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CDO Interview with Jude Schramm and Guy Peri

Jude Schramm and Guy Peri

Jude Schramm, Chief Information Officer at Fifth Third Bank &
Guy Peri, Senior Vice President, Chief Data & Analytics Officer, Information Technology at Procter & Gamble

A chat with Jude Schramm, chief information officer at Fifth Third Bank and Guy Peri with Procter & Gamble.

Transcript

Anthony Deighton:
I am Anthony Deighton, the chief product officer at Tamr and we’re partnering with CDO Magazine, the MIT CDOIQ, and the International Society of Chief Data Officers in a series of interviews and today I have the pleasure, the company, and opportunity to chat with Jude Schramm, chief information officer at Fifth Third Bank and Guy Peri with Procter & Gamble. So, welcome, gentlemen. It’s a pleasure to spend time with you virtually. Maybe we could just start and help everyone who’s listening just with a little bit of context. Maybe give us a quick introduction to yourself. I think most people are familiar with P&G and with Fifth Third Bank, but maybe broadly what your areas of responsibility are in your respective organizations. Jude, maybe we start with you since you’re on my left.

Jude Schramm:
Yeah, sure thing, Anthony. I appreciate the time. So again, my name’s Jude Schramm. I run technology and operations for Fifth Third Bank. We’re the ninth largest US bank. My responsibilities primarily include all of technology, so data infrastructure, application, line of business, CIO structures, all of tech in that space as well as digital and I have banking operations, so call centers, I have some middle office, back office, all of the things that really run the bank.

Jude Schramm:
I like to think of myself as really the supply chain of the bank if you will, and so for me that’s really the scope. I’ve been with the bank since March of 2018, so a little over two years. Prior to that, I spent time in the industrial sector of the US economy for almost 20 years, so that’s a little bit about myself.

Anthony Deighton:
Awesome and, Guy?

Guy Peri:
Hi, Guy Peri, great to be with you guys. I have been with Procter & Gamble 24 years. As of 2015, was appointed the chief data and analytics officer at P&G. Primary responsibility is to serve consumers and customers through data analytics and technology and really accountable for the data science group, the data management team, the underlying platform that serves our data and analytic capabilities and then the embedded transformation teams that are activating capabilities to drive growth and value.

Guy Peri:
Prior to this data and analytics role, I’ve essentially been in analytic roles for the balance of the 24 years serving across various categories and countries. I spent four years in the Middle East and learned a lot about developing markets and how to leverage data and analytics to drive business growth.

Anthony Deighton:
Excellent, so it feels like for both of you, you’ve worked in organizations where data really is at the core of how you deliver value and certainly your role within that organization is to serve data to the business for making better decisions. Maybe if we could just … And, because you have such experience with it, maybe share a little bit about how you’ve seen the role of data inside your respective organization shift and change, especially you, Guy, with 24 years of experience at P&G. I’m sure that the way P&G thinks about data has changed significantly over that period of time and then, Jude, the banking sector, that data is really at the heart of that and really being thoughtful about how to take advantage of that asset, but maybe, Guy, start with you, how’s the role of data changed at P&G with time?

Guy Peri:
The role of data has changed fairly significantly over the years, but what hasn’t changed is our strategy of serving and winning with consumers and customers, so that is pervasive across, at least my career at P&G, and probably will continue to be the same, and what we’re focusing on at P&G as part of our business strategy is what we’re calling constructive disruption. We believe that other than the purpose, values, and principles, pretty much everything else around us is changing in this world that we live in and our constructive disruption is really focusing on five key areas that’s constructively disrupting how we do product innovation, packaging, how we communicate and market to consumers, how we distribute and engage with retailers and sell our products, and then lastly how we provide consumer and customer value.

Guy Peri:
So, data is an underlying enabler for all five of those constructive disruptions and we believe when we drive superiority across all those we win and best serve our consumers, and so the role of data has really evolved into the new strategic asset and really, we talk a lot about data as a strategic asset and just like the raw materials that go into our products or the brands and manufacturing lines that we have in our businesses. Data is becoming increasingly more of an asset that needs to be managed, governed, and treated accordingly.

Anthony Deighton:
It’s super interesting because we would think of P&G, the element of competitive advantage would be the manufacturing line or the marketing campaign or the superior shelf placement, but your point is actually at the core, all of those things are really about data, and you’re willing to question everything as it relates to how you bring that to market. I think that’s very similar in the banking sector. All bets are off, it’s a completely new world. We need to think completely differently about how we deliver, in your case, banking services. So maybe, Jude, how has the role of data changed?

Jude Schramm:
So, Anthony, it is a great question. From a banking perspective, we really think of our information, and we have two camps, there’s the really idea of protecting customer information, there’s data privacy, and all the things we have to do to make sure that when we have such sensitive information of a consumer of ours, or a customer of ours, that we’re doing everything we can to give them the confidence that we can protect them.

Jude Schramm:
Somebody’s financial information is about as personal as it can get, so we invest a lot of time and technology and as we’re getting more and more access to more and more information, which you guys all know better than me, the exponential growth of data just requires a lot of investment on our side to protect it even more so. And so, we spend a ton of time doing that, thinking about all of the different data privacy laws going in state by state, at the national level.

Jude Schramm:
How do we always stay in front of and give that level of confidence to our customers? So, you think about integrating our enterprise data organizations with our information security cyber organizations, they’re blending together at a pace that we just had never seen before when you thin about partnering and strategy. And, then the second way is really about … And, Guy did a lot of this, it’s really about, how are we creating value for the customer? Because with the information that we have, we’re now able to provide much more tailored, personal banking products and solutions for customers that we just couldn’t do in the past.

Jude Schramm:
In the early days of banking with data, I’d say just even three years ago, if you had a product, if you were a [inaudible 00:07:40] checking account customer of ours, you got the same marketing and the same offers across millions of households and now with the information that we have, we know that Anthony is potentially looking at a certain mortgage or looking at another product of ours. We can get very specific to reach out to you and try to help you and give you and advantage that you never would have had before as a customer and it doesn’t feel like spamming and marketing.

Jude Schramm:
It’s what we want to be, which is more of a trusted advisor and a partner to our customers that are trying to financially manage the world. And so, that’s the way we think about it is, how can we just come alongside our customers and utilize information to make their lives that much easier. We don’t want them to wonder or think about how to use their money, we want them to have it where they want it when they want it, so it’s available and accessible.

Anthony Deighton:
It feels like there’s a little bit of a distinction here. Fifth Third Bank is dealing with this highly personal information and the privacy and security of it is really important and that allows you to create these highly personalized offers and personalized product really, whereas with P&G, everybody buys the same P&G products, but I bet you that’s not true. And so, Guy, how do you think about data for personalization and is it going to be the case soon that when I’m buying my detergent or my soap or my consumer product, I’m getting a really personal experience, or maybe it’s more on the marketing side and thinking about personalizing the offers? How do you think about data, privacy, personalization? Are you doing what Fifth Third’s doing?

Guy Peri:
Yeah, we actually have quite a few initiatives that are really about personalization both on the brand marketing side, so how do we make sure we deliver the right message at the right time through the right medium to our consumers, much like what Jude says, in a way that’s relevant versus not viewed as relevant. So, data underlies all of that and you can imagine what’s associated with understanding the consumer in enough detail to be able to serve them in a relevant way with the right message in the right medium.

Guy Peri:
We also have, from a product perspective, an emerging area which we’ve actually showcased over the last two years at the Computer Electronics Show, which is a smart product and these smart products are IoT connected devices, which essentially will allow a personalized experience. And so, these devices help us understand usage and even fragrance, scent, and things can be personalized based on the individual that’s walking in the room. And so, we have a number of devices that have launched that we showcased at CES and it’s a whole new frontier for us and that really the product is now turning into a technology-enabled device and it’s all about enhancing the consumer experience.

Guy Peri:
That’s ultimately why we are investing in this space and we believe that the better of consumer experience we can provide, and the data serving as a way to personalize, it’s a win for consumers.

Anthony Deighton:
So, that’s amazing. Jude, you talk about going from everybody gets the same checking account to this highly personalized experience and now it seems like that’s even happening in the CPG side and that’s fascinating, this idea that my experience with a P&G product would be different than Jude’s. I think that’s really exciting and it all comes back to this core idea that ultimately all businesses are at their core data businesses. We both generate data, we collect data, and maybe we purchase data from third parties. We’re creating an asset at the core of our business, which is not just the physical asset of the banking branch or the manufacturing plant or even the relationship value, the relationship with the consumer, or the relationship with the retailer, but it’s this core asset of the data that we generate as we manufacture our products, whether we manufacture them from a financial services perspective or actually bring a physical product or to your point with IoT data driven products to market.

Anthony Deighton:
So, this idea of data as a core asset, maybe you could share and maybe we start with you, Guy, some of the challenges as a chief data officer you’ve run into with thinking about the volume and the variety of the data that’s coming at P&G. What are some of the challenges of managing this asset that’s different than managing some of the other assets that you have?

Guy Peri:
I think it really starts first with the data strategy and ensuring that every business has a clear data strategy that’s linked to their business strategy because ultimately, in my experience, data is only as useful as the strategy and the business that it’s driving growth and value for. So, having every business define a very deliberate data strategy and then using our data and analytics expertise to create the data signals, maintain the data signals to serve that business strategy is really the first important step and we’re largely through a very robust set of data strategies across our businesses.

Guy Peri:
The second part is once we’re clear on those data signals, probably the two biggest challenges we’re facing and we’re actively working on is one, ensuring that we have consistent quality of the data signals as we operationalize these data signals into our decision processes. As we’re becoming more and more dependent on data to inform our decisions at a very granular and more real time level, we simply can’t afford to have those data signals have quality issues, so we’re leveraging a lot of AI capabilities to do automated data quality checks. We believe self-healing data in that we’re able to identify when a data quality issue happens, address it ahead of any decision maker experiencing that, so that’s the first challenge that we’re innovating on.

Guy Peri:
And, the second very relevant for the Tamr discussion is the integration of data and the harmonization of this data set and that is not an easy task in enterprise level where we’re global companies serving 180 countries and 5 billion consumers a day. You can imagine the integration challenges associated with that, but we’re again, using AI to help with that challenge and what I’ve found is that the real answer is a combination of technology and process ownership and when we combine process technology and the culture and reward systems, those three things really drive the transformation in driving value through data.

Anthony Deighton:
So, it’s really about creating this alignment between the business strategy and the data strategy that you began with, but then thinking about how the supporting technologies integrate the best of what a machine can do, so this AI and ML-type approach, I think a theme we’ll come back to in a moment, but combining the best of what that technology can do with the real human intelligence, the hundreds of thousands of employees and partners and channel and all these people who really understand, in your case, consumer intent, and retail behavior, and creating that relationship.

Anthony Deighton:
But, I think that’s got to be doubly true even in the banking segment. At Fifth Third, how are you thinking about … So, what are some of the challenges or maybe some of the different challenges that you struggle with as it relates to data?

Jude Schramm:
I think for us, one of the challenges with data pairs real well with the underlying effort that us and honestly a lot of banks are going through with we ride on the back of a lot of legacy technology. Most banks still employ mainframes that are 30, 35, 40 years old. We have probably four different generations of technology that at this point with the acceleration digital into where it’s going across our sector, everybody’s really having to … You break free from that legacy and go into more cloud technologies given the newer technologies that are out there in the marketplace and honestly, disruption from non-traditional banks and from technology companies into banking is forcing that at a much faster clip.

Jude Schramm:
And, where that ties to data for me is where your data sits, the position to be on the edge for use and consumed as quickly as possible in a matter or so. For us, thinking about how do you position your underlying architecture to allow data to be both collectively together and accessible and consumable and usable and putting it where it’s most accessible the fastest are two very different mindsets from the way we’ve thought about data up to this point where data is flowed better and stored away and protected and pocketed in little silos and then the real challenge to all of that comes when you want to do something new in technology, sometimes you get the advantage of starting over.

Jude Schramm:
Cloud’s sometimes a nice start over. I can divorce myself from the old stuff, I can sunset it, I can move on to new and I can almost build clean. Well, you can’t start over with data. The data is what it is and it’s where it is, and so while I’ve got some nice new shiny technology in different places I never had it, it’s the same data coming from the same sources that’s disparate, it’s siloed, and it’s all over the place, and now I’ve got to make it look like it’s shiny and new as well.

Jude Schramm:
So, it doesn’t have the benefit of infrastructure and the cleanliness of that, so how do you marry those two things to get the most value out of data is really one of the biggest challenges I think we’re finding, is that transitions easier on infrastructure and it’s easier in software and it’s easier in those more tradition IT worlds than in the data world because you can’t start clean, you got to start with the mess you got to some extent and you’ve got to fix it and you’ve got to fix it quick and then you’ve got to really rethink the whole strategy to Guy’s point I think.

Jude Schramm:
That strategy that he laid out is very identical to the way I think most companies in Fifth Third’s thinking about it. So, you got to marry all that together, then make sense of it, and time isn’t our friend in my world. This industry moves very quickly and disruption’s happening fast, so you’re always up against the clock as well.

Anthony Deighton:
I think your point is very well taken. Not only do you start with the data you have, but it doesn’t stand still. You can’t be like, “All right, everyone stop banking for a day while I get my stuff in order.”

Jude Schramm:
That doesn’t happen. It is quite the opposite if you think about traditional banking up to probably five years ago. The whole bank operated on these banking hours where it was 8:00 to 5:00 on Monday through Friday and 8:00 to 12:00 on Saturdays, and so banking systems were set to run massive overnight processing because that’s when all of the transactions of the day get rolled up in 30 year old legacy systems.

Jude Schramm:
Banking’s a 24 by seven, 365 desire of our customers now. They don’t ever want to be down, they don’t ever want to be off. Branches don’t close because you’ve got your mobile apps. So, when do you fit all that data processing in your world when now the time that you did to do it is no longer off, it’s now on? And so, it brings really different challenges to an industry that didn’t have to think about it that way for a long time.

Anthony Deighton:
So, Guy, Jude brought this issue up of the cloud and moving things to the cloud, and then you actually brought up machine learning as a new technology. How do you see cloud and ML, machine learning, generally as catalysts for change in the data strategy that you have at P&G? Is that something that’s helping? Is it a flash in the pan and it’s a good idea that it’ll never stick? Is it something you like leading into? What’s the thinking there?

Guy Peri:
Cloud and ML are two important components of our digital foundation, so we have a digital strategy that’s focusing on serving consumers and customers, supply chain and product innovation and the underlying foundation to enable those strategies is really the migration to cloud and then leveraging AI, ML to drive insights to help make better decisions and personalized consumer experiences. So, we very much look at the cloud as an opportunity to address a lot of the challenges that Jude mentioned because we, like Fifth Third, even though we’re in completely different industries, we have a lot of parallels, that we also have lots of legacy systems and legacy code that we’re managing through.

Guy Peri:
The cloud gives us an opportunity to not only reset that architecture, but also drives the agility and the elasticity we need to run our business. As you can imagine, in a business like P&G with so many different brands and country category segments, we have different needs at different times, so we need the elasticity of a cloud to serve those needs and be able to move very, very quickly because our business is a very dynamic business.

Guy Peri:
And so, we’re finding that the cloud is a key enabler there. From a data perspective, we very much, like many companies are standing up our data lake. And so, that data lake will be the repository where these integrations and harmonizations happen, and then we’re curating that data via self-service tools that allow our business users to get a personalized experience on that data, so they can make the right decisions. And so, we’re looking at cloud and ML as a chance for us to leapfrog and a really important set of tools is to deliver that constructive disruption I mentioned earlier.

Anthony Deighton:
That’s fascinating, so from P&G’s perspective, it sounds like cloud and ML are strong enabling technologies. And, I love this idea of leapfrogging, so you take the challenge you have, which is the legacy systems and old infrastructure and this is your opportunity to completely disrupt and do things in a new and fresh way, but with the advantage of that tremendous asset that you mentioned, which is all of this data. To your point, yes, that’s the challenge, but it’s also the great benefit that you have as organizations. Maybe just as a quick side note, at Tamr, we also see this move to the cloud and machine learning as a really important catalyst because what that does is as data moves to the cloud, it then also sits next to really highly elastic compute capabilities and it means that machine learning based approaches, which previously would have been too difficult to run on premise, you can’t spin up 1,000 machines on premise if you don’t have them, but in the cloud you can.

Anthony Deighton:
You could have 1,000 machines, you could have 10,000 machines working on a machine learning algorithm, so Tamr’s approach to using machine learning and, Guy, you pointed this out, but to improve the quality of data and break down those silos and integrate data together, you can actually use machine learning to do that because now the data’s sitting in the cloud next to this highly elastic computer infrastructure. But, Jude, from your perspective, just to bring it back to you, the cloud and ML, how are you guys thinking about it?

Jude Schramm:
Probably very similar. At the end of the day, what we’re doing a lot if you think about early on, I talked about the customer experience and we’re trying to help customers with, we call it their next best decision, or our next best discussion. We have a lot of data processing that has to happen and we want it to happen on the edge and we want it to happen as close as we can in the cloud to allow us to take advantage of all of the capacity and compute capacity it has. We’re doing a lot of machine learning, we’re running a lot of big datasets just to try to get to that understanding and that experience that we’re trying to drive for our customer.

Jude Schramm:
And so, a lot of what we’re going through right now in that journey is what data sits where, what data stays on, what data goes up in the cloud, how do you create those egress points, so the data can move back and forth as it needs to, and there’s a lot of in depth work with our data sciences group, with our enterprise data office, around really partnering on those decisions.

Jude Schramm:
And, then even in that curb it’s where we’re integrating our information, security, and cloud engineering team. We got a handful of cross-functional squads there working on just this exact problem that-

Anthony Deighton:
Opportunity.

Jude Schramm:
Opportunity, yeah. Where does data have to sit to maximize its value? Well, balancing and harmonizing that with protecting the data as well as protecting your customers. So, it’s a big push and pull. We’re engaging a lot of partners, we’re working with our own expertise, but it’s a probably the most challenging proposition we have. For one reason, because a lot of people honestly are just still a little paranoid about putting their data in the cloud.

Jude Schramm:
And, what I’ve told other companies and customers that I’ve talked to it’s, I’m a bank, I’m not a data center, so if you feel better that it’s with me, then maybe we need to rethink the whole value proposition because Amazon and Microsoft and other big cloud providers spend billions of dollars on their data centers and on their infrastructure to provide cloud solutions and we should learn how to be comfortable and take advantage of that and maximize that opportunity versus trying to think it’s an on-prem versus off-prem discussion because I think there’s harmonization that what we do great we can do great, what they do great they can do great and if we can find that right balance, we can create a lot of advantage for us I think, relative to even our customers.

Anthony Deighton:
You’re the one who started the conversation around this question that privacy and data security as being really core to the bank, and to see you leaning in on these cloud technologies, it’s hopefully very useful to listeners to think about that. I’m curious, just if you wouldn’t mind, are there any specific examples of data projects or maybe even data projects you’re particularly proud of that really leveraged some of these newer technologies that you think people would find interesting? If you wouldn’t mind sharing them and maybe we’ll just start with you, Jude, since you were sharing before around data and privacy a bit.

Jude Schramm:
I’ve got to mention this, but I think I’ll get into a little more detail in describing it. That’s what we call our next best discussion. It’s really one of the core tenets of our future, which of you think about, and now just going into the whole banking analogy … It’s not an analogy, but the true story of … We have bankers, we have relationship managers, their job is to really have a portfolio of customers that they support and across different aspects of our customer base, we divide up that way by either location or by just types of products and accounts.

Jude Schramm:
In a traditional way, they come in every day and they just have call list of customers that they’ll just pick up the phone and call and hey, how are you doing? And, checking in, how’s the last mortgage you got, blah, blah, blah, what are you thinking about, and they’re just trying to use conversation and really not a lot of information to help that customer to really understand, is there something we can be doing more to help them in some way with the new products that we have, new things that we think might be ideal for them which honestly what that is, is a lot of cold call marketing and the same thing can be said of the way we’ve done traditional marketing. We blast out mailers, we do stuff on the website.

Jude Schramm:
Over the last two years, our data sciences group and enterprise data team have worked together to do a lot where we talk about harnessing data, so that now that relationship manage comes in every morning and instead of seeing this laundry list of names and phone numbers. He actually has a dashboard of best opportunities from what the machine learning, the algorithms and what we’ve been able to do from some of our data models to say, “Hey, here’s 10 customers we think you should call today and here’s why and here are the things individually that each one of them would think are probably most beneficial for you to call them and talk about.”

Jude Schramm:
And again, that could be a customer that maybe we’ve noticed in a time like this, a pandemic, that maybe their auto deposit payroll hasn’t been happening, so maybe we can call it off for hardship relief, maybe we can say, “Hey, the money hasn’t been going in like we thought. It’s time to reach out, it’s time to offer some help for them because maybe they’re going through a challenge, and we’ve done a lot of things as we can do to take a lot of burden off of them from a financial standpoint.”

Jude Schramm:
We’ve noticed that this customer’s spending more money at Home Depot, or Lowe’s, or somewhere and potentially that’s an opportunity that maybe they’re looking for a home line of credit or maybe they’re looking for projects they’re doing that we can offer some solutions. We’re trying to even bundle things we do with other services like Zillow and other places where we can pair insurance and pair other comparable products with things that we do as a bank. We can use that information and just make that interaction with our customers a lot more valuable and the goal that really is to ultimately drive the personal experiences that customers expect from a bank, but I think they’re coming to expect from everywhere. We talked about disruption and, Guy, you guys talk about personalizing the experiences for your customers, but we try to think about it.

Jude Schramm:
It’s not really Fifth Third trying to be more personalized than US Bank or Bank of America. What we’re trying to think about is our customers’ expectation on their experiences was their last experience, which often is Amazon, buying something there or it’s buying something online somewhere with some interaction. We think that it’s that experience that we’re trying to move forward and help be a part of.

Jude Schramm:
It’s not a competitive thing, it’s not any of that. It’s customers come to expect these experiences and they should expect it from their bank, and so that’s what we want. We don’t want to waste a call for you. We don’t waste your time by calling you on something you have no interest in. And, we’re doing this now, we’ve got a little handful of our relationship managers that are already using these models and using those tools and we’re seeing a ton of I think customer value from those and we’re just going to keep growing and growing those capabilities over time, and that’s probably the one I’d say we’re most proud of these days.

Anthony Deighton:
I love that, so it’s this idea of using data to create a better customer experience, not to save money, which it may have that effect as well, and I love this idea that the interaction you would have, in your case, over the phone with a banking advisor isn’t seen as an annoyance, but is seen as a wonderful experience that helps me, and if it’s a really personalized experience, one that speaks to the need that I have in the moment, all the better.

Jude Schramm:
Exactly.

Anthony Deighton:
What a great moment. So, Guy, from your perspective, you could certainly build on this idea of the personalized experience and I’m totally fascinated by what you guys are doing with the personalized IoT-based P&G experiences or if there’s another example that’s better, please feel free, but where have you seen real success with data at P&G?

Guy Peri:
So, we’ve seen quite a few successes behind our AI, ML, and big data applications. I’ll pick two just to really illustrate some constructive disruption. One is what we call neighborhood analytics, which is the ability to really, at a much more micro and precise level, understand our consumers, their shopping behaviors, and their needs and when we do that by combining various internal and external data sources, we’re able to much better serve them in terms of the assortment that shows up when they go in store. We’re also able to grow categories because we partner with our retailers to put in the right products that actually will sell in that specific demographic to neighborhood, so that capability has been launched across 24 different markets and is really giving us precision in how we go to market and how we execute with our retail partners.

Guy Peri:
The other example I’ll share is what we call smart selling. So, it’s actually similar to what Jude is doing at Fifth Third in that some of our developing markets we have distributors that sell on our behalf and in some of the countries, these distributors, the average tenure the account distributor has on our business is three months. So, they really don’t know Procter & Gamble’s products and there was some situations where they were just going based on alphabetical order on what brand to sell in first.

Guy Peri:
So, Ariel, which is the Tide of Asia and Europe, got a lot of distribution and poor Pampers didn’t get much. So, we said, “There’s got to be a smarter way forward,” and so we’re using our big data and AI, ML to essentially do smart selling. So, much like Jude described as the distributor rep shows up to a store, it’s precise based on that store and the demographics around the store, the algorithm recommends exactly what’s the next best product to sell in, and what is personally the right thing for that store and that store owner to buy, to help grow their business, and that’s driven a great value in growth across our geographies. So, those are two examples. The IoT example is an emerging one.

Anthony Deighton:
Sure.

Guy Peri:
We’ve launched quite a few examples and we’re really looking at how do we personalize in a relevant way. It’s all about precision, going from mass marketing to precision communication and experiences.

Anthony Deighton:
While Jude’s trying to make the consumer successful in their financial life, Guy, you’re really thinking about how to make retailers successful in their changing world and I think this idea of the micro-targeted assortment is a brilliant one. It has this direct benefit to consumers as well because … And, we’ve all experience this. You show up in the retail environment and the product you’re looking for is not there, and it’s not because the product doesn’t exist, it’s because they gave something to the wrong place, not the wrong place, but they didn’t do good targeting, and if it was there, I would buy it.

Anthony Deighton:
And, that would create a better experience for me, and obviously a better experience for retail and by its extension, a better experience for P&G. So, I think it’s brilliant and I appreciate you sharing it. Maybe in the last few minutes if you wouldn’t mind, let’s throw our minds to the future and I’m going to put you on the spot and ask you to make a prediction about where you think we will, as a community of chief data officers, and people who are concerned with the use and strategy around data as an asset, where we’ll see the biggest progress in terms of the usage of data, you could either think about it in the context of your own organization or more generally, over the next five years, what’s the biggest change that you think you’ll see in terms of how your organization engages and uses and makes value out of this precious asset of data. Maybe we’ll start with you, Jude.

Jude Schramm:
It’s a good question. I think for me, if I had to gauge something, I think there’s that question of … And, you talked about how we’re doing marketing and targeting, I think the more, more, and more personal that we’re getting, that’s where I think I see it continuing to grow and what I mean by that is there’s some interesting companies out there that … I’ll give you the weird, scary side of it, that they’ve got some capabilities. They’re going to be on geo-fencing and go to something called geo-framing which is really getting to the point to where on this device … You probably can’t see it, it’s my cellphone, they can get you within two or three feet of that thing moving around.

Jude Schramm:
And, if you think about that, and I could liken this to my world of banking or maybe in the world of retail, being able to offer me things based off of not just who I am, but where I am is where I think this could even get to go. If I know that a customer is walking into the bank to do something, maybe because I know that they’re literally walking in the bank and I can be there greeting them and bringing that experience even more personal because of that. I think it goes that far.

Jude Schramm:
I think it starts to get to where you’ve seen marketing go from this broadcasting, mass audience things down over the years more and more and more and more, we all sit there and question if Alexa isn’t listening in to us and giving us stuff based on that or watching what we’re searching online and presenting things on Amazon. That stuff’s fairly real. I think that trend gets even more and more personal, and it starts to look at a device level look at, depending on where you are, trying to help your life just get more easy and having to do less to think about what you want to do, but making it happen as you’re going through it.

Jude Schramm:
To be way out there, I think that that’s a totally realistic place that we’re going pretty quickly. I think within three to five years you can very easily do very little from leaving your home by having your phone and being able to do most of it, and I think when you do leave, based on where you’re going, a lot of ease of what you’re doing could happen almost seamlessly and more so than it does today, just your information getting to the point of being that smart and being that fast because a lot of what machine learning and what we’re working on is really about getting to decisions faster, getting to things faster.

Jude Schramm:
When you think about those decisions turning into outcomes or experiences, that’s where it’s all going to keep going. How does my life become so frictionless? On the standpoint of, the information I need is just always right here, and the decisions I’m making have things in front of me to make them easier and faster just on an exponential scale and you’re going to start to think on tying movement and tie different things into that. It’s not just going to be about data and a system, it’s going to be about data about you as a person and what’s going on.

Anthony Deighton:
Cool, really making precision, personal, and in the moment. I think that’s right. Guy, I’m curious from your perspective, any five year predictions?

Guy Peri:
Yeah, I have two. One is, for our business and many businesses across industries, it’s all about the consumer at the center, and so along the lines on personalization, every consumer in every industry that I know of wants a seamless omnichannel experience. And so, the worlds of physical and online, there is really no seam. At the end of the day, you expect the store to know who you are and you expect when you go online they know who you are, what your needs are. So, data and ML will facilitate that seamless experience more and more.

Guy Peri:
And, the second area I think is operational decisions, whether it’s how we personalize communication or experiences for consumers, or even internally within enterprises, how decisions are made, I believe machine learning, and AI, and ML will be much like electricity in three to five years and that it’ll be everywhere and nowhere and that we as professionals, our job is to operationalize these algorithms in ways that almost make it seamless and almost transparent to the decision makers that there’s actually real data and ML behind the scenes and those decisions just become more intelligent by having these capabilities to underlie it.

Guy Peri:
But, the decision maker’s experience is so seamless that it’s not obvious that this technology is behind the scenes and that I think is the next three year journey for many companies, including P&G, is operationalizing algorithms and making them so pervasive, so high quality, protecting them with the best cyber security and privacy capabilities we have, such that they’re so pervasive that it’s just the way things operate.

Anthony Deighton:
I think that’s actually spot on, so this idea that we want to bring the best of our humans are capable of and augment them with these machine learning capabilities, so they’re able to make better decisions. And, Jude, to your point, they make these highly precise personal decisions and I think you example of the relationship manager on the phone is the perfect example. You support them with an ML algorithm, but ultimately what you want is the conversation with the person.

Anthony Deighton:
And, Guy, to your point, that’s exactly this idea of finding real world use cases for machine learning and almost certainly supported by [COD 00:39:50] infrastructure that makes that possible. Selfishly from my perspective, that’s exactly that we see at Tamr. If we can apply machine learning algorithms to the challenge of messy data, then we can actually create this clean data infrastructure, which allows both of your organizations to make these visions a reality.

Anthony Deighton:
So, I think we’re out of time. I thoroughly enjoyed the conversation and appreciate the time with you and your thoughtful answers and that was a great conversation and also thank you to CDO Magazine for arranging this and our partners at MIT CDOIQ and the International Society of Chief Data Officers and this was a great time and I look forward to more interactions in the future.