are, yeah, congratulations on that front. First off, let's talk about ACI Worldwide for the folks in our audience who aren't familiar with the payments, your role in terms of that payment ecosystem. Tell us a little bit about ACI Worldwide. >> Sure, well, primarily we're a software company. That's ACI, we started 1975 in Omaha, Nebraska built the first debit card system and ATM system for first National Bank of Omaha and over the last 45 years, we've globalized ourselves, we have, we are delivering mission-critical real-time payment systems across the world to banks to merchants to billers, we help them meet the payment needs of their consumers and their corporates. So we process, manage digital payments, we power omni-commerce and e-commerce payments, we present and process bill payments, we manage fraud, we manage the risk all within that and as I said on a global basis 13 of the G20 countries with a leading DDA account or current account payment processing software in those countries and have been for many years. >> So, as the CPO then quite obviously in the financial space your plate is quite full these days in terms of providing for your client base. How would you characterize maybe the evolution in terms of product development that you've been through in the financial world here over the past say, three to five years, where were you back then to where you are now and what role has Postgres played in that journey? >> Sure, yeah. So, specific to the Postgres part of the ecosystem, previously five-plus years ago our previous database solution was complex, it was expensive, it was hard to change and maintain and we leveraged multiple pieces of software from multiple vendors as a result of that. So at that time we looked for an alternative that was simpler and better and we went through a very comprehensive due diligence process, we explored both open source and license models of database to support our solution and when we looked at all of the options we determined that 2ndQuadrant Postgres was the one that provided the most comprehensive solution we were looking for. It had the right mix of capabilities and performance at the right total cost of ownership that we were looking for. And in the payments world as you can imagine, you've got to to be 24/7 365. And we also required a lower cost of ownership than we had before. But we also wanted a greater flexibility and time to market that we could pass on to our customers. And then the last thing I'd say that we were looking for was a multi-deployment capability. And what I mean by that is that we would be able to use this new platform, Postgres platform in our own data centers in our own private cloud, but we could also deploy it in the public cloud, whether we would run it or whether our customers would run it. We wanted that ability to mix and match between these different deployment options. >> So you've talked about a lot of key elements here attributes in terms of availability, accessibility reliability, security obviously. Walk us through those in terms of why you think 2ndQuadrant was addressing your needs in those particular areas or any others for that matter but what it was that checked the box specifically about what Postgres was offering you as opposed to what these other possible solutions and services were that you were looking at. >> Yeah, I think, we're very focused on being able to identify what our customers need and when they're offering services to consumers and to their corporates what is it that they require that's going to enable them to win and compete. And payments industry has a lot of cost pressures within it. It has regulation, it has consumer convenience and the whole movement of digitalization that puts a lot of downward pressure on the cost space. And those who are going to win in the payment space need to be able to address that. So, that is relevant for our banks, for our merchants, for the billers. They all come under very similar regulatory pressure and market pressure and as a result, the ability to reduce dramatically in a very significant way, the total cost of ownership upon which the payment software was going to be operating that was one of the key elements that was very important to us as we made that decision. The second one I think was to enable us to be able to do what we are good at and what our customers expect us to do. And that in turn enables them to focus on their core competencies. We're a software company, we own our own IP we manage our own software for the needs of the 24/7 365 payment requirements and therefore the merchant or the biller or the bank can really focus in on the digital experience for their customers, focusing on their core competencies and what they need to do to win. That was a second key factor for us. I think the third one for us was as well speed to market. Speed to market for ourselves and being competitive to the alternative to ACI, but also more importantly a speed to market for our customers. And there are, the payment world is highly regulated requires significant certification in order to launch new services that's often the long pole in the tent. So we want to be able to get to that point as quickly as possible. And being able to have a public cloud deployment open systems capabilities that would really allow us to pass on that speed to market to those customers. So for example, an acquirer, a payment acquirer moving into a new geographical country they want to compete in they can (indistinct) on their competitors by launching minimum viable products in six to nine months that is five years ago, that could have been a 24 to 30 months endeavor for them to take on. So I, those were important considerations for us as we were choosing a longterm partner for the Postgres world and the public cloud world. >> Obviously, so you've talked a lot about your relationship with your clients and I know you have a really keen awareness of the need to ensure that trust, to ensure that reliability to ensure the collaboration. How about your relationship on the other side with EDB and in terms of all those elements so how has that evolved over a period of time and what kind of service and what kind of value do you think are you deriving from that relationship now? >> So with EDB, first of all, our journey started with 2ndQuadrant and now EDB. And we were specifically looking at the, one area was at the Bi-Directional Replication BDR that we were wanting to support with our solutions particularly in the public cloud. And that was going to enable us to replace multiple pieces of software from multiple vendors. And so we were to create that solution that was right for ACI, it was right for our customers from a functionality and agility and a cost perspective. So technologically with the non-functional requirements and the reliability, availability, serviceability aspects that we were looking for that was in partnership with 2ndQuadrant and EDB, that was a key element. I think the second piece of it is we worked really well with 2ndQuadrant EDB in terms of partnering to meet the needs of the market. It's great to have the right technology in place but then you need your partners really to be able to work with you tactically real-time in order to win in the market and make it work. And I found that they'd been a great partner for us to be able to do that and to be able to react quickly, do the right thing and really enable us to be a great partner to our customers as we deliver real-time payments, as we deliver the acquiring capabilities, as we deliver a modernization for the big banks that we work with as well. >> Now, before I let you go, I'm going to give you a two-part question here. That's always one way to squeeze a little more info (laughing) to the guest. First off advice. You've been through this transformation obviously you're very happy with all that has transpired, so your advice to others who are considering this journey. And then secondly, what can they and you do you think expect in terms of future challenges, opportunities how we might want to frame that with Postgres? Like, where are we going from here, basically? So, two parts, advice and then where do you think this is headed? >> So advice, I certainly learnings from us versus advice is number one, be very thorough in the due diligence that you do and be very clear on what you want and what are your goals that you're looking for. So from an AGI perspective, we were clear that total cost of ownership in terms of the stack that we were going to be providing to our customers. That was very important, number one number two, nonfunctional requirements. So I've talked about the mission criticality of payments 24/7 365. That was a key second piece. And then the third one, ease of deployment. I talked about that, multi-cloud deployment that we were looking for. So we were clear what we wanted and we we took our time from a due diligence point of view. It's a multi-year decision being made so it's not something specifically I think we want to rush into. In terms of looking forward and where do we go from here? Performance is critical so further up performance enhancements, ability for rapid failover availability, near 100% availability that we're looking for five-nines and above, working together with Postgres in order to make those failovers more seamless because they will happen, particularly in the real-time payments world, where we're now seeing billions of transactions happening in a week and soon that will be in a day, they will need to be able to deal with. And for all of this to happen in a public cloud environment, we, I think all understand a lot of the benefits of public cloud and we need to be able to provide this failover availability capability in the public cloud but also in a hybrid cloud environments we're in a multi-cloud environment, so we need to keep working that and make that happen that will make Postgres a payment-grade infrastructure that could power the world's real-time payments and we would love to be able to do that into the future. >> Well, Jeremy thanks for the insights, we appreciate that and once again, congratulations on getting back in that office. I know it's probably a pretty welcomed addition to your regimen now. >> Yeah, John, thank you very much and thanks to everyone who's dialed in for this and John I look forward to welcoming you in the office soon. >> Very good sir, I look forward to that as well. I'll take you up on that in Miami for sure. John Walls here on theCUBE talking with Jeremy Wilmot is the chief product officer at ACI Worldwide. part of our Postgres Vision 2021 coverage. (upbeat music)

(upbeat music) >> From around the globe, it's theCUBE with digital coverage of Postgres Vision 2021 brought to you by EDB. >> Well, hi everybody John Walls here on theCUBE and we're now welcoming Jeremy Wilmot who is the chief product officer at ACI Worldwide part of the Postgres movement, you might say or certainly benefiting from the great value that Postgres is providing a number of enterprises across the globe. Jeremy good to see you today and first off, congratulations you are the first guest I've talked to maybe in a year and a half in their office. So good for you. >> Thanks (chuckles) John that's very kind of you John and great to see you and thanks for having me here. Yeah, it's great to be in the office, it really is. I'm here in Miami in South Florida and getting some sort of normalcy back is great for all of us and I'm certainly enjoying it. So thank you before (indistinct) has been. >> I'm sure you are, yeah, congratulations on that front. First off, let's talk about ACI Worldwide for the folks in our audience who aren't familiar with the payments, your role in terms of that payment ecosystem. Tell us a little bit about ACI Worldwide. >> Sure, well, primarily we're a software company. That's ACI, we started 1975 in Omaha, Nebraska built the first debit card system and ATM system for first National Bank of Omaha and over the last 45 years, we've globalized ourselves, we have, we are delivering mission-critical real-time payment systems across the world to banks to merchants to billers, we help them meet the payment needs of their consumers and their corporates. So we process, manage digital payments, we power omni-commerce and e-commerce payments, we present and process bill payments, we manage fraud, we manage the risk all within that and as I said on a global basis 13 of the G20 countries with a leading DDA account or current account payment processing software in those countries and have been for many years. >> So, as the CPO then quite obviously in the financial space your plate is quite full these days in terms of providing for your client base. How would you characterize maybe the evolution in terms of product development that you've been through in the financial world here over the past say, three to five years, where were you back then to where you are now and what role has Postgres played in that journey? >> Sure, yeah. So, specific to the Postgres part of the ecosystem, previously five-plus years ago our previous database solution was complex, it was expensive, it was hard to change and maintain and we leveraged multiple pieces of software from multiple vendors as a result of that. So at that time we looked for an alternative that was simpler and better and we went through a very comprehensive due diligence process, we explored both open source and license models of database to support our solution and when we looked at all of the options we determined that 2ndQuadrant Postgres was the one that provided the most comprehensive solution we were looking for. It had the right mix of capabilities and performance at the right total cost of ownership that we were looking for. And in the payments world as you can imagine, you've got to to be 24/7 365. And we also required a lower cost of ownership than we had before. But we also wanted a greater flexibility and time to market that we could pass on to our customers. And then the last thing I'd say that we were looking for was a multi-deployment capability. And what I mean by that is that we would be able to use this new platform, Postgres platform in our own data centers in our own private cloud, but we could also deploy it in the public cloud, whether we would run it or whether our customers would run it. We wanted that ability to mix and match between these different deployment options. >> So you've talked about a lot of key elements here attributes in terms of availability, accessibility reliability, security obviously. Walk us through those in terms of why you think 2ndQuadrant was addressing your needs in those particular areas or any others for that matter but what it was that checked the box specifically about what Postgres was offering you as opposed to what these other possible solutions and services were that you were looking at. >> Yeah, I think, we're very focused on being able to identify what our customers need and when they're offering services to consumers and to their corporates what is it that they require that's going to enable them to win and compete. And payments industry has a lot of cost pressures within it. It has regulation, it has consumer convenience and the whole movement of digitalization that puts a lot of downward pressure on the cost space. And those who are going to win in the payment space need to be able to address that. So, that is relevant for our banks, for our merchants, for the billers. They all come under very similar regulatory pressure and market pressure and as a result, the ability to reduce dramatically in a very significant way, the total cost of ownership upon which the payment software was going to be operating that was one of the key elements that was very important to us as we made that decision. The second one I think was to enable us to be able to do what we are good at and what our customers expect us to do. And that in turn enables them to focus on their core competencies. We're a software company, we own our own IP we manage our own software for the needs of the 24/7 365 payment requirements and therefore the merchant or the biller or the bank can really focus in on the digital experience for their customers, focusing on their core competencies and what they need to do to win. That was a second key factor for us. I think the third one for us was as well speed to market. Speed to market for ourselves and being competitive to the alternative to ACI, but also more importantly a speed to market for our customers. And there are, the payment world is highly regulated requires significant certification in order to launch new services that's often the long pole in the tent. So we want to be able to get to that point as quickly as possible. And being able to have a public cloud deployment open systems capabilities that would really allow us to pass on that speed to market to those customers. So for example, an acquirer, a payment acquirer moving into a new geographical country they want to compete in they can (indistinct) on their competitors by launching minimum viable products in six to nine months that is five years ago, that could have been a 24 to 30 months endeavor for them to take on. So I, those were important considerations for us as we were choosing a longterm partner for the Postgres world and the public cloud world. >> Obviously, so you've talked a lot about your relationship with your clients and I know you have a really keen awareness of the need to ensure that trust, to ensure that reliability to ensure the collaboration. How about your relationship on the other side with EDB and in terms of all those elements so how has that evolved over a period of time and what kind of service and what kind of value do you think are you deriving from that relationship now? >> So with EDB, first of all, our journey started with 2ndQuadrant and now EDB. And we were specifically looking at the, one area was at the Bi-Directional Replication BDR that we were wanting to support with our solutions particularly in the public cloud. And that was going to enable us to replace multiple pieces of software from multiple vendors. And so we were to create that solution that was right for ACI, it was right for our customers from a functionality and agility and a cost perspective. So technologically with the non-functional requirements and the reliability, availability, serviceability aspects that we were looking for that was in partnership with 2ndQuadrant and EDB, that was a key element. I think the second piece of it is we worked really well with 2ndQuadrant EDB in terms of partnering to meet the needs of the market. It's great to have the right technology in place but then you need your partners really to be able to work with you tactically real-time in order to win in the market and make it work. And I found that they'd been a great partner for us to be able to do that and to be able to react quickly, do the right thing and really enable us to be a great partner to our customers as we deliver real-time payments, as we deliver the acquiring capabilities, as we deliver a modernization for the big banks that we work with as well. >> Now, before I let you go, I'm going to give you a two-part question here. That's always one way to squeeze a little more info (laughing) to the guest. First off advice. You've been through this transformation obviously you're very happy with all that has transpired, so your advice to others who are considering this journey. And then secondly, what can they and you do you think expect in terms of future challenges, opportunities how we might want to frame that with Postgres? Like, where are we going from here, basically? So, two parts, advice and then where do you think this is headed? >> So advice, I certainly learnings from us versus advice is number one, be very thorough in the due diligence that you do and be very clear on what you want and what are your goals that you're looking for. So from an AGI perspective, we were clear that total cost of ownership in terms of the stack that we were going to be providing to our customers. That was very important, number one number two, nonfunctional requirements. So I've talked about the mission criticality of payments 24/7 365. That was a key second piece. And then the third one, ease of deployment. I talked about that, multi-cloud deployment that we were looking for. So we were clear what we wanted and we we took our time from a due diligence point of view. It's a multi-year decision being made so it's not something specifically I think we want to rush into. In terms of looking forward and where do we go from here? Performance is critical so further up performance enhancements, ability for rapid failover availability, near 100% availability that we're looking for five-nines and above, working together with Postgres in order to make those failovers more seamless because they will happen, particularly in the real-time payments world, where we're now seeing billions of transactions happening in a week and soon that will be in a day, they will need to be able to deal with. And for all of this to happen in a public cloud environment, we, I think all understand a lot of the benefits of public cloud and we need to be able to provide this failover availability capability in the public cloud but also in a hybrid cloud environments we're in a multi-cloud environment, so we need to keep working that and make that happen that will make Postgres a payment-grade infrastructure that could power the world's real-time payments and we would love to be able to do that into the future. >> Well, Jeremy thanks for the insights, we appreciate that and once again, congratulations on getting back in that office. I know it's probably a pretty welcomed addition to your regimen now. >> Yeah, John, thank you very much and thanks to everyone who's dialed in for this and John I look forward to welcoming you in the office soon. >> Very good sir, I look forward to that as well. I'll take you up on that in Miami for sure. John Walls here on theCUBE talking with Jeremy Wilmot is the chief product officer at ACI Worldwide. part of our Postgres Vision 2021 coverage. (upbeat music)

>> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE conversation. >> Hey, welcome back everybody Jeff Frick here with the cube. This is a special week it's Grace Hopper week, and Grace Hopper is the best name in tech conferences. The celebration of women in computing, and we've been going there for years we're not there this year, but one of the themes that comes up over and over at Grace Hopper is women and girls need to see women in positions that they can envision themselves being in someday. That is a really important piece of the whole diversity conversation is can I see people that I can role model after and I just want to bring up something from a couple years back from 2016 when we were there, we were there with Mimi Valdez, Christina Deoja and Dr. Jeanette Epps, Dr. Jeanette Epps is the astronaut on the right. They were there talking about "The Hidden Figures" movie. If you remember it came out 2016, it was about Katherine Johnson and all the black women working at NASA. They got no credit for doing all the math that basically keep all the astronauts safe and they made a terrific movie about it. And Janet is going up on the very first Blue Origin Space Mission Next year. This was announced a couple of months ago, so again, phenomenal leadership, black lady astronaut, going to go into space and really provide a face for a lot of young girls that want to get into that and its clearly a great STEM opportunity. So we're excited to have four terrific women today that well also are the leaders that the younger women can look up to and follow their career. So we're excited to have them so we're just going to go around. We got four terrific guests, our first one is Annabel Chang, She is the Head of State Policy and Government Regulations at Waymo. Annabel great to see you, where are you coming in from today? >> from San Francisco >> Jeff: Awesome. Next up is Inamarie Johnson. She is the Chief People and Diversity Officer for Zendesk Inamarie, great to see you. Where are you calling in from today? >> Great to be here. I am calling in from Palos Verdes the state >> Jeff: awesome >> in Southern California. >> Jeff: Some of the benefits of a virtual sometimes we can, we couldn't do that without the power of the internet. And next up is Jennifer Cabalquinto she is the Chief Financial Officer of the Golden State Warriors. Jennifer, great to see you Where are you coming in from today? >> Well, I wish I was coming in from the Chase Center in San Francisco but I'm actually calling in from Santa Cruz California today. >> Jeff: Right, It's good to see you and you can surf a lot better down there. So that's probably not all bad. And finally to round out our panelists, Kate Hogan, she is the COO of North America for Accenture. Kate, great to see you as well. Where are you coming in from today? >> Well, it's good to see you too. I am coming in from the office actually in San Jose. >> Jeff: From the office in San Jose. All right, So let's get into it . You guys are all very senior, you've been doing this for a long time. We're in a kind of a crazy period of time in terms of diversity with all the kind of social unrest that's happening. So let's talk about some of your first your journeys and I want to start with you Annabel. You're a lawyer you got into lawyering. You did lawyering with Diane Feinstein, kind of some politics, and also the city of San Francisco. And then you made this move over to tech. Talk about that decision and what went into that decision and how did you get into tech? 'cause we know part of the problem with diversity is a pipeline problem. You came over from the law side of the house. >> Yes, and to be honest politics and the law are pretty homogenous. So when I made the move to tech, it was still a lot of the same, but what I knew is that I could be an attorney anywhere from Omaha Nebraska to Miami Florida. But what I couldn't do was work for a disruptive company, potentially a unicorn. And I seized that opportunity and (indistinct) Lyft early on before Ride Hailing and Ride Sharing was even a thing. So it was an exciting opportunity. And I joined right at the exact moment that made myself really meaningful in the organization. And I'm hoping that I'm doing the same thing right now at Waymo. >> Great, Inamarie you've come from one of my favorite stories I like to talk about from the old school Clorox great product management. I always like to joke that Silicon Valley needs a pipeline back to Cincinnati and Proctor and Gamble to get good product managers out here. You were in the classic, right? You were there, you were at Honeywell Plantronics, and then you jumped over to tech. Tell us a little bit about that move. Cause I'm sure selling Clorox is a lot different than selling the terrific service that you guys provide at Zendesk. I'm always happy when I see Zendesk in my customer service return email, I know I'm going to get taken care of. >> Oh wow, that's great. We love customers like you., so thank you for that. My journey is you're right from a fortune 50 sort of more portfolio type company into tech. And I think one of the reasons is because when tech is starting out and that's what Zendesk was a few five years back or so very much an early stage growth company, two things are top of mind, one, how do we become more global? And how do we make sure that we can go up market and attract enterprise grade customers? And so my experience having only been in those types of companies was very interesting for a startup. And what was interesting for me is I got to live in a world where there were great growth targets and numbers, things I had never seen. And the agility, the speed, the head plus heart really resonated with my background. So super glad to be in tech, but you're right. It's a little different than a consumer products. >> Right, and then Jennifer, you're in a completely different world, right? So you worked for the Golden State Warriors, which everybody knows is an NBA team, but I don't know that everyone knows really how progressive the Warriors are beyond just basketball in terms of the new Chase Center, all the different events that you guys put on it. And really the leadership there has decided we really want to be an entertainment company of which the Golden State Warrior basketball team has a very, very important piece, you've come from the entertainment industry. So that's probably how they found you, but you're in the financial role. You've always been in the financial role, not traditionally thought about as a lot of women in terms of a proportion of total people in that. So tell us a little bit about your experience being in finance, in entertainment, and then making this kind of hop over to, I guess Uber entertainment. I don't know even how you would classify the warriors. >> Sports entertainment, live entertainment. Yeah, it's interesting when the Warriors opportunity came up, I naturally said well no, I don't have any sports background. And it's something that we women tend to do, right? We self edit and we want to check every box before we think that we're qualified. And the reality is my background is in entertainment and the Warriors were looking to build their own venue, which has been a very large construction project. I was the CFO at Universal Studios Hollywood. And what do we do there? We build large attractions, which are just large construction projects and we're in the entertainment business. And so that sort of B to C was a natural sort of transition for me going from where I was with Universal Studios over to the Warriors. I think a finance career is such a great career for women. And I think we're finding more and more women entering it. It is one that you sort of understand your hills and valleys, you know when you're going to be busy and so you can kind of schedule around that. I think it's really... it provides that you have a seat at the table. And so I think it's a career choice that I think is becoming more and more available to women certainly more now than it was when I first started. >> Yeah, It's interesting cause I think a lot of people think of women naturally in human resources roles. My wife was a head of human resources back in the day, or a lot of marketing, but not necessarily on the finance side. And then Kate go over to you. You're one of the rare birds you've been at Accenture  for over 20 years. So you must like airplanes and travel to stay there that long. But doing a little homework for this, I saw a really interesting piece of you talking about your boss challenging you to ask for more work, to ask for a new opportunity. And I thought that was really insightful that you, you picked up on that like Oh, I guess it's incumbent on me to ask for more, not necessarily wait for that to be given to me, it sounds like a really seminal moment in your career. >> It was important but before I tell you that story, because it was an important moment of my career and probably something that a lot of the women here on the panel here can relate to as well. You mentioned airplanes and it made me think of my dad. My father was in the air force and I remember him telling stories when I was little about his career change from the air force into a career in telecommunications. So technology for me growing up Jeff was, it was kind of part of the dinner table. I mean it was just a conversation that was constantly ongoing in our house. And I also, as a young girl, I loved playing video games. We had a Tandy computer down in the basement and I remember spending too many hours playing video games down there. And so for me my history and my really at a young age, my experience and curiosity around tech was there. And so maybe that's, what's fueling my inspiration to stay at Accenture for as long as I have. And you're right It's been two decades, which feels tremendous, but I've had the chance to work across a bunch of different industries, but you're right. I mean, during that time and I relate with what Jennifer said in terms of self editing, right? Women do this and I'm no exception, I did this. And I do remember I'm a mentor and a sponsor of mine who called me up when I'm kind of I was at a pivotal moment in my career and he said you know Kate, I've been waiting for you to call me and tell me you want this job. And I never even thought about it. I mean I just never thought that I'd be a candidate for the job and let alone somebody waiting for me to kind of make the phone call. I haven't made that mistake again, (laughing) but I like to believe I learned from it, but it was an important lesson. >> It's such a great lesson and women are often accused of being a little bit too passive and not necessarily looking out for in salary negotiations or looking for that promotion or kind of stepping up to take the crappy job because that's another thing we hear over and over from successful people is that some point in their career, they took that job that nobody else wanted. They took that challenge that really enabled them to take a different path and really a different Ascension. And I'm just curious if there's any stories on that or in terms of a leader or a mentor, whether it was in the career, somebody that you either knew or didn't know that was someone that you got kind of strength from kind of climbing through your own, kind of career progression. Will go to you first Annabel. >> I actually would love to talk about the salary negotiations piece because I have a group of friends about that we've been to meeting together once a month for the last six years now. And one of the things that we committed to being very transparent with each other about was salary negotiations and signing bonuses and all of the hard topics that you kind of don't want to talk about as a manager and the women that I'm in this group with span all types of different industries. And I've learned so much from them, from my different job transitions about understanding the signing bonus, understanding equity, which is totally foreign to me coming from law and politics. And that was one of the most impactful tools that I've ever had was a group of people that I could be open with talking about salary negotiations and talking about how to really manage equity. Those are totally foreign to me up until this group of women really connected me to these topics and gave me some of that expertise. So that is something I strongly encourage is that if you haven't openly talked about salary negotiations before you should begin to do so. >> It begs the question, how was the sensitivity between the person that was making a lot of money and the person that wasn't? And how did you kind of work through that as a group for the greater good of everyone? >> Yeah, I think what's really eye opening is that for example, We had friends who were friends who were on tech, we had friends who were actually the entrepreneurs starting their own businesses or law firm, associates, law firm partners, people in PR, so we understood that there was going to be differences within industry and frankly in scale, but it was understanding even the tools, whether I think the most interesting one would be signing bonus, right? Because up until a few years ago, recruiters could ask you what you made and how do you avoid that question? How do you anchor yourself to a lower salary range or avoid that happening? I didn't know this, I didn't know how to do that. And a couple of women that had been in more senior negotiations shared ways to make sure that I was pinning myself to a higher salary range that I wanted to be in. >> That's great. That's a great story and really important to like say pin. it's a lot of logistical details, right? You just need to learn the techniques like any other skill. Inamarie, I wonder if you've got a story to share here. >> Sure. I just want to say, I love the example that you just gave because it's something I'm super passionate about, which is transparency and trust. Then I think that we're building that every day into all of our people processes. So sure, talk about sign on bonuses, talk about pay parody because that is the landscape. But a quick story for me, I would say is all about stepping into uncertainty. And when I coach younger professionals of course women, I often talk about, don't be afraid to step into the role where all of the answers are not vetted down because at the end of the day, you can influence what those answers are. I still remember when Honeywell asked me to leave the comfort of California and to come to the East coast to New Jersey and bring my family. And I was doing well in my career. I didn't feel like I needed to do that, but I was willing after some coaching to step into that uncertainty. And it was one of the best pivotal moment in my career. I didn't always know who I was going to work with. I didn't know the challenges and scope I would take on, but those were some of the biggest learning experiences and opportunities and it made me a better executive. So that's always my coaching, like go where the answers aren't quite vetted down because you can influence that as a leader. >> That's great, I mean, Beth Comstock former vice chair at GE, one of her keynotes I saw had a great line, get comfortable with being uncomfortable. And I think that its a really good kind of message, especially in the time we're living in with accelerated change. But I'm curious, Inamarie was the person that got you to take that commitment. Would you consider that a sponsor, a mentor, was it a boss? Was it maybe somebody not at work, your spouse or a friend that said go for it. What kind of pushed you over the edge to take that? >> It's a great question. It was actually the boss I was going to work for. He was the CHRO, and he said something that was so important to me that I've often said it to others. And he said trust me, he's like I know you don't have all the answers, I know we don't have this role all figured out, I know you're going to move your family, but if you trust me, there is a ton of learning on the other side of this. And sometimes that's the best thing a boss can do is say we will go on this journey together. I will help you figure it out. So it was a boss, but I think it was that trust and that willingness for him to stand and go alongside of me that made me pick up my family and be willing to move across the country. And we stayed five years and really, I am not the same executive because of that experience. >> Right, that's a great story, Jennifer, I want to go to you, you work for two owners that are so progressive and I remember when Joe Lacob came on the floor a few years back and was booed aggressively coming into a franchise that hadn't seen success in a very long time, making really aggressive moves in terms of personnel, both at the coaches and the players level, the GM level. But he had a vision and he stuck to it. And the net net was tremendous success. I wonder if you can share any of the stories, for you coming into that organization and being able to feel kind of that level of potential success and really kind of the vision and also really a focus on execution to make the vision real cause vision without execution doesn't really mean much. If you could share some stories of working for somebody like Joe Lacob, who's so visionary but also executes so very, very effectively. >> Yeah, Joe is, well I have the honor of working for Joe, for Rick Welts to who's our president. Who's living legend with the NBA with Peter Guber. Our leadership at the Warriors are truly visionary and they set audacious targets. And I would say from a story the most recent is, right now what we're living through today. And I will say Joe will not accept that we are not having games with fans. I agree he is so committed to trying to solve for this and he has really put the organization sort of on his back cause we're all like well, what do we do? And he has just refused to settle and is looking down every path as to how do we ensure the safety of our fans, the safety of our players, but how do we get back to live entertainment? And this is like a daily mantra and now the entire organization is so focused on this and it is because of his vision. And I think you need leaders like that who can set audacious goals, who can think beyond what's happening today and really energize the entire organization. And that's really what he's done. And when I talked to my peers and other teams in there they're talking about trying to close out their season or do these things. And they're like well, we're talking about, how do we open the building? And we're going to have fans, we're going to do this. And they look at me and they're like, what are you talking about? And I said, well we are so fortunate. We have leadership that just is not going to settle. Like they are just always looking to get out of whatever it is that's happening and fix it. So Joe is so committed His background, he's an epidemiologist major I think. Can you imagine how unique a background that is and how timely. And so his knowledge of just around the pandemic and how the virus is spread. And I mean it's phenomenal to watch him work and leverage sort of his business acumen, his science acumen and really think through how do we solve this. Its amazing. >> The other thing thing that you had said before is that you basically intentionally told people that they need to rethink their jobs, right? You didn't necessarily want to give them permission to get you told them we need to rethink their jobs. And it's a really interesting approach when the main business is just not happening, right? There's just no people coming through the door and paying for tickets and buying beers and hotdogs. It's a really interesting talk. And I'm curious, kind of what was the reception from the people like hey, you're the boss, you just figure it out or were they like hey, this is terrific that he pressed me to come up with some good ideas. >> Yeah, I think when all of this happened, we were resolved to make sure that our workforce is safe and that they had the tools that they needed to get through their day. But then we really challenged them with re imagining what the next normal is. Because when we come out of this, we want to be ahead of everybody else. And that comes again from the vision that Joe set, that we're going to use this time to make ourselves better internally because we have the time. I mean, we had been racing towards opening Chase Center and not having time to pause. Now let's use this time to really rethink how we're doing business. What can we do better? And I think it's really reinvigorated teams to really think and innovate in their own areas because you can innovate anything, right?. We're innovating how you pay payables, we're all innovating, we're rethinking the fan experience and queuing and lines and all of these things because now we have the time that it's really something that top down we want to come out of this stronger. >> Right, that's great. Kate I'll go to you, Julie Sweet, I'm a big fan of Julie Sweet. we went to the same school so go go Claremont. But she's been super aggressive lately on a lot of these things, there was a get to... I think it's called Getting to 50 50 by 25 initiative, a formal initiative with very specific goals and objectives. And then there was a recent thing in terms of doing some stuff in New York with retraining. And then as you said, military being close to your heart, a real specific military recruiting process, that's formal and in place. And when you see that type of leadership and formal programs put in place not just words, really encouraging, really inspirational, and that's how you actually get stuff done as you get even the consulting businesses, if you can't measure it, you can't improve it. >> Yeah Jeff, you're exactly right. And as Jennifer was talking, Julie is exactly who I was thinking about in my mind as well, because I think it takes strong leadership and courage to set bold bold goals, right? And you talked about a few of those bold goals and Julie has certainly been at the forefront of that. One of the goals we set in 2018 actually was as you said to achieve essentially a gender balance workforce. So 50% men, 50% women by 2025, I mean, that's ambitious for any company, but for us at the time we were 400,000 people. They were 500, 6,000 globally. So when you set a goal like that, it's a bold goal and it's a bold vision. And we have over 40% today, We're well on our path to get to 50%, I think by 2025. And I was really proud to share that goal in front of a group of 200 clients the day that it came out, it's a proud moment. And I think it takes leaders like Julie and many others by the way that are also setting bold goals, not just in my company to turn the dial here on gender equality in the workforce, but it's not just about gender equality. You mentioned something I think it's probably at as, or more important right now. And that's the fact that at least our leadership has taken a Stand, a pretty bold stand against social injustice and racism, >> Right which is... >> And so through that we've made some very transparent goals in North America in terms of the recruitment and retention of our black African American, Hispanic American, Latinex communities. We've set a goal to increase those populations in our workforce by 60% by 2025. And we're requiring mandatory training for all of our people to be able to identify and speak up against racism. Again, it takes courage and it takes a voice. And I think it takes setting bold goals to make a change and these are changes we're committed to. >> Right, that's terrific. I mean, we started the conversation with Grace Hopper, they put out an index for companies that don't have their own kind of internal measure to do surveys again so you can get kind of longitudinal studies over time and see how you're improving Inamarie, I want to go to you on the social justice thing. I mean, you've talked a lot about values and culture. It's a huge part of what you say. And I think that the quote that you use, if I can steal it is " no culture eats strategy for breakfast" and with the social injustice. I mean, you came out with special values just about what Zendesk is doing on social injustice. And I thought I was actually looking up just your regular core mission and value statement. And this is what came up on my Google search. So I wanted to A, you published this in a blog in June, taking a really proactive stand. And I think you mentioned something before that, but then you're kind of stuck in this role as a mind reader. I wonder if you can share a little bit of your thoughts of taking a proactive stand and what Zendesk is doing both you personally, as well as a company in supporting this. And then what did you say as a binder Cause I think these are difficult kind of uncharted waters on one hand, on the other hand, a lot of people say, hello, this has been going on forever. You guys are just now seeing cellphone footage of madness. >> Yeah Wow, there's a lot in there. Let me go to the mind reader comments, cause people are probably like, what is that about? My point was last December, November timing. I've been the Chief People Officer for about two years And I decided that it really was time with support from my CEO that Zendesk have a Chief Diversity Officer sitting in at the top of the company, really putting a face to a lot of the efforts we were doing. And so the mind reader part comes in little did I know how important that stance would become, in the may June Timing? So I joked that, it almost felt like I could have been a mind reader, but as to what have we done, a couple of things I would call out that I think are really aligned with who we are as a company because our culture is highly threaded with the concept of empathy it's been there from our beginning. We have always tried to be a company that walks in the shoes of our customers. So in may with the death of George Floyd and the world kind of snapping and all of the racial injustice, what we said is we wanted to not stay silent. And so most of my postings and points of view were that as a company, we would take a stand both internally and externally and we would also partner with other companies and organizations that are doing the big work. And I think that is the humble part of it, we can't do it all at Zendesk, we can't write all the wrongs, but we can be in partnership and service with other organizations. So we used funding and we supported those organizations and partnerships. The other thing that I would say we did that was super important along that empathy is that we posted space for our employees to come together and talk about the hurt and the pain and the experiences that were going on during those times and we called those empathy circles. And what I loved is initially, it was through our mosaic community, which is what we call our Brown and black and persons of color employee resource group. But it grew into something bigger. We ended up doing five of these empathy circles around the globe and as leadership, what we were there to do is to listen and stand as an ally and support. And the stories were life changing. And the stories really talked about a number of injustice and racism aspects that are happening around the world. And so we are committed to that journey, we will continue to support our employees, we will continue to partner and we're doing a number of the things that have been mentioned. But those empathy circles, I think were definitely a turning point for us as an organization. >> That's great, and people need it right? They need a place to talk and they also need a place to listen if it's not their experience and to be empathetic, if you just have no data or no knowledge of something, you need to be educated So that is phenomenal. I want to go to you Jennifer. Cause obviously the NBA has been very, very progressive on this topic both as a league, and then of course the Warriors. We were joking before. I mean, I don't think Steph Curry has ever had a verbal misstep in the history of his time in the NBA, the guy so eloquent and so well-spoken, but I wonder if you can share kind of inside the inner circle in terms of the conversations, that the NBA enabled right. For everything from the jerseys and going out on marches and then also from the team level, how did that kind of come down and what's of the perception inside the building? >> Sure, obviously I'm so proud to be part of a league that is as progressive and has given voice and loud, all the teams, all the athletes to express how they feel, The Warriors have always been committed to creating a diverse and equitable workplace and being part of a diverse and equitable community. I mean that's something that we've always said, but I think the situation really allowed us, over the summer to come up with a real formal response, aligning ourselves with the Black Lives Matter movement in a really meaningful way, but also in a way that allows us to iterate because as you say, it's evolving and we're learning. So we created or discussed four pillars that we wanted to work around. And that was really around wallet, heart, beat, and then tongue or voice. And Wallet is really around putting our money where our mouth is, right? And supporting organizations and groups that aligned with the values that we were trying to move forward. Heart is around engaging our employees and our fan base really, right? And so during this time we actually launched our employee resource groups for the first time and really excited and energized about what that's doing for our workforce. This is about promoting real action, civic engagement, advocacy work in the community and what we've always been really focused in a community, but this really hones it around areas that we can all rally around, right? So registration and we're really focused on supporting the election day results in terms of like having our facilities open to all the electorate. So we're going to have our San Francisco arena be a ballot drop off, our Oakland facilities is a polling site, Santa Cruz site is also a polling location, So really promoting sort of that civic engagement and causing people to really take action. heart is all around being inclusive and developing that culture that we think is really reflective of the community. And voice is really amplifying and celebrating one, the ideas, the (indistinct) want to put forth in the community, but really understanding everybody's culture and really just providing and using the platform really to provide a basis in which as our players, like Steph Curry and the rest want to share their own experiences. we have a platform that can't be matched by any pedigree, right? I mean, it's the Warriors. So I think really getting focused and rallying around these pillars, and then we can iterate and continue to grow as we define the things that we want to get involved in. >> That's terrific. So I have like pages and pages and pages of notes and could probably do this for hours and hours, but unfortunately we don't have that much time we have to wrap. So what I want to do is give you each of you the last word again as we know from this problem, right? It's not necessarily a pipeline problem, it's really a retention problem. We hear that all the time from Girls in Code and Girls in Tech. So what I'd like you to do just to wrap is just a couple of two or three sentences to a 25 year old, a young woman sitting across from you having coffee socially distanced about what you would tell her early in the career, not in college but kind of early on, what would the be the two or three sentences that you would share with that person across the table and Annabel, we'll start with you. >> Yeah, I will have to make a pitch for transportation. So in transportation only 15% of the workforce is made up of women. And so my advice would be that there are these fields, there are these opportunities where you can make a massive impact on the future of how people move or how they consume things or how they interact with the world around them. And my hope is that being at Waymo, with our self driving car technology, that we are going to change the world. And I am one of the initial people in this group to help make that happen. And one thing that I would add is women spend almost an hour a day, shuttling their kids around, and we will give you back that time one day with our self driving cars so that I'm a mom. And I know that that is going to be incredibly powerful on our daily lives. >> Jeff: That's great. Kate, I think I might know what you're already going to say, but well maybe you have something else you wanted to say too. >> I don't know, It'll be interesting. Like if I was sitting across the table from a 25 year old right now I would say a couple of things first I'd say look intentionally for a company that has an inclusive culture. Intentionally seek out the company that has an inclusive culture, because we know that companies that have inclusive cultures retain women in tech longer. And the companies that can build inclusive cultures will retain women in tech, double, double the amount that they are today in the next 10 years. That means we could put another 1.4 million women in tech and keep them in tech by 2030. So I'd really encourage them to look for that. I'd encouraged them to look for companies that have support network and reinforcements for their success, and to obviously find a Waymo car so that they can not have to worry where kids are on for an hour when you're parenting in a few years. >> Jeff: I love the intentional, it's such a great word. Inamarie, >> I'd like to imagine that I'm sitting across from a 25 year old woman of color. And what I would say is be authentically you and know that you belong in the organization that you are seeking and you were there because you have a unique perspective and a voice that needs to be heard. And don't try to be anything that you're not, be who you are and bring that voice and that perspective, because the company will be a better company, the management team will be a better management team, the workforce will be a better workforce when you belong, thrive and share that voice. >> I love that, I love that. That's why you're the Chief People Officer and not Human Resources Officer, cause people are not resources like steel and cars and this and that. All right, Jennifer, will go to you for the wrap. >> Oh my gosh, I can't follow that. But yes, I would say advocate for yourself and know your value. I think really understanding what you're worth and being willing to fight for that is critical. And I think it's something that women need to do more. >> Awesome, well again, I wish we could go all day, but I will let you get back to your very, very busy day jobs. Thank you for participating and sharing your insight. I think it's super helpful. And there and as we said at the beginning, there's no better example for young girls and young women than to see people like you in leadership roles and to hear your voices. So thank you for sharing. >> Thank you. >> All right. >> Thank you. >> Okay thank you. >> Thank you >> All right, so that was our diversity panel. I hope you enjoyed it, I sure did. I'm looking forward to chapter two. We'll get it scheduled as soon as we can. Thanks for watching. We'll see you next time. (upbeat music)

>> [Narrator] From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. (mellow electronic music) Now here's your host, Stu Miniman. >> Hi, I'm Stu Miniman, and welcome to theCUBE's Boston area studio. Gonna be having a different conversation today. We often talk about cloud and data and all the various technologies, and we're gonna talk about a different application of them in the customer experience base. And to help me to do that, thought leader in the space, Steve Papas, who's the senior vice president and chief marketing officer of Panviva. Steve, thanks so much for joining us. >> Thanks for having me, this is great. >> I go back in my roots, and my first job out of school, the company I started with, put everybody into customer support. There's no better way to kinda understand how something works and how people interact with technology as well as the product than taking those phone calls when something goes wrong in customer support. So I have a little bit of experience in the CX space, as I believe you call it these days. You know, watch from the technology space, the call centers, and all those kind, but, maybe start us out, when you say CX here in 2019, what's the scope, what are we talking about that, and we'll go from there. >> That's a great place to start, Stu, 'cause really when we're talking about customer experience, or CX as it's being known, we're really talking about what is the customer's experience when they're interacting with our organization or they're even transacting with our organization. So if you think about it, there's probably the three things that they could do with a company. They can either interact with them and get some information, maybe they're checking on a rate for a mortgage or looking for a car loan or getting claim information from their health insurer, or they're transacting, they're buying something, they're conveying some kind of a transaction together. Or there may be more of a back office approach to it, so that someone's operating on behalf of the customer. So when we think about all of those three dimensions, it's really about, was it frictionless, was it easy to do, did they get to the point where they felt delighted and they're willing to provide a reference or a testimonial because they're gushing because the experience was so good. They got what they needed and they're willing to tell people about it. >> Yeah, that's great. You see friction lists, it reminds me of what we talked for years about with cloud computing, talked about bringing joy and having authentic conversations. We've been talking for years about how social media and engagement should be. I have to think that the balance and interaction between people and machines and technology have to be a hot-button topic there. One of my favorite events that we did a few years ago was with MIT, talking about automation, are the robots going to take over everything? And what we know tends to work best is that there needs to be a balance of the robots, chatbots, whatever they are, and people. It can't be all of one, or even all of the other because it either get too costly, or the experience might not be optimal. >> Yeah, I think you're exactly right. I think all of those different things have their place. And if you think about it, it's kind of like the pie. You're adding pieces to the pie. So you're adding the chatbot as another method, or another medium that someone can interact with the organization, but it's not the be-all and end-all. There has to be a level of human aspect to a lot of things. I'll give you an example. You're not gonna call your hospital when you're feeling some chest pains, and want the chatbot to be on the other end. >> [Stu] Right. >> So there has to be, we have to temper what types of technology we use with what areas, and we have to be thinking about the customer. I always advocate, you always think about the customer at the center of the universe, and make sure the customer has a seat at every decision table. So when we're thinking about bringing technology into organizations, we have to think about, well, how does this make the customer's experience better? Does it help them? Does it make their interaction with us better? And overall, does that technology make the types of customers and lifetimes value increase for us as organizations? >> You hear a lot of organizations, I'm customer focused, you go read Jeff Bezos talks about, I need to be paranoid about my customer, I need to think about everything they're doing, because if they change and they leave us, what are we left with? Bring us into customer experience, what does that matter, how do we get beyond lip service, talking, yeah, it's great to listen to the customers but, I've gotta worry about my bottom line and my employees and stockholders and things like that. >> Sure, well today, if you think about customer service, good customer service, every company in the world is talking about it. That's the baseline now, right? That is where we begin, and we're moving up from there to a level of customer service. So if we're thinking about customer service as, it has to be good, but how do you get from that good to great scenario, it's how do we train our people, how do we make sure they're empowered to provide the customer with all they need, and give them a little bit of decision making power when it makes the difference of keeping that customer for life, and maybe their children and friends and relatives, or potentially losing them at that single interaction. So, when we're thinking about the bottom line, we always have to think that every interaction could be the last interaction. But also, every interaction's an opportunity to make that relationship better. So we have to think about that in terms of how we do things, as well as what technology surrounds that. And obviously, the negative side of customer experience is if we do it wrong, we're certainly losing, but if we do it right, it's exponentially better. >> I'm curious Steve, what your thoughts are, how do I measure that? In the B to B world we talk about the net promoter score, NPS, and we love it, and it's great when you see a high net promoter score, but when you understand the details and what goes under it, that's only part of the picture. And boy do I agree with you about, if you have that opportunity to talk to a customer and turn it around, uh, you know. If you spend any time in the space, it's great when somebody comes back and has something good to say, but if somebody comes and says something bad, it's usually only the tip of the iceberg. There's usually other people that can have it, and you have to take that opportunity to turn it into something good. So, metrics and, how do we measure whether we're doing good or bad in the space? >> Absolutely, well, the contact center itself is metriced to the nth degree anyway. Net promoter score is one dimension of how we have to looks at things. I'll tell you a story, that I recommend to every C-suite person that I interact with, and I'm a member of a lot of associations, that the best thing that they can do is spend time in the contact center. Double check into those calls. Not only is it the best focus group you can ever pay for, that you already have, but also it allows for a C-suite, whether it's a CEO or the Chief Operating Officer or the CFO it allows them to understand really how the interactions are happening between the customer as well as the organization. But it goes a step further. Not only do you measure the customer satisfaction levels, but you also need to measure your employee satisfaction levels that way too. Because having employees that have the tools necessary to provide the best service possible, as well as make sure that they have the training, they have the empowerment to do it. Once you have those things in place, I always say that the CEO and others in the C-suite should be listening to those calls to understand, does the employee have all the right tools to make that customer interaction better. And to extend that lifetime value or not. And that's one way, which is much more of a qualitative versus the metrics, but it's one that's missed all the time. And I have CEOs tell me time and time again, after they've done it, it was absolutely enlightening for them to say, I never saw that part of the business, from that vantage point. >> Steve, bring us inside those call centers a little bit. I've got a little bit of background, but it was often overworked, underappreciated, very much metrics-driven. There's the big thing on the wall, saying how long the average call's been waiting. Have you hit the number that you needed to do? Has it gotten better? What's it like in these environments today? Outsourcing was a big push for a number of years, what's it like in those call centers? >> Outsourcing is still a big thing. A lot of U.S. companies have started bringing some of the things back in-house too. When they found that the metrics might not have been there. But they're also holding their outsources to a higher standard, now. So they're providing not only the training, but what I'm seeing as far as trends, is that they're providing the how-to much better. They're realizing that having a single source of truth that your employees are using, your customers can access via self-service, as well as the outsourcer, is really the key to making all of this work so you have the portability of process much better. Now the call centers are getting much better because they're starting to move more onto the knowledge side as well as the process side. They're looking internal. It's not good enough for them to say, well, it's been working fine, right? It's now to the point with, how much better can we get it to work? How do we get to the last mile? How do we get to the point where these customers are willing and call back to say, hey, I had a great experience. So, we're finding that one of the keys was making sure the employees, the people on the front lines, have what they need the second that they need it. Not to pop their head over the cube, not to escalate to a help desk, right? Because that just increases the overall cost of a contact center. Not to be shuffling through papers, or flipping through the pages of a binder every few minutes. But giving them the tools that they need so that A. They know exactly what the process is, they can remain compliant with the process, they can navigate the myriad of applications that are open on the desktop, as well as know how to say the right things, they avoid saying the wrong things, and they don't come across as robotic. And that's one of the keys that is happening now. And we see that trend happening more and more in contact centers, and I probably walk in and out of 150 of them in a year, and see them from the inside. You know the one thing that I always look around in a contact center is, if there's lots of sticky notes around the monitors, if there's lots of binders on the desk and papers up on the cubes, there are process problems. There are opportunities to make that organization run a lot smoother on behalf of the customer. >> Steve, related to CX, one of the topics you've written about is Omnichannel. Maybe you can explain what that is, and what you're finding. >> Sure, so Omnichannel is really geared around how do we communicate with our customers, our partners, our dealers, our distributors, etc. So how do we communicate properly on any channel necessary that the customer wants? We always say that it used to be multichannel, right? We would have the telephone, we would have maybe the IVR is giving them directions and allowing them some information when they call in. But now customers want to be communicated with on Skype, or Slack, or Facebook Messenger, or Twitter or Instagram, or various other methods that are accessible to the consumer. The consumer has a lot of information as well as a lot of power at their fingertips now, that they probably didn't have, 10, 15 years ago. Now, the Omnichannel is really geared around creating a universal way of, of communicating with the customer where they want to be communicated with, and we say that's probably the best channel, is, what does the customer want, right? Where are they, so how do we get to them where they are? And to make that work there's technology involved. And also if we want to say, well how do we take care of our customers at 2:00 AM, maybe with a chatbot, so they can get some of the information that they need when they need it. It's all about time, right? The thing that we're trying to solve now is the problem of time. How do we make sure that we get the information into the hands of the person that needs it, whether it's our employee or our customer, or maybe our third-party dealers, distributors, etc. How do we get that information in a timely manner so that they can do something of action, of value? And that's really the key. So Omnichannel really is gearing around, how do we maintain all of those? But, some of the keys to it, and I wanna put those out there, is we have to curate content better. We have to look at the fact that, you don't write a procedure for the employee to speak over the phone the same way you're gonna write for Alexa as a virtual assistant, to be speaking out into the air. So we have to think about content curation as, what are the multiple versions of the same thing that need to be housed in one place? And then how do we orchestrate them at the moment of need? When Alexa does call in and says, I need information about your hours, that information and the version of that content has to be pushed in a sub-second method to go to the right channel at the right time, in the right format. >> That machine-to-machine discussion added a whole new dimension for a lot of companies. >> [Steve] Absolutely. To try to solve that. Great, give us, we're here towards the beginning of the year still, at 2019, give us a little bit look forward, what are the challenges, what are the things that are exciting you, as we look throughout this year. >> Sure. I think we're really looking forward to more companies understanding the customer. And by that I don't mean that they have to go through an entire customer journey mapping, but that is a good place to start. But at the end of the day, you have to make sure the operator 24 in Omaha, Nebraska knows the result of all that journey mapping. So there has to be a third dimension if you will, after you've done your analysis and your mapping, is how do they execute? On all of the stuff we've found in the customer journey mapping, how do they execute at that point, at that cold face of business happening, for the benefit of the customer, as well as for the employee satisfaction. So we're seeing that customer-centric conversations are increasing. By that I mean that companies are looking for, what are all the methods, the simple methods that we can incorporate today, which doesn't necessarily mean bringing in all kinds of technology, what are the methods that we can start bringing in to make our employees feel empowered around the whole customer experience paradigm. So, that I'm seeing, is happening. The other one is, as you referred to Jeff Bezos, whenever you have a decision in your company, and you've got the conference room table, put one chair over there and just put the name Customer on the chair, and allow the customer to have a decision. Allow the customer to have a seat at the decision table. And by that I mean that, always think, as you're making your moves in 2019 and 2020 and beyond, what does it mean to the customer? How is that going to affect the customer, and will it be positive for their experience? Those are the types of things that are getting real exciting, that companies are finally starting to look at those and they're not saying good enough is good enough anymore, and they're not looking at, well the whole operation is factored into the cost of doing business. They're all starting to say, how can we do better? And mostly the thing that's driving that is they have to get better at customer experience. Because if you think about it, price, everybody knows the price now, they can search for everything that they're shopping for, and they know who's got the same price, and pretty much there's an equilibrium on that. So, if price is one thing, and size and color and all of those things are similar, and all of the components of that are similar across the board, well what are companies starting to compete on? Companies in 2019 and 2020 are gonna start competing on the experience. So if you think about one of the best competitive advantages moving forward? Is, what is the experience that we give, over and above our competition. And that is so important, we're seeing the trends moving to that and honestly, that's what's really starts to excite me as companies are moving down this path. >> Steve, one of the things I know is that you've written a number of things on this topic. If people want to learn more about CX what are some of the resources they can go to? Which, not trying to pitch product from Panviva, but really thought leadership interface. >> We also work with a lot of thought leaders, and we only approach it from an educational perspective. Matter of fact, we just published a new e-book on customer experience. All the tips from ten industry leaders in customer experience, and that's available on our website, at Panviva.com. They can connect with me at Twitter @SXP01, or by email at spapas@panviva.com. And I'm happy to point them in the direction of any of these resources that fundamentally will help them start a workshop inside, and start the thought process of, how can we get better on behalf of the customer? >> Alright well, Steve, really appreciate you helping to educate our community a little bit more about the CX base and definitely do check out, either reach out to Steve directly, or check out the Panviva.com website to learn more. And be sure to check out thecube.net for all the upcoming shows as well as the archive of everything we have. If you go into the search box you can search on topic, company, or person, we've got the database of thousands of interviews we've done in the past. So, once again, I'm Stu Miniman, and thank you for watching The Cube. (electronic music)

(upbeat music) >> Live, from Las Vegas, it's theCUBE. Covering the AWS Accenture Executive Summit. Brought to you by Accenture. >> Welcome back to theCUBE's live coverage of the AWS Executive Summit. I'm your host, Rebecca Knight. We have three guests for this segment. We have Jim Phillips, Cloud Architect, Mutual of Omaha. Roy Bacharach, Senior Principle Technology, Accenture. And Brian Bohan, Global Business Lead, AWS. Thank you so much for coming on the show. >> Thanks for having us. >> Thank you. >> Thanks for having us. >> So we're talking about the transformation of the Contact Center but before we get started there, let's tell our viewers a little bit about Mutual of Omaha, your business, your target demographic and what you do. >> So Mutual of Omaha's a 109 year old insurance company. We have, our biggest market segments are in the senior health space and then, in the life space. So we service customers with a wide variety of needs, everything from Medicare supplement policies so we have like seasonal surges in business and things like that to people who are concerned about like, how do they, how do they prepare for their family for their life insurance needs and things like that. So, we've been around for quite a while. Predominantly, we'd been servicing our customer base through agents and as that shift occurs, right, we've been looking at how do we provide a much more finely focused view of the customer and emphasizing that within our contact centers with the recent creation of our service practice. >> So it was really just this idea of let's think about how to touch the customer in a different way. That that was really the business imperative toward this move. >> Right, so previously, we had, this all started in 2016 when we decided to take a focus on the customers, specifically from the service practice. So instead of service kind of being like an overhead associated with a product line, what we decided to do is we decided to really have something where the focus was on the end to end customer experience and how do we make that consistent across your interaction with Mutual of Omaha. That then lead us to reevaluate how we were doing our contacts centers and that's when we became involved with Roy and Accenture to look at what are our options to really kind of improve that experience for the customer. >> So when a customer like Mutual of Omaha comes to you Roy, with this business problem, what, how do you walk them through it and have them think about it? >> Okay, so we typically start at the top, you know, and understanding not only the business strategy but their current state of their technology architecture. And then, you try to work through the specific gaps, you know, gap analysis. What are they missing to get them there? With Mutual of Omaha, it was really they were being held back by their legacy on premise solution. You know, high levels of technical dept, huge complexity to support maintain and to make the changes. You know, so it was in that analysis we -- It was easy to see that the cloud was probably the best option for them. >> And did Amazon Connect immediately stand out? >> So even initially, when we were looking at options for this, Amazon Connect wasn't actually even on our list, so that was something that was brought to our attention during the sort of short-listing of candidates process. And then, you know, when we really looked at it, it just kind of blew our minds. So, you know, Roy had mentioned about taking a look at the gap analysis. So, as sort of as embarrassing or sad as this may seem, right, the decision to do something is a lot easier when there are a lot of gaps. We had a lot of gaps between what we could deliver with our current technology solution and then, what really the business strategy outcomes were wanting us to do. So, it did make a decision to look at completely sort of reinventing how we do the contact centers a lot easier position to consider. >> Bryan, in terms of the nuts and bolts of making Amazon Connect, can you give our viewers a little sense about really what is the infrastructure we're talking about here? >> So the interesting thing with Amazon Connect is it's really the call center platform capability that Amazon.com has been using for a number of years and that we decided to commercialize and externalize to customers like Mutual of Omaha. And so, like a lot things with AWS, what's nice about it is that it's you can start small. You can layer it in and it can integrate into some of the existing technologies and investments that you've made. It's not a rip and replace and then you can scale it as you see success and you can scale it up and down. So it's very economical as well. And so, it's an area we're really excited to see Mutual of Omaha really on the cutting edge there but we're seeing, with Accenture, a lot of momentum with this platform in insurance, financial services, even as CPG companies become more focused on delivering services, it's changing how they have to interact with customers so it's a great platform for that, a great starting place. >> So Amazon, a famously customer centric company. So what are the kinds of. You think, oh customers will love this but in fact, we were talking before the cameras were rolling, there was a little bit of resistance. >> So you have to think about like, how do you introduce this type of sort of radical change from what was traditionally just an exclusively a hands on service process that was, you know, agents and contact centers with an audience demographic that is not what you would think of as being like cutting edge in terms of technology adoption. But what we found through things like paying a lot of attention to our call flow development with Accenture. Paying lot of attention actually to our voice tuning and getting the voice of the customer to understand like what that voice tuning and how well that worked. We were able to actually get a more positive reception for the Connect solution that we even over like professionally recorded voice talent on it. So, you do have to address like all of the, all of the like customer touch points within the contact center and think about how do you manage the change within your audience demographic and how do you manage that adoption. But, you know, it's your customers, it's your agents. How do you make them comfortable with the solution? Right, because the, you know, customer can detect it, an agents uncomfortable with the solution that they're using. Right, how do you make this kind of like really seamless? So we took a, put a lot of emphasis on customer experience development as part of this. We didn't, we did not take any of our existing call flows and just put them in Connect. So all of our call flows were re-architected. >> What are some of the best practices that have emerged because he has just pointed out so many of the kind of challenges of implementing this new kind of approach and system with both clients and also the workforce itself. I mean, what would you say, what is sort of your advice in best practices that have emerged in terms of Mutual of Omaha's experience? >> You know, I think it's really start with the desired customer experience that you want. You know, so start with that customer experience and then with Amazon Connect, you know, likes Amazon Web Services, you can deliver that experience. So start there, throw out the legacy call flows, the legacy IVR scripts and start from scratch with the customer experience at the top of mind. And then you can get there. >> Yeah, I would second that. The, 'cause managing change internally organization, like if your focus is exclusively on what the customer experience is, that shortcuts a lot of arguments within the organization about what's the right thing to do because you know, everybody tends to kind of sub optimize for whatever their stakeholder perspective is. >> If your clearly focused on what the customer is looking for, that actually clarifies a lot what your internal conversations are. >> How do you three work together in terms of this tri-partnership? Accenture, AWS and Mutual of Omaha. How do you collaborate? >> Yeah, so I guess first from the partnership perspective, like I talked about, Connect and modernizing customer care, is a really big focus area for us as a partnership and a big investment area. So, we worked with Accenture and gotten their teams very much skilled up on the new platform and they have done a great job of integrating it into their existing practice. So now, when we come to a customer like Mutual of Omaha, we, you know, Accenture's got a very strong point of view, they've got technology skills behind it and they know how the solution can solve customer problems. So that's my job is to make sure that foundation is there and then, the team takes it from there really with the client. >> Yeah, I would say our experience with Amazon around this, is they're really very interested in the experience that we're having and how we can provide feedback around our particular use cases and understanding like what are the types of, what are the types of things that would make our stuff more successful. So because we work with a combination of health and life insurance products, things like HIPAA eligibility for services are a big deal for us and when you look at how the ecosystem is all tied together with Connect, that has really kind of, we've got a lot of attention and help from Amazon with regards to dealing with like HIPAA incompliance issues associated with how we put the solutions together and it's been really helpful for us. >> I want to talk about the role of empathy in this kind of technology, because as we know, we are dealing with really difficult times in peoples' lives. That they are in need of these kinds of products and services. So how do you make sure that the technology is taking that into account? >> Yeah, that's an excellent point. So we tend to think of financial services products as kind of sort of emotionally neutral or cold even, right? But when you're dealing with insurance, and a lot of times you're dealing with people who are calling and they're in a very emotional sort of situation. The, one of the things that is really good for that, that we hope to leverage much more in the future is being able to get the transcripts of the conversations out, so we can understand as part using that data that's coming from the interactions with the Lex bots and understanding that data as the customer works through the call flows to be able to look at how do we continue to improve these around how that customers responding to it, so that we can get to better customer experiences. Like, in often times, it's a very highly emotional situation. If you're dealing with like a life claims contact center, you're dealing with someone who's just recently experienced a loss of a loved one and as a result, peoples' patience is really low, they're really stressed, they're facing -- You know, our demographic is selling final expense policies and that means that people are facing a lot of financial uncertainty in addition to emotional distress. >> Right. >> Being able to take that information and use that to continually tune things for delightful customer outcomes is really important to us. >> So, what's next for Mutual of Omaha? >> So really, what's next for us is we're in the process of major migration of our contact center agents onto it. Once that is completed, that allows us to kind of get rid of some existing technology debt with our on premise telephony solution. And then we really start to get into kind of the good stuff. Right, so that's like integration with our customer portal, taking more advantage of what we want to do from a machine learning and AI perspective with regards to what we can get from the call data and the customer, the customer interaction. And really starting to kind of like make a huge jump in terms of what that customer experience can be. >> Great, I look forward to hearing more about it at next years Executive Summit. (laughing) >> Yeah, it would be great to be back. >> Great. >> Jim, Roy, Brian. Thank you so much for coming on theCUBE. >> Thank you. >> Thank you. >> Thanks for having us. >> I'm Rebecca Knight. We will have more from the AWS Executive Summit and theCUBE's live coverage coming up in just a little bit. (upbeat music)

>> Live from Las Vegas, it's theCUBE covering the AWS Accenture Executive Summit. Brought to you by Accenture. >> Welcome back everyone to theCUBE's live coverage of the AWS Executive Summit here at the Venetian in Las Vegas, Nevada. I'm your host, Rebecca Knight. We have two guests for this segment, we have Adam Burden, Chief Software Engineer at Accenture and Chris Scott, AWS North America Lead. Thank you both so much for coming back on theCUBE for returning. >> Sure, thanks for having us. >> Awesome, thanks. >> So we're talking today about future systems. So, in the past, when Accenture has talked about this, it's talked about the future of applications, future applications, now it's future systems. What are we talking about first of all? >> Sure. >> And why the switch? >> Look, it's actually a key question for us, and I think that we aspire to be to our clients thought leaders about where we believe that the technology landscape of tomorrow is heading. To help give them guidance about the path that they should chart their own systems on today. And we wrote kind of a seminal paper several years ago, called The Future of Applications, and it laid out different strategies that our clients, we recommended to our clients that they follow in order to build the technology systems of tomorrow. And in it, we have three characteristics, liquid, intelligent, and connected. And the outcome from that was great. It was an inspiration for many of them to build their future technology landscape and that language of liquid, intelligent, connected from a white paper was written five years ago has really entered the lexicon of many of our clients in industry. Now, however, they've seen the success, but they want to be able to do that truly at scale. They want to be able to take advantage of applications and the way that they're built and designed for tomorrow, but do that at an enterprise wide scale. And we felt like it was a time for us to go back and reflect upon what we had wrote about as the future of applications, and said, let's think about how systems, three years on, four years on, are going to be built for tomorrow. And that's exactly what we did in future systems. So future systems, you can look at it as a compass for how they'll continue to chart their path to be able to scale the new and close something that we call the innovation achievement gap. And this innovation achievement gap is really kind of the diagnosis that we put on there of where, they've seen success in pockets of innovation across their enterprise, but they want to be able to have that occurring across all of their businesses simultaneously. And we believe that following some of the prescriptive advice that we provide in future systems, that clients, our clients, would be able to do exactly that. >> So I want to dig into that research a little bit and you said, liquid, intelligent, connected. Those really became part of the vernacular. This year, it's three new-- >> Three new ones that's right! >> Three new ones, boundaryless, adaptable, and radically human. These are the characteristics that you say are the secret sauce for a successful system. >> That's right. >> So, let's get into these a little bit, let's start with boundaryless. >> Sure, boundaryless is great to talk here about, reinvent, because it really is all about cloud and how you use cloud. But before I get ahead of myself, and really define about what boundaryless is. Naturally, it's about breaking down barriers between systems, between businesses, and between humans and machines. And the successful companies that do this can really quickly respond to the market 'cause their systems are very agile and can react. There are really two really important elements to boundaryless, first is cloud. Being able to leverage cloud not just as a data center, but as an innovation platform to be able to do more, leveraging the great services from AWS, like Lambda and API Gateway and across the entire stack of AWS services and leveraging automation and really getting beyond infrastructure, to treating it infrastructure as code with an environment is an important component of that. The second is decoupling. It's decoupling applications and data. For years, we designed systems and the data that's part of that system would remain within that system. But you didn't get the value out of it by linking that across various parts of the organization. So it's important to decouple that data and application and give that access to other parts of the organization. The other important part is decoupling applications from legacy infrastructure. I talked a little bit about infrastructure as code. That's an important component of it. And lastly, it's decoupling integrated systems into loosely coupled applications and systems. And that's important because you develop components that you can share across the organization. You do really well for one system, you want to share that component across other systems in the organization. So Adam and I were talking a little bit about boundaryless and different examples that we've seen in working with our clients. Adam had a really good one that he was talking about before. >> Yeah, so this, I think this characteristic kind of sets the foundation for how future systems are going to be constructed and when you think about the restrictions that you perhaps even falsely place on applications today by sort of limiting how they can actually expand or grow or scale over time, you're limiting the potential growth of your business, and that's why we think it's so important that as you're designing and building systems of tomorrow and we're working with a client right now who is rethinking their loyalty program, it's Cathay Pacific, a big airline. >> We're going to be speaking with them later on theCUBE. >> Yeah, and it's a remarkable story and you're going to get a lot of details of this later, but what I really love about this is they've embraced this concept of boundaryless by introducing blockchain technologies in cloud into how their loyalty points program is going to work in the future. So whether they have 10 partners, 1,000 partners, or 10,000 partners in there, the way that they've constructed their system is it is going to elastically scale to be able to support all that, and it's going to make it faster and better with higher quality than ever before for them to onboard new partners and even more importantly, serve their mile point program customers better. So great example of boundaryless and how the systems of tomorrow are going to be built. >> And particularly because you said that that was a big challenge, that it's not only not communicating with your partners, but it's also not communicating within the business, the different units not talking to each other. >> Exactly. >> So let's move onto adaptable, and adapt, you think every system's got to be adaptable, duh! But what do we need, let's break it down. >> It's actually, you know, this is a really interesting challenge for us and you're starting to see the early stages now of systems and technologies that can embrace these characteristics. Basically what we mean by adaptable is that these are systems that autonomously change. They anticipate, for example, new loads or performance expectations or they anticipate certain changes in user patterns or behavior and actually reorganize themselves without you telling them to do it. So they're taking advantage of trusted data and artificial intelligence and other elements so that they can perform better and that you can focus more attention on the business value that's delivered on top of them. A great analogy that I've used for this is imagine you've got kind of two gears that are turning towards each other, right? And one gear has like a really big tooth on it and you can kind of see it coming and it's going to wreck the other gear when it gets there. Well, imagine that gear sort of sees that coming and adapts, and says, oh, okay, I can make this area wider, and that tooth will fit right in there. That's what adaptable is all about, is it's looking at what's happening around it and it's adjusting itself so it can perform better in the enterprise instead of falling over. And that makes your systems more reliable, it makes your customer experiences better and allows you to have systems that will make you one of these high performers of tomorrow. >> Anticipating and adapting? >> Anticipating and adapting, exactly right. >> Finally, the final characteristic, radically human, I love this. Define what it is, and then I want to talk about the kinds of companies that you've seen do this best. >> Yeah, radically human, I love the term too. I think it's great, and it's really about creating systems that are simple, they're elegant, but they're also immersive to our customers. Natural language processing, computer vision, machine learning are all important components and it's really about how these systems listen, they see, they can adapt, they understand what's going on just like people do. And it's interesting that technology's become so invasive in our lives, but it's also become invisible and it's woven into the fabric of what we do, with digital assistants and all the things that are out there today. It's such an important part of what we do. So it's important to create systems that are aligned to the users, and this is created an interesting inversion. We would design systems in the past that would gather requirements and then eventually, when the system went live, you'd have to train all of the users how to use that system and you would have to adapt the user to the system. Now what we're talking about is developing systems that can adapt, to the adaptable point that Adam mentioned, but really change to work better for the users. We were talking a little bit before as well about Amazon Connect, and a great example of this is leveraging Connect and omnichannel capabilities to allow customers to interact with customer service and businesses the way they want to interact. Whether that's via phone or through online or text message, find the right medium to get them the right answers as fast as possible. A great example of this is a client we're working with, Mutual of Omaha, who's going to be here on theCUBE and we've done a breakout session with them. They've been through this whole journey and they've really gotten much better customer engagement through this. >> So it's not necessarily feeling that your technology is mimicking a human, it's really just the technology is what you, the human, want it to be, in whatever format, I mean, is that right? >> That's a really interesting way of putting it. It's about so many times, and there's examples all around us, where people have kind of adapted to technology rather than us adapting to, or rather than that, technology adapting to us. I mean, even the keyboard, I have right here, right, the keyboard? This keyboard and the layout was invented in 1870, okay? And it was invented in a way to actually slow down typists so that the arms wouldn't get stuck on it. I mean, why are we still suffering with a keyboard that limits how fast we can type this many years later. And that's the point we're trying to make with radically human, is that we should be thinking about how technology is designed around people rather than the other way around. >> So that's a real cultural shift that has to take place within companies, so what are some of the best practices that sort of how companies can become more radically human and their systems become more radically human? >> Well, look, there's human-centered design, is a really important aspect of it, and then a lot of great emerging thought in that space. We think that design thinking contributes a lot to kind of really thinking from the very beginning about how do we build applications or technology systems in the future that are going to work with people so it's human plus machine, not human versus machine. And we think the outcomes that you get from embracing some of those approaches allow you to build solutions and design them that are much more radically human in the future. And this is really important. You're going to be more productive, more effective, your workforce is going to be happier, your customers are going to be happier, and they're going to be more engaged. And there's a paradox here too. Is it the more we do this, actually the less you'll see of the technology, because it'll become embedded in the things around us. So maybe, I've actually written some things in the past that says AI is the new UI, and the end of screens, right? So maybe it doesn't really mean the end of screens, but we're going to see a lot less screens because it's easier for people to hear information, sometimes, than it is to actually see it. >> Right, this is really fascinating stuff. Thank you both so much for coming back on theCUBE for these great conversation. >> Oh, we're happy to, thank you, Rebecca. >> Adam and Chris, thank you. >> Thank you. >> I'm Rebecca Knight, we will have more of theCUBE's live coverage of the AWS Executive Summit coming up in just a little bit. (techno music)

(techno music) [Narrator]- Live from Las Vegas. It's the CUBE covering the AWS Accenture Executive Summit brought to you by Accenture. >> Welcome back everyone to the CUBE live coverage of the AWS Executive Summit here at the Venetian, in Las Vegas, Nevada. I am your host, Rebecca Knight. We have two guests for this segment. We have Chris Scott, Managing Director, Accenture AWS Business Group and J.C. Novoa, Senior Manager, Accenture AWS Business Group. Chris, J.C., thank you much for coming on the show. >> No problem. Thank you Rebecca for having us here. >> So we're talking today about the call center transformation. And I'm excited about it as a customer who loathes call centers. So Chris, why don't you paint the picture for us right now of what a call center looks like, the customer experience, and then also the business experience too? >> Absolutely. Thanks again for having us here. We're really excited to talk about Amazon Connect. I think it's one of the services in Amazon that everyone, as you were saying, can really identify with 'cause they've all been through that kind of customer experience before. So I think what's really interesting about contact center is that it really hasn't dramatically changed last ten or fifteen years. It's all kind of the same, kind of phone tree type conversations. So I think there's a few companies that do it a little bit better but still it hasn't really radically changed over the last ten or fifteen years. And I think Amazon's really playing in that space of disruption, in really thinking how can we do something different in the contact center. So I think there's a lot of challenges that we see with contact centers today. They're not scalable, right? And a lot of representatives spend 90% of their day handling inbound calls. And that's just not scalable. You can't train people up to address that. Also there's an issue with reporting. You don't get as much data about the customer experience. When they call you you don't understand their intent and what happened and how you improve in the process for the next round. And then, I think another big challenge they have is the solutions for contact centers are very complex. And it takes a lot of time to address and change those solutions. So you amass a lot of technical debt over the years of operating this 'cause you can't make those changes that you really want to. So I think Amazon is really playing in the space, like I said, in disruption, in really creating the better customer experience. >> Not only creating that but making it easier making it more human, to some extent Enabling customers to kind of peer behind the green veil and say you know what? This is not that difficult. You should be able to implement something like Amazon connect, which is a contact center as a service. And not have to worry about infrastructure, not have to worry about all the details and the minutiae that goes into actually making that happen and then be able to innovate immediately. Being able to introduce additional artificial intelligence to make that contact center experience more human. Again, to be able to introduce natural language processing and understanding, and then all these capabilities out of the box are able to be integrated with Amazon Connect in a way that improves that, and then additionally increase containment from their perspective of dedicating live agent interactions for things that matter. And then automating some of the activities that are more Q&A, FAQ type of things that can be addressed by a machine in a manner that makes it more understandable by the person that is calling. So that's kind of where we're going here with Amazon Connect. >> I want to dig into some of those features and capabilities because what you're describing is making me excited about the next time I need to call a contact center. So explain exactly how this will work for a customer who calls up. What will happen and then what's sort of happening behind the scenes with the technology? >> So when a customer calls, the idea will be to try to first identify the intent, as Chris was mentioning. What are they calling for? And then be able to identify who they are. Maybe there were interactions that were happening in different channels. These are some of the things that Amazon Connect provides, which is a mechanism for our clients to experience Omni channel and kind of graduate across experiences for their client. Being able to leverage that is important. >> Yeah, Omni channel. I don't think I can underscore the importance of that enough. Because it's all about interacting with a system and a business the way you want to interact with them. Some folks want to be able to call up and have a conversation with an agent, but others want more rapid response. Maybe using a chatbot, or even moving between all of those different channels within the same conversations. When we work with a client, for instance Utility, in order to pick a date to schedule service, it's a lot easier to get a text message, go to a web site, pull up the little calendar and choose your date rather than the representative giving you ten options and you're thinking which one works best for you. And then you're also feeling I've got to rush because this person needs to move on to the next customer. So this Omni channel thing really creates a much, much better experience for the user. >> And Amazon Connect kind of enables that, in a sense. It's our entry point for that Omni channel experience. >> So describe for me how Accenture works with clients implementing Amazon Connect. >> Yes, normally we want to be able to understand what the client's needs is, and understand their customer base. So we go through the process of identifying what that use case looks like. How do we then determine what are the different channels that they want to leverage initially? How do we help them graduate to the full Omni channel experience, one channel at a time? We conduct these workshops, we identify what is the current need. How do we ramp up, and how do we introduce Amazon Connect? Chris will tell us a little bit about the... >> Yeah, great example, and I believe you're speaking with them a little bit, Rebecca, is Mutual of Omaha. Great client that we've worked with, and actually doing a break out session here at re:Invent to talk about their journey out to Amazon Connect. They really started with, you know the problem statement is they wanted to improve their customer engagement. They wanted to retain customers, they wanted to establish new customers and sell new services to their existing customers. And they said the best way for us to do this is to improve our customer engagement through our contact center. So they went about in the market, looked at all the different solutions, looked at their existing solution and they said Amazon is the platform we want to use. We want to innovate on Amazon. It provides us a lot better features, that Omni channel experience. And that's let to better customer engagement, it's led to better tools for the agents, and world leading computer response and machine learning through Amazon. And an overall better experience. Because now they can also get more metrics about what's going on, and they can tailor that and continue to improve their solution and respond to customers, and improve customer engagement. >> So I'm curious though, starting with the business problem, which is Mutual of Omaha, they said we want to do better by our current customers and then also attract new ones. Retract and retain. So is that where you like, is that the starting point in terms of how you start to work with clients? >> That was their starting point. And they said "We found a solution, and that's Amazon. "Now we need to find a partner "that's going to help us with that transformation." And that's when they selected Accenture to help them with the journey. >> But starting with the question... >> Correct, absolutely. They want to understand, a couple of things; they want to be able to innovate, but they also want to be able to provide this excellent customer experience. And what has happened thus far is the current offerings that they have in place are on premise, they're not reliable. They're not scalable and they're costly. At the end of the day, a lot of this actually hits their bottom line. But the reality is that they want to be able to delight their customers. And be able to provide channels that eventually are going to grow with their customer base. Because if you think about it today, that customer is going to expect more of these interactions to follow them through their day. In the morning they might be able to talk to a device. While in the car they might want to talk to a live agent, but when they're at the office they might want to be able to chat with someone. And that kind of day in the life of a customer is what we're actually trying to help our clients solution. >> Also to your point, the folks that are interested in Connect are no longer just I.T. and AWS. It's now the business wanting to engage with AWS in really understanding this new solution. So I think this is a game changer in how Amazon interacts with businesses. 'Cause now it's the business users that are buying, not just I.T. >> And it's those decision makers who are ultimately... talk a little bit about who you go to in terms of... is it the CIO, is it the CTO, about the business decision, and what kind of ROI these folks want to see. >> I think it's a little bit of both, and there's a client that you've been working with, J.C., that's kind of been on this journey. We've started with them, they're looking to expand their business and for that new business expansion, they were looking to have a new solution for their contact center. So we started selling to I.T., because that was the main buyer. But after I.T. heard about, wow, these are all the cool things that we can do, here's how we can improve our customer engagement. We went to the head of customer service for this company, and they were blown away by the capabilities. They said wow, this is really a platform that we can innovate on. It changes. >> And the beauty about that is that those synergies actually is something that we brought together. They themselves were not talking to each other, within the company. So how they can help each other. But the reality is the customer experience relies on data and all these workloads that were helping I.T. move to the cloud actually going to power Amazon Connect and create this more human and natural experience to their customers. So that's kind of the end game here. >> So when you are bringing this new technology to these companies, how hard is it, how big of a challenge is it to get the workforce onboard. (laughter) In some ways the technology's the easy part. >> It is, but I don't think it's all that difficult because people are really excited about doing something different. As I said, this space in contact center hasn't really radically changed in ten to fifteen years, so now folks are saying wait, I can do that? And it doesn't take me three months to do it? I can have what I want next week? That's a game changer, I think that that's what's really getting people excited. And that's why the folks in the business want to work with us to implement Connect. Yes, of course there is change management, which I understand. There's folks that are going to push back, and we understand that. But the reality is at the end of the day, we have the buy in from the executive team in these companies that we're working with and they understand the value. And at the end of the day they help us drive change. Operationally is very much something that we're doing with them, together as a journey, but at the end of the day we're also working with the individual stakeholders within the company, actually to deliver. So we're taking them there. >> Final question. What is the most exciting thing that you're seeing, you're thinking about innovating on for the contact center of the future? What will it be like? >> Artificial Intelligence. >> Yeah, absolutely. If you think about how that conversation is going to happen in the future, you're not going to know whether you're talking to a human or you're talking to a machine, and if we can achieve that, then I think we are getting there. So that's what I see. >> Absolutely. It's understanding customer intent, and being able to intelligently route someone to the right place, without even knowing necessarily why they're calling, or having to tell the agent what they're trying to do. We know why they're calling. Maybe they had a billing issue in the past. So we know that ahead of time, and we can address that proactively in a conversation. >> Great. Well Chris and J.C., thank you both so much for coming on the CUBE. It was a pleasure talking to you. >> Thank you. >> Thank you very much Rebecca. I'm Rebecca Knight. We'll have more from the AWS Executive Summit coming up in just a little bit. (techno music)

>> Narrator: Live, from Munich, Germany, it's the CUBE. Covering IBM, Fast Track Your Data. Brought to you by IBM. >> Welcome to Munich everybody. This is a special presentation of the CUBE, Fast Track Your Data, brought to you by IBM. My name is Dave Vellante. And I'm here with my cohost, Jim Kobielus. Jim, good to see you. Really good to see you in Munich. >> Jim: I'm glad I made it. >> Thanks for being here. So last year Jim and I hosted a panel at New York City on the CUBE. And it was quite an experience. We had, I think it was nine or 10 data scientists and we felt like that was a lot of people to organize and talk about data science. Well today, we're going to do a repeat of that. With a little bit of twist on topics. And we've got five data scientists. We're here live, in Munich. And we're going to kick off the Fast Track Your Data event with this data science panel. So I'm going to now introduce some of the panelists, or all of the panelists. Then we'll get into the discussions. I'm going to start with Lillian Pierson. Lillian thanks very much for being on the panel. You are in data science. You focus on training executives, students, and you're really a coach but with a lot of data science expertise based in Thailand, so welcome. >> Thank you, thank you so much for having me. >> Dave: You're very welcome. And so, I want to start with sort of when you focus on training people, data science, where do you start? >> Well it depends on the course that I'm teaching. But I try and start at the beginning so for my Big Data course, I actually start back at the fundamental concepts and definitions they would even need to understand in order to understand the basics of what Big Data is, data engineering. So, terms like data governance. Going into the vocabulary that makes up the very introduction of the course, so that later on the students can really grasp the concepts I present to them. You know I'm teaching a deep learning course as well, so in that case I start at a lot more advanced concepts. So it just really depends on the level of the course. >> Great, and we're going to come back to this topic of women in tech. But you know, we looked at some CUBE data the other day. About 17% of the technology industry comprises women. And so we're a little bit over that on our data science panel, we're about 20% today. So we'll come back to that topic. But I don't know if there's anything you would add? >> I'm really passionate about women in tech and women who code, in particular. And I'm connected with a lot of female programmers through Instagram. And we're supporting each other. So I'd love to take any questions you have on what we're doing in that space. At least as far as what's happening across the Instagram platform. >> Great, we'll circle back to that. All right, let me introduce Chris Penn. Chris, Boston based, all right, SMI. Chris is a marketing expert. Really trying to help people understand how to get, turn data into value from a marketing perspective. It's a very important topic. Not only because we get people to buy stuff but also understanding some of the risks associated with things like GDPR, which is coming up. So Chris, tell us a little bit about your background and your practice. >> So I actually started in IT and worked at a start up. And that's where I made the transition to marketing. Because marketing has much better parties. But what's really interesting about the way data science is infiltrating marketing is the technology came in first. You know, everything went digital. And now we're at a point where there's so much data. And most marketers, they kind of got into marketing as sort of the arts and crafts field. And are realizing now, they need a very strong, mathematical, statistical background. So one of the things, Adam, the reason why we're here and IBM is helping out tremendously is, making a lot of the data more accessible to people who do not have a data science background and probably never will. >> Great, okay thank you. I'm going to introduce Ronald Van Loon. Ronald, your practice is really all about helping people extract value out of data, driving competitive advantage, business advantage, or organizational excellence. Tell us a little bit about yourself, your background, and your practice. >> Basically, I've three different backgrounds. On one hand, I'm a director at a data consultancy firm called Adversitement. Where we help companies to become data driven. Mainly large companies. I'm an advisory board member at Simply Learn, which is an e-learning platform, especially also for big data analytics. And on the other hand I'm a blogger and I host a series of webinars. >> Okay, great, now Dez, Dez Blanchfield, I met you on Twitter, you know, probably a couple of years ago. We first really started to collaborate last year. We've spend a fair amount of time together. You are a data scientist, but you're also a jack of all trades. You've got a technology background. You sit on a number of boards. You work very active with public policy. So tell us a little bit more about what you're doing these days, a little bit more about your background. >> Sure, I think my primary challenge these days is communication. Trying to join the dots between my technical background and deeply technical pedigree, to just plain English, every day language, and business speak. So bridging that technical world with what's happening in the boardroom. Toe to toe with the geeks to plain English to execs in boards. And just hand hold them and steward them through the journey of the challenges they're facing. Whether it's the enormous rapid of change and the pace of change, that's just almost exhaustive and causing them to sprint. But not just sprint in one race but in multiple lanes at the same time. As well as some of the really big things that are coming up, that we've seen like GDPR. So it's that communication challenge and just hand holding people through that journey and that mix of technical and commercial experience. >> Great, thank you, and finally Joe Caserta. Founder and president of Caserta Concepts. Joe you're a practitioner. You're in the front lines, helping organizations, similar to Ronald. Extracting value from data. Translate that into competitive advantage. Tell us a little bit about what you're doing these days in Caserta Concepts. >> Thanks Dave, thanks for having me. Yeah, so Caserta's been around. I've been doing this for 30 years now. And natural progressions have been just getting more from application development, to data warehousing, to big data analytics, to data science. Very, very organically, that's just because it's where businesses need the help the most, over the years. And right now, the big focus is governance. At least in my world. Trying to govern when you have a bunch of disparate data coming from a bunch of systems that you have no control over, right? Like social media, and third party data systems. Bringing it in and how to you organize it? How do you ingest it? How do you govern it? How do you keep it safe? And also help to define ownership of the data within an organization within an enterprise? That's also a very hot topic. Which ties back into GDPR. >> Great, okay, so we're going to be unpacking a lot of topics associated with the expertise that these individuals have. I'm going to bring in Jim Kobielus, to the conversation. Jim, the newest Wikibon analyst. And newest member of the SiliconANGLE Media Team. Jim, get us started off. >> Yeah, so we're at an event, at an IBM event where machine learning and data science are at the heart of it. There are really three core themes here. Machine learning and data science, on the one hand. Unified governance on the other. And hybrid data management. I want to circle back or focus on machine learning. Machine learning is the coin of the realm, right now in all things data. Machine learning is the heart of AI. Machine learning, everybody is going, hiring, data scientists to do machine learning. I want to get a sense from our panel, who are experts in this area, what are the chief innovations and trends right now on machine learning. Not deep learning, the core of machine learning. What's super hot? What's in terms of new techniques, new technologies, new ways of organizing teams to build and to train machine learning models? I'd like to open it up. Let's just start with Lillian. What are your thoughts about trends in machine learning? What's really hot? >> It's funny that you excluded deep learning from the response for this, because I think the hottest space in machine learning is deep learning. And deep learning is machine learning. I see a lot of collaborative platforms coming out, where people, data scientists are able to work together with other sorts of data professionals to reduce redundancies in workflows. And create more efficient data science systems. >> Is there much uptake of these crowd sourcing environments for training machine learning wells. Like CrowdFlower, or Amazon Mechanical Turk, or Mighty AI? Is that a huge trend in terms of the workflow of data science or machine learning, a lot of that? >> I don't see that crowdsourcing is like, okay maybe I've been out of the crowdsourcing space for a while. But I was working with Standby Task Force back in 2013. And we were doing a lot of crowdsourcing. And I haven't seen the industry has been increasing, but I could be wrong. I mean, because there's no, if you're building automation models, most of the, a lot of the work that's being crowdsourced could actually be automated if someone took the time to just build the scripts and build the models. And so I don't imagine that, that's going to be a trend that's increasing. >> Well, automation machine learning pipeline is fairly hot, in terms of I'm seeing more and more research. Google's doing a fair amount of automated machine learning. The panel, what do you think about automation, in terms of the core modeling tasks involved in machine learning. Is that coming along? Are data scientists in danger of automating themselves out of a job? >> I don't think there's a risk of data scientist's being put out of a job. Let's just put that on the thing. I do think we need to get a bit clearer about this meme of the mythical unicorn. But to your call point about machine learning, I think what you'll see, we saw the cloud become baked into products, just as a given. I think machine learning is already crossed this threshold. We just haven't necessarily noticed or caught up. And if we look at, we're at an IBM event, so let's just do a call out for them. The data science experience platform, for example. Machine learning's built into a whole range of things around algorithm and data classification. And there's an assisted, guided model for how you get to certain steps, where you don't actually have to understand how machine learning works. You don't have to understand how the algorithms work. It shows you the different options you've got and you can choose them. So you might choose regression. And it'll give you different options on how to do that. So I think we've already crossed this threshold of baking in machine learning and baking in the data science tools. And we've seen that with Cloud and other technologies where, you know, the Office 365 is not, you can't get a non Cloud Office 365 account, right? I think that's already happened in machine learning. What we're seeing though, is organizations even as large as the Googles still in catch up mode, in my view, on some of the shift that's taken place. So we've seen them write little games and apps where people do doodles and then it runs through the ML library and says, "Well that's a cow, or a unicorn, or a duck." And you get awards, and gold coins, and whatnot. But you know, as far as 12 years ago I was working on a project, where we had full size airplanes acting as drones. And we mapped with two and 3-D imagery. With 2-D high res imagery and LiDAR for 3-D point Clouds. We were finding poles and wires for utility companies, using ML before it even became a trend. And baking it right into the tools. And used to store on our web page and clicked and pointed on. >> To counter Lillian's point, it's not crowdsourcing but crowd sharing that's really powering a lot of the rapid leaps forward. If you look at, you know, DSX from IBM. Or you look at Node-RED, huge number of free workflows that someone has probably already done the thing that you are trying to do. Go out and find in the libraries, through Jupyter and R Notebooks, there's an ability-- >> Chris can you define before you go-- >> Chris: Sure. >> This is great, crowdsourcing versus crowd sharing. What's the distinction? >> Well, so crowdsourcing, kind of, where in the context of the question you ask is like I'm looking for stuff that other people, getting people to do stuff that, for me. It's like asking people to mine classifieds. Whereas crowd sharing, someone has done the thing already, it already exists. You're not purpose built, saying, "Jim, help me build this thing." It's like, "Oh Jim, you already "built this thing, cool. "So can I fork it and make my own from it?" >> Okay, I see what you mean, keep going. >> And then, again, going back to earlier. In terms of the advancements. Really deep learning, it probably is a good idea to just sort of define these things. Machine learning is how machines do things without being explicitly programmed to do them. Deep learning's like if you can imagine a stack of pancakes, right? Each pancake is a type of machine learning algorithm. And your data is the syrup. You pour the data on it. It goes from layer, to layer, to layer, to layer, and what you end up with at the end is breakfast. That's the easiest analogy for what deep learning is. Now imagine a stack of pancakes, 500 or 1,000 high, that's where deep learning's going now. >> Sure, multi layered machine learning models, essentially, that have the ability to do higher levels of abstraction. Like image analysis, Lillian? >> I had a comment to add about automation and data science. Because there are a lot of tools that are able to, or applications that are able to use data science algorithms and output results. But the reason that data scientists aren't in risk of losing their jobs, is because just because you can get the result, you also have to be able to interpret it. Which means you have to understand it. And that involves deep math and statistical understanding. Plus domain expertise. So, okay, great, you took out the coding element but that doesn't mean you can codify a person's ability to understand and apply that insight. >> Dave: Joe, you have something to add? >> I could just add that I see the trend. Really, the reason we're talking about it today is machine learning is not necessarily, it's not new, like Dez was saying. But what's different is the accessibility of it now. It's just so easily accessible. All of the tools that are coming out, for data, have machine learning built into it. So the machine learning algorithms, which used to be a black art, you know, years ago, now is just very easily accessible. That you can get, it's part of everyone's toolbox. And the other reason that we're talking about it more, is that data science is starting to become a core curriculum in higher education. Which is something that's new, right? That didn't exist 10 years ago? But over the past five years, I'd say, you know, it's becoming more and more easily accessible for education. So now, people understand it. And now we have it accessible in our tool sets. So now we can apply it. And I think that's, those two things coming together is really making it becoming part of the standard of doing analytics. And I guess the last part is, once we can train the machines to start doing the analytics, right? And get smarter as it ingests more data. And then we can actually take that and embed it in our applications. That's the part that you still need data scientists to create that. But once we can have standalone appliances that are intelligent, that's when we're going to start seeing, really, machine learning and artificial intelligence really start to take off even more. >> Dave: So I'd like to switch gears a little bit and bring Ronald on. >> Okay, yes. >> Here you go, there. >> Ronald, the bromide in this sort of big data world we live in is, the data is the new oil. You got to be a data driven company and many other cliches. But when you talk to organizations and you start to peel the onion. You find that most companies really don't have a good way to connect data with business impact and business value. What are you seeing with your clients and just generally in the community, with how companies are doing that? How should they do that? I mean, is that something that is a viable approach? You don't see accountants, for example, quantifying the value of data on a balance sheet. There's no standards for doing that. And so it's sort of this fuzzy concept. How are and how should organizations take advantage of data and turn it into value. >> So, I think in general, if you look how companies look at data. They have departments and within the departments they have tools specific for this department. And what you see is that there's no central, let's say, data collection. There's no central management of governance. There's no central management of quality. There's no central management of security. Each department is manages their data on their own. So if you didn't ask, on one hand, "Okay, how should they do it?" It's basically go back to the drawing table and say, "Okay, how should we do it?" We should collect centrally, the data. And we should take care for central governance. We should take care for central data quality. We should take care for centrally managing this data. And look from a company perspective and not from a department perspective what the value of data is. So, look at the perspective from your whole company. And this means that it has to be brought on one end to, whether it's from C level, where most of them still fail to understand what it really means. And what the impact can be for that company. >> It's a hard problem. Because data by its' very nature is now so decentralized. But Chris you have a-- >> The thing I want to add to that is, think about in terms of valuing data. Look at what it would cost you for data breach. Like what is the expensive of having your data compromised. If you don't have governance. If you don't have policy in place. Look at the major breaches of the last couple years. And how many billions of dollars those companies lost in market value, and trust, and all that stuff. That's one way you can value data very easily. "What will it cost us if we mess this up?" >> So a lot of CEOs will hear that and say, "Okay, I get it. "I have to spend to protect myself, "but I'd like to make a little money off of this data thing. "How do I do that?" >> Well, I like to think of it, you know, I think data's definitely an asset within an organization. And is becoming more and more of an asset as the years go by. But data is still a raw material. And that's the way I think about it. In order to actually get the value, just like if you're creating any product, you start with raw materials and then you refine it. And then it becomes a product. For data, data is a raw material. You need to refine it. And then the insight is the product. And that's really where the value is. And the insight is absolutely, you can monetize your insight. >> So data is, abundant insights are scarce. >> Well, you know, actually you could say that intermediate between insights and the data are the models themselves. The statistical, predictive, machine learning models. That are a crystallization of insights that have been gained by people called data scientists. What are your thoughts on that? Are statistical, predictive, machine learning models something, an asset, that companies, organizations, should manage governance of on a centralized basis or not? >> Well the models are essentially the refinery system, right? So as you're refining your data, you need to have process around how you exactly do that. Just like refining anything else. It needs to be controlled and it needs to be governed. And I think that data is no different from that. And I think that it's very undisciplined right now, in the market or in the industry. And I think maturing that discipline around data science, I think is something that's going to be a very high focus in this year and next. >> You were mentioning, "How do you make money from data?" Because there's all this risk associated with security breaches. But at the risk of sounding simplistic, you can generate revenue from system optimization, or from developing products and services. Using data to develop products and services that better meet the demands and requirements of your markets. So that you can sell more. So either you are using data to earn more money. Or you're using data to optimize your system so you have less cost. And that's a simple answer for how you're going to be making money from the data. But yes, there is always the counter to that, which is the security risks. >> Well, and my question really relates to, you know, when you think of talking to C level executives, they kind of think about running the business, growing the business, and transforming the business. And a lot of times they can't fund these transformations. And so I would agree, there's many, many opportunities to monetize data, cut costs, increase revenue. But organizations seem to struggle to either make a business case. And actually implement that transformation. >> Dave, I'd love to have a crack at that. I think this conversation epitomizes the type of things that are happening in board rooms and C suites already. So we've really quickly dived into the detail of data. And the detail of machine learning. And the detail of data science, without actually stopping and taking a breath and saying, "Well, we've "got lots of it, but what have we got? "Where is it? "What's the value of it? "Is there any value in it at all?" And, "How much time and money should we invest in it?" For example, we talk of being about a resource. I look at data as a utility. When I turn the tap on to get a drink of water, it's there as a utility. I counted it being there but I don't always sample the quality of the water and I probably should. It could have Giardia in it, right? But what's interesting is I trust the water at home, in Sydney. Because we have a fairly good experience with good quality water. If I were to go to some other nation. I probably wouldn't trust that water. And I think, when you think about it, what's happening in organizations. It's almost the same as what we're seeing here today. We're having a lot of fun, diving into the detail. But what we've forgotten to do is ask the question, "Well why is data even important? "What's the reasoning to the business? "Why are we in business? "What are we doing as an organization? "And where does data fit into that?" As opposed to becoming so fixated on data because it's a media hyped topic. I think once you can wind that back a bit and say, "Well, we have lot's of data, "but is it good data? "Is it quality data? "Where's it coming from? "Is it ours? "Are we allowed to have it? "What treatment are we allowed to give that data?" As you said, "Are we controlling it? "And where are we controlling it? "Who owns it?" There's so many questions to be asked. But the first question I like to ask people in plain English is, "Well is there any value "in data in the first place? "What decisions are you making that data can help drive? "What things are in your organizations, "KPIs and milestones you're trying to meet "that data might be a support?" So then instead of becoming fixated with data as a thing in itself, it becomes part of your DNA. Does that make sense? >> Think about what money means. The Economists' Rhyme, "Money is a measure for, "a systems for, a medium, a measure, and exchange." So it's a medium of exchange. A measure of value, a way to exchange something. And a way to store value. Data, good clean data, well governed, fits all four of those. So if you're trying to figure out, "How do we make money out of stuff." Figure out how money works. And then figure out how you map data to it. >> So if we approach and we start with a company, we always start with business case, which is quite clear. And defined use case, basically, start with a team on one hand, marketing people, sales people, operational people, and also the whole data science team. So start with this case. It's like, defining, basically a movie. If you want to create the movie, You know where you're going to. You know what you want to achieve to create the customer experience. And this is basically the same with a business case. Where you define, "This is the case. "And this is how we're going to derive value, "start with it and deliver value within a month." And after the month, you check, "Okay, where are we and how can we move forward? "And what's the value that we've brought?" >> Now I as well, start with business case. I've done thousands of business cases in my life, with organizations. And unless that organization was kind of a data broker, the business case rarely has a discreet component around data. Is that changing, in your experience? >> Yes, so we guide companies into be data driven. So initially, indeed, they don't like to use the data. They don't like to use the analysis. So that's why, how we help. And is it changing? Yes, they understand that they need to change. But changing people is not always easy. So, you see, it's hard if you're not involved and you're not guiding it, they fall back in doing the daily tasks. So it's changing, but it's a hard change. >> Well and that's where this common parlance comes in. And Lillian, you, sort of, this is what you do for a living, is helping people understand these things, as you've been sort of evangelizing that common parlance. But do you have anything to add? >> I wanted to add that for organizational implementations, another key component to success is to start small. Start in one small line of business. And then when you've mastered that area and made it successful, then try and deploy it in more areas of the business. And as far as initializing big data implementation, that's generally how to do it successfully. >> There's the whole issue of putting a value on data as a discreet asset. Then there's the issue, how do you put a value on a data lake? Because a data lake, is essentially an asset you build on spec. It's an exploratory archive, essentially, of all kinds of data that might yield some insights, but you have to have a team of data scientists doing exploration and modeling. But it's all on spec. How do you put a value on a data lake? And at what point does the data lake itself become a burden? Because you got to store that data and manage it. At what point do you drain that lake? At what point, do the costs of maintaining that lake outweigh the opportunity costs of not holding onto it? >> So each Hadoop note is approximately $20,000 per year cost for storage. So I think that there needs to be a test and a diagnostic, before even inputting, ingesting the data and storing it. "Is this actually going to be useful? "What value do we plan to create from this?" Because really, you can't store all the data. And it's a lot cheaper to store data in Hadoop then it was in traditional systems but it's definitely not free. So people need to be applying this test before even ingesting the data. Why do we need this? What business value? >> I think the question we need to also ask around this is, "Why are we building data lakes "in the first place? "So what's the function it's going to perform for you?" There's been a huge drive to this idea. "We need a data lake. "We need to put it all somewhere." But invariably they become data swamps. And we only half jokingly say that because I've seen 90 day projects turn from a great idea, to a really bad nightmare. And as Lillian said, it is cheaper in some ways to put it into a HDFS platform, in a technical sense. But when we look at all the fully burdened components, it's actually more expensive to find Hadoop specialists and Spark specialists to maintain that cluster. And invariably I'm finding that big data, quote unquote, is not actually so much lots of data, it's complex data. And as Lillian said, "You don't always "need to store it all." So I think if we go back to the question of, "What's the function of a data lake in the first place? "Why are we building one?" And then start to build some fully burdened cost components around that. We'll quickly find that we don't actually need a data lake, per se. We just need an interim data store. So we might take last years' data and tokenize it, and analyze it, and do some analytics on it, and just keep the meta data. So I think there is this rush, for a whole range of reasons, particularly vendor driven. To build data lakes because we think they're a necessity, when in reality they may just be an interim requirement and we don't need to keep them for a long term. >> I'm going to attempt to, the last few questions, put them all together. And I think, they all belong together because one of the reasons why there's such hesitation about progress within the data world is because there's just so much accumulated tech debt already. Where there's a new idea. We go out and we build it. And six months, three years, it really depends on how big the idea is, millions of dollars is spent. And then by the time things are built the idea is pretty much obsolete, no one really cares anymore. And I think what's exciting now is that the speed to value is just so much faster than it's ever been before. And I think that, you know, what makes that possible is this concept of, I don't think of a data lake as a thing. I think of a data lake as an ecosystem. And that ecosystem has evolved so much more, probably in the last three years than it has in the past 30 years. And it's exciting times, because now once we have this ecosystem in place, if we have a new idea, we can actually do it in minutes not years. And that's really the exciting part. And I think, you know, data lake versus a data swamp, comes back to just traditional data architecture. And if you architect your data lake right, you're going to have something that's substantial, that's you're going to be able to harness and grow. If you don't do it right. If you just throw data. If you buy Hadoop cluster or a Cloud platform and just throw your data out there and say, "We have a lake now." yeah, you're going to create a mess. And I think taking the time to really understand, you know, the new paradigm of data architecture and modern data engineering, and actually doing it in a very disciplined way. If you think about it, what we're doing is we're building laboratories. And if you have a shabby, poorly built laboratory, the best scientist in the world isn't going to be able to prove his theories. So if you have a well built laboratory and a clean room, then, you know a scientist can get what he needs done very, very, very efficiently. And that's the goal, I think, of data management today. >> I'd like to just quickly add that I totally agree with the challenge between on premise and Cloud mode. And I think one of the strong themes of today is going to be the hybrid data management challenge. And I think organizations, some organizations, have rushed to adopt Cloud. And thinking it's a really good place to dump the data and someone else has to manage the problem. And then they've ended up with a very expensive death by 1,000 cuts in some senses. And then others have been very reluctant as a result of not gotten access to rapid moving and disruptive technology. So I think there's a really big challenge to get a basic conversation going around what's the value using Cloud technology as in adopting it, versus what are the risks? And when's the right time to move? For example, should we Cloud Burst for workloads? Do we move whole data sets in there? You know, moving half a petabyte of data into a Cloud platform back is a non-trivial exercise. But moving a terabyte isn't actually that big a deal anymore. So, you know, should we keep stuff behind the firewalls? I'd be interested in seeing this week where 80% of the data, supposedly is. And just push out for Cloud tools, machine learning, data science tools, whatever they might be, cognitive analytics, et cetera. And keep the bulk of the data on premise. Or should we just move whole spools into the Cloud? There is no one size fits all. There's no silver bullet. Every organization has it's own quirks and own nuances they need to think through and make a decision themselves. >> Very often, Dez, organizations have zonal architectures so you'll have a data lake that consists of a no sequel platform that might be used for say, mobile applications. A Hadoop platform that might be used for unstructured data refinement, so forth. A streaming platform, so forth and so on. And then you'll have machine learning models that are built and optimized for those different platforms. So, you know, think of it in terms of then, your data lake, is a set of zones that-- >> It gets even more complex just playing on that theme, when you think about what Cisco started, called Folk Computing. I don't really like that term. But edge analytics, or computing at the edge. We've seen with the internet coming along where we couldn't deliver everything with a central data center. So we started creating this concept of content delivery networks, right? I think the same thing, I know the same thing has happened in data analysis and data processing. Where we've been pulling social media out of the Cloud, per se, and bringing it back to a central source. And doing analytics on it. But when you think of something like, say for example, when the Dreamliner 787 from Boeing came out, this airplane created 1/2 a terabyte of data per flight. Now let's just do some quick, back of the envelope math. There's 87,400 fights a day, just in the domestic airspace in the USA alone, per day. Now 87,400 by 1/2 a terabyte, that's 43 point five petabytes a day. You physically can't copy that from quote unquote in the Cloud, if you'll pardon the pun, back to the data center. So now we've got the challenge, a lot of our Enterprise data's behind a firewall, supposedly 80% of it. But what's out at the edge of the network. Where's the value in that data? So there are zonal challenges. Now what do I do with my Enterprise versus the open data, the mobile data, the machine data. >> Yeah, we've seen some recent data from IDC that says, "About 43% of the data "is going to stay at the edge." We think that, that's way understated, just given the examples. We think it's closer to 90% is going to stay at the edge. >> Just on the airplane topic, right? So Airbus wasn't going to be outdone. Boeing put 4,000 sensors or something in their 787 Dreamliner six years ago. Airbus just announced an 83, 81,000 with 10,000 sensors in it. Do the same math. Now the FAA in the US said that all aircraft and all carriers have to be, by early next year, I think it's like March or April next year, have to be at the same level of BIOS. Or the same capability of data collection and so forth. It's kind of like a mini GDPR for airlines. So with the 83, 81,000 with 10,000 sensors, that becomes two point five terabytes per flight. If you do the math, it's 220 petabytes of data just in one day's traffic, domestically in the US. Now, it's just so mind boggling that we're going to have to completely turn our thinking on its' head, on what do we do behind the firewall? What do we do in the Cloud versus what we might have to do in the airplane? I mean, think about edge analytics in the airplane processing data, as you said, Jim, streaming analytics in flight. >> Yeah that's a big topic within Wikibon, so, within the team. Me and David Floyer, and my other colleagues. They're talking about the whole notion of edge architecture. Not only will most of the data be persisted at the edge, most of the deep learning models like TensorFlow will be executed at the edge. To some degree, the training of those models will happen in the Cloud. But much of that will be pushed in a federated fashion to the edge, or at least I'm predicting. We're already seeing some industry moves in that direction, in terms of architectures. Google has a federated training, project or initiative. >> Chris: Look at TensorFlow Lite. >> Which is really fascinating for it's geared to IOT, I'm sorry, go ahead. >> Look at TensorFlow Lite. I mean in the announcement of having every Android device having ML capabilities, is Google's essential acknowledgment, "We can't do it all." So we need to essentially, sort of like a setting at home. Everyone's smartphone top TV box just to help with the processing. >> Now we're talking about this, this sort of leads to this IOT discussion but I want to underscore the operating model. As you were saying, "You can't just "lift and shift to the Cloud." You're not going to, CEOs aren't going to get the billion dollar hit by just doing that. So you got to change the operating model. And that leads to, this discussion of IOT. And an entirely new operating model. >> Well, there are companies that are like Sisense who have worked with Intel. And they've taken this concept. They've taken the business logic and not just putting it in the chip, but actually putting it in memory, in the chip. So as data's going through the chip it's not just actually being processed but it's actually being baked in memory. So level one, two, and three cache. Now this is a game changer. Because as Chris was saying, even if we were to get the data back to a central location, the compute load, I saw a real interesting thing from I think it was Google the other day, one of the guys was doing a talk. And he spoke about what it meant to add cognitive and voice processing into just the Android platform. And they used some number, like that had, double the amount of compute they had, just to add voice for free, to the Android platform. Now even for Google, that's a nontrivial exercise. So as Chris was saying, I think we have to again, flip it on its' head and say, "How much can we put "at the edge of the network?" Because think about these phones. I mean, even your fridge and microwave, right? We put a man on the moon with something that these days, we make for $89 at home, on the Raspberry Pie computer, right? And even that was 1,000 times more powerful. When we start looking at what's going into the chips, we've seen people build new, not even GPUs, but deep learning and stream analytics capable chips. Like Google, for example. That's going to make its' way into consumer products. So that, now the compute capacity in phones, is going to, I think transmogrify in some ways because there is some magic in there. To the point where, as Chris was saying, "We're going to have the smarts in our phone." And a lot of that workload is going to move closer to us. And only the metadata that we need to move is going to go centrally. >> Well here's the thing. The edge isn't the technology. The edge is actually the people. When you look at, for example, the MIT language Scratch. This is kids programming language. It's drag and drop. You know, kids can assemble really fun animations and make little movies. We're training them to build for IOT. Because if you look at a system like Node-RED, it's an IBM interface that is drag and drop. Your workflow is for IOT. And you can push that to a device. Scratch has a converter for doing those. So the edge is what those thousands and millions of kids who are learning how to code, learning how to think architecturally and algorithmically. What they're going to create that is beyond what any of us can possibly imagine. >> I'd like to add one other thing, as well. I think there's a topic we've got to start tabling. And that is what I refer to as the gravity of data. So when you think about how planets are formed, right? Particles of dust accrete. They form into planets. Planets develop gravity. And the reason we're not flying into space right now is that there's gravitational force. Even though it's one of the weakest forces, it keeps us on our feet. Oftentimes in organizations, I ask them to start thinking about, "Where is the center "of your universe with regard to the gravity of data." Because if you can follow the center of your universe and the gravity of your data, you can often, as Chris is saying, find where the business logic needs to be. And it could be that you got to think about a storage problem. You can think about a compute problem. You can think about a streaming analytics problem. But if you can find where the center of your universe and the center of your gravity for your data is, often you can get a really good insight into where you can start focusing on where the workloads are going to be where the smarts are going to be. Whether it's small, medium, or large. >> So this brings up the topic of data governance. One of the themes here at Fast Track Your Data is GDPR. What it means. It's one of the reasons, I think IBM selected Europe, generally, Munich specifically. So let's talk about GDPR. We had a really interesting discussion last night. So let's kind of recreate some of that. I'd like somebody in the panel to start with, what is GDPR? And why does it matter, Ronald? >> Yeah, maybe I can start. Maybe a little bit more in general unified governance. So if i talk to companies and I need to explain to them what's governance, I basically compare it with a crime scene. So in a crime scene if something happens, they start with securing all the evidence. So they start sealing the environment. And take care that all the evidence is collected. And on the other hand, you see that they need to protect this evidence. There are all kinds of policies. There are all kinds of procedures. There are all kinds of rules, that need to be followed. To take care that the whole evidence is secured well. And once you start, basically, investigating. So you have the crime scene investigators. You have the research lab. You have all different kind of people. They need to have consent before they can use all this evidence. And the whole reason why they're doing this is in order to collect the villain, the crook. To catch him and on the other hand, once he's there, to convict him. And we do this to have trust in the materials. Or trust in basically, the analytics. And on the other hand to, the public have trust in everything what's happened with the data. So if you look to a company, where data is basically the evidence, this is the value of your data. It's similar to like the evidence within a crime scene. But most companies don't treat it like this. So if we then look to GDPR, GDPR basically shifts the power and the ownership of the data from the company to the person that created it. Which is often, let's say the consumer. And there's a lot of paradox in this. Because all the companies say, "We need to have this customer data. "Because we need to improve the customer experience." So if you make it concrete and let's say it's 1st of June, so GDPR is active. And it's first of June 2018. And I go to iTunes, so I use iTunes. Let's go to iTunes said, "Okay, Apple please "give me access to my data." I want to see which kind of personal information you have stored for me. On the other end, I want to have the right to rectify all this data. I want to be able to change it and give them a different level of how they can use my data. So I ask this to iTunes. And then I say to them, okay, "I basically don't like you anymore. "I want to go to Spotify. "So please transfer all my personal data to Spotify." So that's possible once it's June 18. Then I go back to iTunes and say, "Okay, I don't like it anymore. "Please reduce my consent. "I withdraw my consent. "And I want you to remove all my "personal data for everything that you use." And I go to Spotify and I give them, let's say, consent for using my data. So this is a shift where you can, as a person be the owner of the data. And this has a lot of consequences, of course, for organizations, how to manage this. So it's quite simple for the consumer. They get the power, it's maturing the whole law system. But it's a big consequence of course for organizations. >> This is going to be a nightmare for marketers. But fill in some of the gaps there. >> Let's go back, so GDPR, the General Data Protection Regulation, was passed by the EU in 2016, in May of 2016. It is, as Ronald was saying, it's four basic things. The right to privacy. The right to be forgotten. Privacy built into systems by default. And the right to data transfer. >> Joe: It takes effect next year. >> It is already in effect. GDPR took effect in May of 2016. The enforcement penalties take place the 25th of May 2018. Now here's where, there's two things on the penalty side that are important for everyone to know. Now number one, GDPR is extra territorial. Which means that an EU citizen, anywhere on the planet has GDPR, goes with them. So say you're a pizza shop in Nebraska. And an EU citizen walks in, orders a pizza. Gives her the credit card and stuff like that. If you for some reason, store that data, GDPR now applies to you, Mr. Pizza shop, whether or not you do business in the EU. Because an EU citizen's data is with you. Two, the penalties are much stiffer then they ever have been. In the old days companies could simply write off penalties as saying, "That's the cost of doing business." With GDPR the penalties are up to 4% of your annual revenue or 20 million Euros, whichever is greater. And there may be criminal sanctions, charges, against key company executives. So there's a lot of questions about how this is going to be implemented. But one of the first impacts you'll see from a marketing perspective is all the advertising we do, targeting people by their age, by their personally identifiable information, by their demographics. Between now and May 25th 2018, a good chunk of that may have to go away because there's no way for you to say, "Well this person's an EU citizen, this person's not." People give false information all the time online. So how do you differentiate it? Every company, regardless of whether they're in the EU or not will have to adapt to it, or deal with the penalties. >> So Lillian, as a consumer this is designed to protect you. But you had a very negative perception of this regulation. >> I've looked over the GDPR and to me it actually looks like a socialist agenda. It looks like (panel laughs) no, it looks like a full assault on free enterprise and capitalism. And on its' face from a legal perspective, its' completely and wholly unenforceable. Because they're assigning jurisdictional rights to the citizen. But what are they going to do? They're going to go to Nebraska and they're going to call in the guy from the pizza shop? And call him into what court? The EU court? It's unenforceable from a legal perspective. And if you write a law that's unenforceable, you know, it's got to be enforceable in every element. It can't be just, "Oh, we're only "going to enforce it for Facebook and for Google. "But it's not enforceable for," it needs to be written so that it's a complete and actionable law. And it's not written in that way. And from a technological perspective it's not implementable. I think you said something like 652 EU regulators or political people voted for this and 10 voted against it. But what do they know about actually implementing it? Is it possible? There's all sorts of regulations out there that aren't possible to implement. I come from an environmental engineering background. And it's absolutely ridiculous because these agencies will pass laws that actually, it's not possible to implement those in practice. The cost would be too great. And it's not even needed. So I don't know, I just saw this and I thought, "You know, if the EU wants to," what they're essentially trying to do is regulate what the rest of the world does on the internet. And if they want to build their own internet like China has and police it the way that they want to. But Ronald here, made an analogy between data, and free enterprise, and a crime scene. Now to me, that's absolutely ridiculous. What does data and someone signing up for an email list have to do with a crime scene? And if EU wants to make it that way they can police their own internet. But they can't go across the world. They can't go to Singapore and tell Singapore, or go to the pizza shop in Nebraska and tell them how to run their business. >> You know, EU overreach in the post Brexit era, of what you're saying has a lot of validity. How far can the tentacles of the EU reach into other sovereign nations. >> What court are they going to call them into? >> Yeah. >> I'd like to weigh in on this. There are lots of unknowns, right? So I'd like us to focus on the things we do know. We've already dealt with similar situations before. In Australia, we introduced a goods and sales tax. Completely foreign concept. Everything you bought had 10% on it. No one knew how to deal with this. It was a completely new practice in accounting. There's a whole bunch of new software that had to be written. MYRB had to have new capability, but we coped. No one actually went to jail yet. It's decades later, for not complying with GST. So what it was, was a framework on how to shift from non sales tax related revenue collection. To sales tax related revenue collection. I agree that there are some egregious things built into this. I don't disagree with that at all. But I think if I put my slightly broader view of the world hat on, we have well and truly gone past the point in my mind, where data was respected, data was treated in a sensible way. I mean I get emails from companies I've never done business with. And when I follow it up, it's because I did business with a credit card company, that gave it to a service provider, that thought that I was going to, when I bought a holiday to come to Europe, that I might want travel insurance. Now some might say there's value in that. And other's say there's not, there's the debate. But let's just focus on what we're talking about. We're talking about a framework for governance of the treatment of data. If we remove all the emotive component, what we are talking about is a series of guidelines, backed by laws, that say, "We would like you to do this," in an ideal world. But I don't think anyone's going to go to jail, on day one. They may go to jail on day 180. If they continue to do nothing about it. So they're asking you to sort of sit up and pay attention. Do something about it. There's a whole bunch of relief around how you approach it. The big thing for me, is there's no get out of jail card, right? There is no get out of jail card for not complying. But there's plenty of support. I mean, we're going to have ambulance chasers everywhere. We're going to have class actions. We're going to have individual suits. The greatest thing to do right now is get into GDPR law. Because you seem to think data scientists are unicorn? >> What kind of life is that if there's ambulance chasers everywhere? You want to live like that? >> Well I think we've seen ad blocking. I use ad blocking as an example, right? A lot of organizations with advertising broke the internet by just throwing too much content on pages, to the point where they're just unusable. And so we had this response with ad blocking. I think in many ways, GDPR is a regional response to a situation where I don't think it's the exact right answer. But it's the next evolutional step. We'll see things evolve over time. >> It's funny you mentioned it because in the United States one of the things that has happened, is that with the change in political administrations, the regulations on what companies can do with your data have actually been laxened, to the point where, for example, your internet service provider can resell your browsing history, with or without your consent. Or your consent's probably buried in there, on page 47. And so, GDPR is kind of a response to saying, "You know what? "You guys over there across the Atlantic "are kind of doing some fairly "irresponsible things with what you allow companies to do." Now, to Lillian's point, no one's probably going to go after the pizza shop in Nebraska because they don't do business in the EU. They don't have an EU presence. And it's unlikely that an EU regulator's going to get on a plane from Brussels and fly to Topeka and say, or Omaha, sorry, "Come on Joe, let's get the pizza shop in order here." But for companies, particularly Cloud companies, that have offices and operations within the EU, they have to sit up and pay attention. So if you have any kind of EU operations, or any kind of fiscal presence in the EU, you need to get on board. >> But to Lillian's point it becomes a boondoggle for lawyers in the EU who want to go after deep pocketed companies like Facebook and Google. >> What's the value in that? It seems like regulators are just trying to create work for themselves. >> What about the things that say advertisers can do, not so much with the data that they have? With the data that they don't have. In other words, they have people called data scientists who build models that can do inferences on sparse data. And do amazing things in terms of personalization. What do you do about all those gray areas? Where you got machine learning models and so forth? >> But it applies-- >> It applies to personally identifiable information. But if you have a talented enough data scientist, you don't need the PII or even the inferred characteristics. If a certain type of behavior happens on your website, for example. And this path of 17 pages almost always leads to a conversion, it doesn't matter who you are or where you're coming from. If you're a good enough data scientist, you can build a model that will track that. >> Like you know, target, infer some young woman was pregnant. And they inferred correctly even though that was never divulged. I mean, there's all those gray areas that, how can you stop that slippery slope? >> Well I'm going to weigh in really quickly. A really interesting experiment for people to do. When people get very emotional about it I say to them, "Go to Google.com, "view source, put it in seven point Courier "font in Word and count how many pages it is." I guess you can't guess how many pages? It's 52 pages of seven point Courier font, HTML to render one logo, and a search field, and a click button. Now why do we need 52 pages of HTML source code and Java script just to take a search query. Think about what's being done in that. It's effectively a mini operating system, to figure out who you are, and what you're doing, and where you been. Now is that a good or bad thing? I don't know, I'm not going to make a judgment call. But what I'm saying is we need to stop and take a deep breath and say, "Does anybody need a 52 page, "home page to take a search query?" Because that's just the tip of the iceberg. >> To that point, I like the results that Google gives me. That's why I use Google and not Bing. Because I get better search results. So, yeah, I don't mind if you mine my personal data and give me, our Facebook ads, those are the only ads, I saw in your article that GDPR is going to take out targeted advertising. The only ads in the entire world, that I like are Facebook ads. Because I actually see products I'm interested in. And I'm happy to learn about that. I think, "Oh I want to research that. "I want to see this new line of products "and what are their competitors?" And I like the targeted advertising. I like the targeted search results because it's giving me more of the information that I'm actually interested in. >> And that's exactly what it's about. You can still decide, yourself, if you want to have this targeted advertising. If not, then you don't give consent. If you like it, you give consent. So if a company gives you value, you give consent back. So it's not that it's restricting everything. It's giving consent. And I think it's similar to what happened and the same type of response, what happened, we had the Mad Cow Disease here in Europe, where you had the whole food chain that needed to be tracked. And everybody said, "No, it's not required." But now it's implemented. Everybody in Europe does it. So it's the same, what probably going to happen over here as well. >> So what does GDPR mean for data scientists? >> I think GDPR is, I think it is needed. I think one of the things that may be slowing data science down is fear. People are afraid to share their data. Because they don't know what's going to be done with it. If there are some guidelines around it that should be enforced and I think, you know, I think it's been said but as long as a company could prove that it's doing due diligence to protect your data, I think no one is going to go to jail. I think when there's, you know, we reference a crime scene, if there's a heinous crime being committed, all right, then it's going to become obvious. And then you do go directly to jail. But I think having guidelines and even laws around privacy and protection of data is not necessarily a bad thing. You can do a lot of data, really meaningful data science, without understanding that it's Joe Caserta. All of the demographics about me. All of the characteristics about me as a human being, I think are still on the table. All that they're saying is that you can't go after Joe, himself, directly. And I think that's okay. You know, there's still a lot of things. We could still cure diseases without knowing that I'm Joe Caserta, right? As long as you know everything else about me. And I think that's really at the core, that's what we're trying to do. We're trying to protect the individual and the individual's data about themselves. But I think as far as how it affects data science, you know, a lot of our clients, they're afraid to implement things because they don't exactly understand what the guideline is. And they don't want to go to jail. So they wind up doing nothing. So now that we have something in writing that, at least, it's something that we can work towards, I think is a good thing. >> In many ways, organizations are suffering from the deer in the headlight problem. They don't understand it. And so they just end up frozen in the headlights. But I just want to go back one step if I could. We could get really excited about what it is and is not. But for me, the most critical thing there is to remember though, data breaches are happening. There are over 1,400 data breaches, on average, per day. And most of them are not trivial. And when we saw 1/2 a billion from Yahoo. And then one point one billion and then one point five billion. I mean, think about what that actually means. There were 47,500 Mongodbs breached in an 18 hour window, after an automated upgrade. And they were airlines, they were banks, they were police stations. They were hospitals. So when I think about frameworks like GDPR, I'm less worried about whether I'm going to see ads and be sold stuff. I'm more worried about, and I'll give you one example. My 12 year old son has an account at a platform called Edmodo. Now I'm not going to pick on that brand for any reason but it's a current issue. Something like, I think it was like 19 million children in the world had their username, password, email address, home address, and all this social interaction on this Facebook for kids platform called Edmodo, breached in one night. Now I got my hands on a copy. And everything about my son is there. Now I have a major issue with that. Because I can't do anything to undo that, nothing. The fact that I was able to get a copy, within hours on a dark website, for free. The fact that his first name, last name, email, mobile phone number, all these personal messages from friends. Nobody has the right to allow that to breach on my son. Or your children, or our children. For me, GDPR, is a framework for us to try and behave better about really big issues. Whether it's a socialist issue. Whether someone's got an issue with advertising. I'm actually not interested in that at all. What I'm interested in is companies need to behave much better about the treatment of data when it's the type of data that's being breached. And I get really emotional when it's my son, or someone else's child. Because I don't care if my bank account gets hacked. Because they hedge that. They underwrite and insure themselves and the money arrives back to my bank. But when it's my wife who donated blood and a blood donor website got breached and her details got lost. Even things like sexual preferences. That they ask questions on, is out there. My 12 year old son is out there. Nobody has the right to allow that to happen. For me, GDPR is the framework for us to focus on that. >> Dave: Lillian, is there a comment you have? >> Yeah, I think that, I think that security concerns are 100% and definitely a serious issue. Security needs to be addressed. And I think a lot of the stuff that's happening is due to, I think we need better security personnel. I think we need better people working in the security area where they're actually looking and securing. Because I don't think you can regulate I was just, I wanted to take the microphone back when you were talking about taking someone to jail. Okay, I have a background in law. And if you look at this, you guys are calling it a framework. But it's not a framework. What they're trying to do is take 4% of your business revenues per infraction. They want to say, "If a person signs up "on your email list and you didn't "like, necessarily give whatever "disclaimer that the EU said you need to give. "Per infraction, we're going to take "4% of your business revenue." That's a law, that they're trying to put into place. And you guys are talking about taking people to jail. What jail are you? EU is not a country. What jurisdiction do they have? Like, you're going to take pizza man Joe and put him in the EU jail? Is there an EU jail? Are you going to take them to a UN jail? I mean, it's just on its' face it doesn't hold up to legal tests. I don't understand how they could enforce this. >> I'd like to just answer the question on-- >> Security is a serious issue. I would be extremely upset if I were you. >> I personally know, people who work for companies who've had data breaches. And I respect them all. They're really smart people. They've got 25 plus years in security. And they are shocked that they've allowed a breach to take place. What they've invariably all agreed on is that a whole range of drivers have caused them to get to a bad practice. So then, for example, the donate blood website. The young person who was assist admin with all the right skills and all the right experience just made a basic mistake. They took a db dump of a mysql database before they upgraded their Wordpress website for the business. And they happened to leave it in a folder that was indexable by Google. And so somebody wrote a radio expression to search in Google to find sql backups. Now this person, I personally respect them. I think they're an amazing practitioner. They just made a mistake. So what does that bring us back to? It brings us back to the point that we need a safety net or a framework or whatever you want to call it. Where organizations have checks and balances no matter what they do. Whether it's an upgrade, a backup, a modification, you know. And they all think they do, but invariably we've seen from the hundreds of thousands of breaches, they don't. Now on the point of law, we could debate that all day. I mean the EU does have a remit. If I was caught speeding in Germany, as an Australian, I would be thrown into a German jail. If I got caught as an organization in France, breaching GDPR, I would be held accountable to the law in that region, by the organization pursuing me. So I think it's a bit of a misnomer saying I can't go to an EU jail. I don't disagree with you, totally, but I think it's regional. If I get a speeding fine and break the law of driving fast in EU, it's in the country, in the region, that I'm caught. And I think GDPR's going to be enforced in that same approach. >> All right folks, unfortunately the 60 minutes flew right by. And it does when you have great guests like yourselves. So thank you very much for joining this panel today. And we have an action packed day here. So we're going to cut over. The CUBE is going to have its' interview format starting in about 1/2 hour. And then we cut over to the main tent. Who's on the main tent? Dez, you're doing a main stage presentation today. Data Science is a Team Sport. Hillary Mason, has a breakout session. We also have a breakout session on GDPR and what it means for you. Are you ready for GDPR? Check out ibmgo.com. It's all free content, it's all open. You do have to sign in to see the Hillary Mason and the GDPR sessions. And we'll be back in about 1/2 hour with the CUBE. We'll be running replays all day on SiliconAngle.tv and also ibmgo.com. So thanks for watching everybody. Keep it right there, we'll be back in about 1/2 hour with the CUBE interviews. We're live from Munich, Germany, at Fast Track Your Data. This is Dave Vellante with Jim Kobielus, we'll see you shortly. (electronic music)