>> 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, I'm your host Rebecca Knight. We have three guests for this segment. Doctor Justin Marley, he is a consultant psychiatrist for the NHS in the U.K., Chris Ashley, Technology Delivery Lead Associate Manager, and Gayle Sirard, Applied Intelligence Lead North America for Accenture, thank you so much the three of you for coming on the show. >> Thanks. >> Well, thanks for having us. >> Very much, yeah. >> I'm really excited about the topic that we're going to have today. which is a home healthcare system for the elderly. I'm going to start with you, Doctor Marley. In terms, can you just provide the context of what, of sort what this problem is with an elderly person living at home, experiencing loneliness, experiencing isolation, can you paint the portrait for our viewers? >> Yes certainly, so the older adult population is heterogeneous, but what happens is as people get older they retire from work, they lose part of their purpose in life, sadly they lose loved ones in their lives, and can often find themselves at home by themselves. As people get older, they start to develop cognitive impairment, it may not be at the level of dementia or it may be at the level of dementia. And so, they become increasingly isolated. And there's something called a digital divide, which is basically, we're living in a connected world which is permeated with digital technology to help connect people. The older adult population haven't grown up with this technology, so they're a little bit disconnected from all of this, which just adds to everything else. >> So what is the idea here, so he just described older people who are lonely, who are experiencing forgetfulness, they've lost a lot of their friends and social connections. So what's the answer, what's the solution here? >> Our mission is to help people feel socially connected in this, it's always changing, this digital world and stay independent in their own home for a bit longer. So, what we've done is we've made an Alexa Skill and web portal. So, an Alexa Skill is just like an app on your iPhone. And it's really about day to day help. Everyday, little things, so medicine reminders or finding things that are happening in their area. And just a bit more independence, a bit more joy day to day. >> So Gayle, so what did this look in practice? So we have this Alexa who is in a person's home and reminding the person to take his or her pills and just providing a little connection. So, just describe to me what it looks like. >> Yeah, so I mean, well if you kind of take a step back a minute and you think about this, as Doctor described right, the impact of an individual sitting at home alone and you think about their daily lives and what they're doing, this solution starts by really thinking about what are they doing on a daily basis and what's going to motivate them to get up and get engaged? So, fundamentally this solution thinks about the process that they do and it's constantly learning the behaviors that they're doing on a daily basis and as you know there's constant reminders. You start to get forgetful, so there's activities that you have to do and if you don't do, you become stagnant, lonely. And so, getting a system that allows them to learn their behaviors, understand what those behaviors are, and what's going to motivate them to wake up, get excited everyday, feel engaged. Fundamentally, this system is about learning that and nudging them to get engaged and move forward. The thing I really love about this is, if you think about sort of what we're doing here, fundamentally we're taking really sophisticated technologies, artificial intelligence, and voice recognition and applying it to an everyday process. Which is like so exciting to see the application of the elderly adopting a solution like this. And being able to reach out and engage through this new technology channel. >> And it is, as you said, learning the behaviors, it's learning their proclivities, but it's also providing a bit of social interaction for these people who are incredibly lonely. What have you seen, Doctor Marley? >> So, what we've seen is from the Hanover Project, which the Liquid Studio team in London have been working on. Which is some of the work in sheltered accommodation with older adults and Chris can talk a little bit about that, we've also been trialing it in a young onset dementia group, as well. So, we're looking at people with dementia across the lifespan, both young and old. And we've had some very promising feedback from the younger group, as well. >> So yes, how are you using this for people who are experiencing the onset of dementia? >> So, the idea is that, well first of all what we've had to do is to start a trial, a pilot study. So, we're currently going through the ethics committee process, which is very important for vulnerable adults, we can't just trial a new technology in this group, but we've had a design session with the Liquid Studio team, they've come in and they've showcased the technology within the group. And this is a group of people with young onset dementia and their carers, and we've had some very interesting feedback about how they can communicate with it and how easy it is to understand, and some of the features that we've been developing, such as the information about the condition that they can access from the home care solution. >> Chris, I wanted to ask you about the role of empathy in technology, so here's artificial intelligence which is it's not human intelligence. >> Absolutely. >> It's not our human social intelligence, but yet empathy is so important. Particularly in a technology like this. Can you, can you talk about how you approach this question? >> So, I take your point completely. It is AI, it's not a real human. And ultimately, we would love it if every single old person had human contact everyday, but it's just not the case. And I have sat with people, one of whom is my nan, and genuinely she was just like, it's really nice to have a voice in the house, my grandad's been dead for 15 years, she's been alone for 15 years in that same house and she loves it, when Alexa sang her Happy Birthday because it was configured with the birth, I put her birthday in and she was just like, I love it. She says goodnight to it's like, oh sleep well, I do call her regularly, but knowing that she has that is amazing. >> So it's giving peace of mind to the loved ones, too. >> Absolutely, yeah. >> Exactly, so talk a little bit more about what, so we've already seen great success, adoption is on the rise, what are some of things that you hope to add to this application going forward? >> So, we see it at the moment as a companion. So, it might be for people with dementia. It might just be for people who are alone and they feel a bit socially isolated. So, the Alexa platform is very powerful. It offers a very simple way to do things, like radio, if you want to mess around with an iPad, unlock it, make sure it's charged, find your contacts, call it, it's much easier to say, Alexa call my daughter. So, that's the things that we have and that's a great service, what we can add very quickly is IoT sensors, so if you put a sensor on the fridge, on the bathroom door, in the bed, you work out whether people are sleeping enough, whether they're eating enough food, whether they're drinking enough. And you're augmenting that role of the caregiver that comes along and sees, hang on, this person hasn't been sleeping very well at all for the last few days. One of the things we get people to do when they interact with home care, is just say, how are you feeling today? And they say, good or bad, and over time you build up a picture of that person's mental health. >> Because Alexa has built a rapport with her roommate. >> Yeah, absolutely. >> So, what you're describing it sounds like a very user-friendly, a straightforward interface that is perfect for people on the other side of the digital divide, as you just described. So, how do you work with technologists, Doctor Marley, in terms of helping the technologists understand where these customers are, I mean so many, I had a member of my family who had Alzheimer's and the idea was meet the person where they are. So, if they come to you and tell you that it's Christmas Day and you say, it is, Merry Christmas, but how do you help the technologists sort of get that? I mean it comes back to the empathy. >> So, in terms of where the person is, there are many different barriers to the use of technology, the sensor impairment, there's for example if someone has a moderate to severe level of dementia, then it would be very difficult for them to interact with the device. So, we have to kind of work with carers to work with them. So, there's all sorts of kind of complications about taking this out into the real world. And what we're also looking to do is develop a service with the AI at the front end, backed up by healthcare professionals at the back end. So, that we can quickly escalate if there's problems because the last thing that you want is someone to run into problems, for the technology to be able to detect that there's something is amiss, and not being able to do anything about it. So, I think combining the AI with all of the warning signs flagged up by the algorithms with healthcare professionals in the background ready to escalate in-house services is the best of both worlds. And gets the right services to the person at the right time, and I think that's only possible through this combination. >> Yeah, it's an extraordinary story. >> It is. >> It's really been a great conversation. Thank you all so much for coming on theCube and sharing it with our viewers. >> Thank you. >> Thank you. >> It's a pleasure, thank you very much. >> I'm Rebecca Knight, that wraps up our coverage of today's AWS Executive Summit. Join us tomorrow for more from AWS re:Invent. (quirky upbeat music)

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe, everybody, We're back. We're talking about enterprise data automation. The hashtag is data automated, and we're going to really dig into data migrations, data, migrations. They're risky. They're time consuming, and they're expensive. Yousef con is here. He's the head of partnerships and alliances at I o ta ho coming again from London. Hey, good to see you, Seth. Thanks very much. >>Thank you. >>So your role is is interesting. We're talking about data migrations. You're gonna head of partnerships. What is your role specifically? And how is it relevant to what we're gonna talk about today? >>Uh, I work with the various businesses such as cloud companies, systems integrators, companies that sell operating systems, middleware, all of whom are often quite well embedded within a company. I t infrastructures and have existing relationships. Because what we do fundamentally makes migrating to the cloud easier on data migration easier. A lot of businesses that are interested in partnering with us. Um, we're interested in parting with, So >>let's set up the problem a little bit. And then I want to get into some of the data. You know, I said that migration is a risky, time consuming, expensive. They're they're often times a blocker for organizations to really get value out of data. Why is that? >>Uh, I think I mean, all migrations have to start with knowing the facts about your data, and you can try and do this manually. But when that you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate so that I have everything from on premise mainframes. They may have stuff which is probably in the cloud, but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. Um, now they're understanding of what they have. Ai's often quite limited because you can try and draw a manual maps, but they're outdated very quickly. Every time that data changes the manual that's out of date on people obviously leave organizations over time, so that kind of tribal knowledge gets built up is limited as well. So you can try a Mackel that manually you might need a db. Hey, thanks. Based analyst or ah, business analyst, and they won't go in and explore the data for you. But doing that manually is very, very time consuming this contract teams of people, months and months. Or you can use automation just like what's the bank with Iot? And they managed to do this with a relatively small team. Are in a timeframe of days. >>Yeah, we talked to Paul from Webster Bank. Awesome discussion. So I want to dig into this migration and let's let's pull up graphic it will talk about. We'll talk about what a typical migration project looks like. So what you see here it is. It's very detailed. I know it's a bit of an eye test, but let me call your attention to some of the key aspects of this Ah, and then use. If I want you to chime in. So at the top here, you see that area graph that's operational risk for a typical migration project, and you can see the timeline and the the milestones. That blue bar is the time to test so you can see the second step data analysis talking 24 weeks so, you know, very time consuming. And then Let's not get dig into the stuff in the middle of the fine print, but there's some real good detail there, but go down the bottom. That's labor intensity in the in the bottom and you can see high is that sort of brown and and you could see a number of data analysis, data staging data prep, the trial, the implementation post implementation fixtures, the transition toe B A B a year, which I think is business as usual. Those are all very labor intensive. So what do you take aways from this typical migration project? What do we need to know yourself? >>I mean, I think the key thing is, when you don't understand your data upfront, it's very difficult to scope to set up a project because you go to business stakeholders and decision makers and you say Okay, we want to migrate these data stores. We want to put them in the cloud most often, but actually, you probably don't know how much data is there. You don't necessarily know how many applications that relates to, you know, the relationships between the data. You don't know the flow of the data. So the direction in which the data is going between different data stores and tables, so you start from a position where you have pretty high risk and alleviate that risk. You could be stacking project team of lots and lots of people to do the next base, which is analysis. And so you set up a project which has got a pretty high cost. The big projects, more people, the heavy of governance, obviously on then there, then in the phase where they're trying to do lots and lots of manual analysis manage. That, in a sense, is, as we all know, on the idea of trying to relate data that's in different those stores relating individual tables and columns. Very, very time consuming, expensive. If you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use party tools, Aziz said earlier. The people who understand some of those systems may have left a while ago. See you even high risks quite cost situation from the off on the same things that have developed through the project. Um, what are you doing with it, Ayatollah? Who is that? We're able to automate a lot of this process from the very beginning because we can do the initial data. Discovery run, for example, automatically you very quickly have an automated validator. A data map on the data flow has been generated automatically, much less time and effort and much less cars. Doctor Marley. >>Okay, so I want to bring back that that first chart, and I want to call your attention to the again that area graph the blue bars and then down below that labor intensity. And now let's bring up the the the same chart. But with a set of an automation injection in here and now. So you now see the So let's go Said Accelerated by Iot, Tom. Okay, great. And we're going to talk about this. But look, what happens to the operational risk. A dramatic reduction in that. That graph. And then look at the bars, the bars, those blue bars. You know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. The it was all these were high data analysis data staging data prep. Try a lot post implementation fixtures in transition to be a you. All of those went from high labor intensity. So we've now attack that and gone to low labor intensity. Explain how that magic happened. >>I think that the example off a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about their data in its Price States catalog, if you like, um, imagine trying to do that manually. You need to go into every individual data store. You need a DB a business analyst, rich data store they need to do in extracted the data table was individually they need to cross reference that with other data school, it stores and schemers and tables. You probably were the mother of all lock Excel spreadsheets. It would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of data lots of these things is, um it accelerates the ability to water may, But in some cases, it also makes it possible for enterprise customers with legacy systems um, take banks, for example. There quite often end up staying on mainframe systems that they've had in place for decades. Uh, no migrating away from them because they're not able to actually do the work of understanding the data g duplicating the data, deleting data isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems assistance systems that are out of support. Go back to the data catalog example. Um, whatever you discover invades, discovery has to persist in a tool like a data catalog. And so we automate data catalog books, including Out Way Cannot be others, but we have our own. The only alternative to this kind of automation is to build out this very large project team or business analysts off db A's project managers processed analysts together with data to understand that the process of gathering data is correct. To put it in the repository to validate it except etcetera, we've got into organizations and we've seen them ramp up teams off 2030 people costs off £234 million a year on a time frame, 15 20 years just to try and get a data catalog done. And that's something that we can typically do in a timeframe of months, if not weeks. And the difference is using automation. And if you do what? I've just described it. In this manual situation, you make migrations to the cloud prohibitively expensive. Whatever saving you might make from shutting down your legacy data stores, we'll get eaten up by the cost of doing it. Unless you go with the more automated approach. >>Okay, so the automated approach reduces risk because you're not gonna, you know you're going to stay on project plan. Ideally, it's all these out of scope expectations that come up with the manual processes that kill you in the rework andan that data data catalog. People are afraid that their their family jewels data is not going to make it through to the other side. So So that's something that you're you're addressing and then you're also not boiling the ocean. You're really taking the pieces that are critical and stuff you don't need. You don't have to pay for >>process. It's a very good point. I mean, one of the other things that we do and we have specific features to do is to automatically and noise data for a duplication at a rover or record level and redundancy on a column level. So, as you say before you go into a migration process. You can then understand. Actually, this stuff it was replicated. We don't need it quite often. If you put data in the cloud you're paying, obviously, the storage based offer compute time. The more data you have in there that's duplicated, that is pure cost. You should take out before you migrate again if you're trying to do that process of understanding what's duplicated manually off tens or hundreds of bases stores. It was 20 months, if not years. Use machine learning to do that in an automatic way on it's much, much quicker. I mean, there's nothing I say. Well, then, that costs and benefits of guitar. Every organization we work with has a lot of money existing, sunk cost in their I t. So have your piece systems like Oracle or Data Lakes, which they've spent a good time and money investing in. But what we do by enabling them to transition everything to the strategic future repositories, is accelerate the value of that investment and the time to value that investment. So we're trying to help people get value out of their existing investments on data estate, close down the things that they don't need to enable them to go to a kind of brighter, more future well, >>and I think as well, you know, once you're able to and this is a journey, we know that. But once you're able to go live on, you're infusing sort of a data mindset, a data oriented culture. I know it's somewhat buzzword, but when you when you see it in organizations, you know it's really and what happens is you dramatically reduce that and cycle time of going from data to actually insights. Data's plentiful, but insights aren't, and that is what's going to drive competitive advantage over the next decade and beyond. >>Yeah, definitely. And you could only really do that if you get your data estate cleaned up in the first place. Um, I worked with the managed teams of data scientists, data engineers, business analysts, people who are pushing out dashboards and trying to build machine learning applications. You know, you know, the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is on cleaning data, which really you don't want a highly paid thanks to scientists doing with their time. But if you sort out your data stays in the first place, get rid of duplication. If that pans migrate to cloud store, where things are really accessible on its easy to build connections and to use native machine learning tools, you're well on the way up to date the maturity curve on you can start to use some of those more advanced applications. >>You said. What are some of the pre requisites? Maybe the top few that are two or three that I need to understand as a customer to really be successful here? Is it skill sets? Is it is it mindset leadership by in what I absolutely need to have to make this successful? >>Well, I think leadership is obviously key just to set the vision of people with spiky. One of the great things about Ayatollah, though, is you can use your existing staff to do this work. If you've used on automation, platform is no need to hire expensive people. Alright, I was a no code solution. It works out of the box. You just connect to force on your existing stuff can use. It's very intuitive that has these issues. User interface? >>Um, it >>was only to invest vast amounts with large consultants who may well charging the earth. Um, and you already had a bit of an advantage. If you've got existing staff who are close to the data subject matter experts or use it because they can very easily learn how to use a tool on, then they can go in and they can write their own data quality rules on. They can really make a contribution from day one, when we are go into organizations on way. Can I? It's one of the great things about the whole experience. Veritas is. We can get tangible results back within the day. Um, usually within an hour or two great ones to say Okay, we started to map relationships. Here's the data map of the data that we've analyzed. Harrison thoughts on where the sensitive data is because it's automated because it's running algorithms stater on. That's what they were really to expect. >>Um, >>and and you know this because you're dealing with the ecosystem. We're entering a new era of data and many organizations to your point, they just don't have the resources to do what Google and Amazon and Facebook and Microsoft did over the past decade To become data dominant trillion dollar market cap companies. Incumbents need to rely on technology companies to bring that automation that machine intelligence to them so they can apply it. They don't want to be AI inventors. They want to apply it to their businesses. So and that's what really was so difficult in the early days of so called big data. You have this just too much complexity out there, and now companies like Iot Tahoe or bringing your tooling and platforms that are allowing companies to really become data driven your your final thoughts. Please use it. >>That's a great point, Dave. In a way, it brings us back to where it began. In terms of partnerships and alliances. I completely agree with a really exciting point where we can take applications like Iot. Uh, we can go into enterprises and help them really leverage the value of these type of machine learning algorithms. And and I I we work with all the major cloud providers AWS, Microsoft Azure or Google Cloud Platform, IBM and Red Hat on others, and we we really I think for us. The key thing is that we want to be the best in the world of enterprise data automation. We don't aspire to be a cloud provider or even a workflow provider. But what we want to do is really help customers with their data without automated data functionality in partnership with some of those other businesses so we can leverage the great work they've done in the cloud. The great work they've done on work flows on virtual assistants in other areas. And we help customers leverage those investments as well. But our heart, we really targeted it just being the best, uh, enterprised data automation business in the world. >>Massive opportunities not only for technology companies, but for those organizations that can apply technology for business. Advantage yourself, count. Thanks so much for coming on the Cube. Appreciate. All right. And thank you for watching everybody. We'll be right back right after this short break. >>Yeah, yeah, yeah, yeah.

(fast intro music) >> Narrator: From London England, it's the Cube, covering Coupa Inspire 19 EMEA. Brought to you by Coupa. >> Hey, welcome to The Cube. Lisa Martin coming to you from Coupa Inspire 19 in London. Pleased to welcome one of Coupa's spend setters, joining me now is James Wagstaff, the chief procurement officer of Provident. James, welcome to the Cube. >> Hello, Lisa, nice to be here. >> So you had a very busy day. Thank you for taking some time to talk to me about Provident, what you doing with Coupa. But give our audience an overview of Provident and what you guys do and deliver to your customers. >> Very good, so Provident is a ftse 250 UK financial services business. It lends money to people without access to mainstream lending. Um, so it's real focus is to do that in a responsible, caring way. So if you can't borrow money from Barclays or HSBC, then Provident is a company that will help you get back to access to that mainstream market. >> Individuals as well as like small businesses? >> Consumers, around two million people in the UK currently use Provident, either the credit card or our home credit or our car leasing business. >> Okay, so how long have you been there? >> I have been at Provident now since April of 2018. >> Okay. >> So we're coming up now to, I think 19 months, and we put Coupa into the bank, which is the credit card business in April or April/May. >> Okay talk to me, though, about about your journey in business and finance. One of the things I read about you is that you were encouraged from an early age to really understand all aspects of a business from operations to finance to marketing to truly provide value through procurement. Talk to me about the history there that you have. >> So I'm a big fan of mentor programs. So I think everyone should have a mentor, and I lucked into mine, a chap called Terry, who, for reasons best known to him, took me under his wing. I was quite old when I came to procurement. I was around late 20s, maybe 30, and he had a vision about what great procurement looked like, and it was a holistic view. So procurement at its worst can be very tactical, very cost focused, and Terry was very focused on the bigger picture, about top-line growth not just bottom line, and right from day one, he seeded that in me, and it's been the strength of my career. So I owe Terry, Terry Western, if he's watching, I owe Terry, I owe Terry everything for that. And then I spent the last 10 years as an expat. So prior to Provident, I had three years as the group CPI for VimpelCom, which is the Russian equivalent of Vodafone or AT&T, who have businesses throughout Soviet Union, CIS, and Asia-Pac. And then seven years with Huawei, who are China's largest private company, telecoms company, and I was traveling around the world on the sales side facing procurement. So that was a very sobering enlightening experience to see procurement from the supplier side of the table, and I think it's made me a different procurement person as a result in terms of the way the I treat people and relate to people. So that holistic nature combined with, I think, a very business-centric view of what procurement should do. >> Interesting, though, that you that you said, I got a late start in procurement, but your start was founded upon someone giving you very solid advice of look beyond that because this is an element of the business that can, somebody that clearly was seeing how transformative, but also how it was important for procurement to partner and understand different requirements and needs within each division within an organization, so it sounds like you didn't really grow up in that traditional siloed approach of procurement. >> I did not, and I think that for me it makes my life interesting. So I think if you're in procurement and the danger is you become quite siloed, you're very narrow, and I did my MBA quite recently while I was traveling just to get that bigger perspective. It makes the job fun. I mean, I think you know you can negotiate contract after contract after contract, but it's the context of what that's doing for the business. And I think when I looked at Coupa as a system, it was with that in mind. So looking at Coupa, not from the perspective of what it did for procurement, but how it was for end-user customers. So as a service, was it really, really simple to use? Did it feel like an Amazon shopping experience? Because that drives adoption, and if you can get people wanting to use the system because it's easy, then the data's in the system, and then the data's in the system you can do something with it. So you're not, you're not fighting that adoption issue that you would be on a lot of systems. So if you go to some of the big ERP systems, they can be really hard for people to change and adopt, and Coupa's not been like that. It's been relatively easy. >> Interesting that you talk about it as it needs to be as simple as an Amazon marketplace. As consumers we're so used to that, right. I mean, people transact daily and get fulfillment of whatever product or service they're ordering from Amazon within... Sometimes it's within an hour or two. So we have this expectation and this demand. To your point, though, about wanting to have software that would be as easy for your teams to take up, that consumer effect. Talk to me about that as an influence. Because you know, kind of right away experience with other systems that might be bigger legacy systems that are challenging to get folks to use because they're not that intuitive. Did you know right away when you came into Provident that I need to have something that is more consumer-like. >> I knew that we needed a system and because as a regulated industry, we had to control our spend. So the fact that we needed a procurement system was a given, so then the choice is what do you buy? I think you don't really need a big ERP unless you really want to spend a lot of money on assistant inspirations and complexity. So your then into the mid-market space. And, um, there's a lot of vendors out there that have had an on-premise model, been around a long time, but you can feel that when you use it. So I didn't do a paper-based RFP. I think this is probably a terrible way of evaluating systems because you can get a function list on paper, but that doesn't really tell you what it's like to use. So the procurement process was around video online demos. So getting users into the room, three hours for an online demo walk through the system. So it's a very non-traditional procurement process to buy a procurement system. And I think at the end of that, I think it was a more valuable process for it. >> Was that something that was driven by you or was that something that was driven by Coupa? Is that how they deliver that type of experience? >> It was driven by me, but I think it was welcomed by Coupa. I think, I think from the sales guys I think they do an awful lot of paper-based RFP, and I think it's a challenge because it's very hard to differentiate on paper. Actually, a lot of the systems kind of do the same stuff, but it's not what they do. It's how they do it. And you can't, you can't get that out of the paper. You have to see it and feel it and touch it. >> Exactly. One of the things that Rob Bernshteyn talks about, and he spoke about it this morning, is that the best UI is no UI. And he really talked about what they've done to be user-centric and talked proudly about the adoption that they've had. And you know, it's... We all know whatever software you're putting in an organization, all these, you know, whether its marketing, finance, operation, sales, if people aren't going to use it, it's not going to be able to deliver the value that whoever purchased it and brought it on needs it to do. Talk to me about that user-centric. Did you see and feel that right away in those demos? >> I think if you're a procurement guy, you have suppliers every day send you certain messages, and those messages are fairly consistent around, you know, delivering value and solutions. I mean, Rob's great. He's a bit of a force of nature. Um, you got to say that. But what I like about it is that he's got a very clear sense of vision about what the system should be, and I think he's done a great job of getting that throughout the company, top to bottom. And to date we've felt that. So normally what happens is you buy the software license, you sign the agreement, there's lots of love and care, and then kind of the vendor disappears a little bit, and you're on your own. And to date, Coupa done a great job. We got Damian Pinnell, who's our success manager. I get the sense that he really cares about whether the system is going to do what it promised to do. And how do we get more value out of it? Some of it is about selling more licenses because Coupa have got other modules they want you to buy, but that's kind of okay if the modules are delivering more value, then you don't mind paying for them. But even the modules we own, there is a real sense of are you exploiting it to the max? And that's pretty cool. >> What are some of the key values that you have gleaned so far in just the, what, maybe six months or so that you guys have been using the platform? >> So I'm getting, I'm quite surprised at the extent to the insights, the value I'm getting out of the insights. So as an example, and I'll be honest. Coupa told me that said your, your spend-through catalog is 27% and your industry top quartile is 95. And I kind of went, "Nah, I don't believe you." And then they said, "Your electronic invoicing could be 77%, and you're currently single digits." And I went, "Nah, I don't believe you." And then through the community we spoke to Co-Oper, another Coupa customer, and Marley there was saying, "No, we're doing it. We're at this. "We're at 95% or 97% even." And I went "Well, how are you doing it?" And she just talked me through how they sell it to suppliers and how, in my head, the reluctance to adopt actually evaporated because she was able to sell the idea to suppliers, sell the value as. She didn't force them to do it. She just said this is what you're going to get out of it if you do it this way, and she's genuinely got to 97. So what it's done for me is it's remove my own blockers in my own mind, you know, in my own head "You can't do this." Well, insights and speaking to other communities. Yes, I can. So it's opened my, changed my targets, changed what I think is possible. And I think that's cool. >> You look back to the beginning of your journey in procurement, business, and finance, when you were given this great advice, like "Be open-minded, understand how different parts of the business work," from then to where you are now and what you're able to deliver, in just a short time, leveraging Coupa, would have believed you'd been able to go from there to there? >> Uh, yeah, so Terry would always say to me, you know, if you're going to negotiate a deal, before you even pick up a contract, you would spend an hour with the business owner or the techie or whoever it is, and you just white board, at a technical level, what the solution is. I think that, years and years and years of doing that, of going deeper into technology and software and integration and through deal after deal after deal, when you come to run the project, to implement Coupa, you have that as a foundation. So you're not just at the surface and relying on other technical people because you're lost when you get to this level of detail. You've already got a little bit more depth. So I think that was the big spin-off, in a way. That you're able to have more in-depth conversations at a technical level, which you need to unblock stuff. >> So some of the news that came out today. They talked about what they're doing to expand Coupa Pay with American Express. I was just talking with Barclays. Barclays card been on that for a little while. Looking at the payment space for instance, on the BDC side, we have this expectation as consumers. We can do any transaction, we can pay bills. It hasn't been as... On the B2B side, it hasn't been as innovative. Some technology gaps, large scale. Where do you see Coupa in that respect with what they're doing with Coupa Pay? Do you see that influence from the consumer side that might eventually become an important part of what you're able to do at Provident? >> We haven't enabled Coupa Pay, so I'm in a position to talk authoritatively about it. >> In terms of taking the consumer and demand? >> So I look at the one-time-use credit cards, and I'm really quite excited about what that could do, and I kind of get the business sense and the use case behind that. So that's certainly on our radar. I like the risk-aware products as well, using the big data and AI stuff. So, there's a few things in the road map I've got my eye on. We're deploying expenses module in December/January, so that'll keep us busy on that. And then we'll need to route six months of data through Coupa so that we've got enough of a data pool to do the analytics. So we've got a busy road map, that's for sure. >> For a last question for you, James, for peers of yours, whether they're in financial services industry or not that are facing similar challenges and opportunities to transform procurement, what's you're best advice? >> Mmm, go and spend a few years as a supplier. I think procurement suffers a little bit from people who have only ever been in procurement. And I think that different perspective would be enormously powerful. So I think if we could get more marketing people, more lawyers, more different people and different professions into procurement, I think it would give you a broader perspective rather than a "I've grown up in procurement the last 20 years" sort of perspective. So go and get that holistic, global view would be my suggestion. >> Well, James, that's great advice for anybody, anywhere, and I'm sure Terry would be proud to hear you say that. >> I'm sure he would. >> Thank you so much for joining me on The Cube and sharing with us what Provident is doing with Coupa. We appreciate your time. >> It's been a real pleasure. Thank you, Lisa. >> Excellent. To James Wagstaff, I'm Lisa Martin, and you're watching The Cube from Coupa Inspire 19. Thanks for watching. (computerized tune)