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InfoQ Homepage Podcasts Engaging with and Serving the Digital Seeker to Craft Great Experiences

Engaging with and Serving the Digital Seeker to Craft Great Experiences

In this podcast Shane Hastie, Lead Editor for Culture & Methods, spoke to Raj de Data about applying AI in high-performance tennis, serving the digital seeker and delivering exceptional digital experiences  

Key Takeaways

  • To truly leverage technologies like AI, organizations need to make digital transformation a part of their DNA 
  • The most successful digital services are about delivering the kind of experience that goes to the underlying need, or motivation, of the customer, and solves for that
  • If you build the experience for what the human being is seeking, not what they're buying, that is actually what creates really distinguished experiences
  • The three A’s – Artificial Intelligence, Ambient Technology and Application Programming Interfaces – combine to enable great digital experiences 
  • A digital experience platform is a collection of services, APIs, that are readily available for you as a developer to build the experience of choice


Shane Hastie: Good day folks. This is Shane Hastie for the InfoQ Engineering Culture podcast. Today, I have the privilege of sitting down across the miles with Raj de Datta. Raj, welcome. Thank you very much for taking the time to talk to us today.

Raj de Datta: Shane, it's great to be here with you.

Shane Hastie:You've written and released a book called The Digital Seeker. I'd like to explore that with you, but before we get into that, who's Raj?

Introductions [00:28]

Raj de Datta: I'm CEO and Co-founder of BloomReach, which is a software company that I started, that is all about AI power and e-commerce experiences. So, that's my professional part of my life, and then live in Palo Alto, California with my two kids, and my wife. When I'm not working, I'm an avid tennis player and tennis fan. So, spend some time on that, and otherwise try to spend time with the family and drive the business.

Shane Hastie: Maybe, we can delve a little bit first into the tennis aspect, because I saw in your bio, is it the high-performance team that you support for the USA?

Raj de Datta: Yes, that's right. So, I'm on the board of what's called the Player Development Council, which is really the group for the US tennis association that tries to foster, and promote professional, men's and women's tennis. And so, I support them and try to help improve the trajectory of American men, and support the strong trajectory of American women, on the professional circuit.

Shane Hastie: And, good doing that using your skills in AI, how does that come together?

Applying AI to enable high-performance in tennis players [01:27]

Well, like all organizations, I think it was fascinating to work with the USTA, because they, like many organizations had an old school way of driving training of its players, of running academies of doing video analysis. And so, have tried to work with them to evolve, to be much more digitally native. And, what that means? Is everything from working through use of AI in video to highlight specific moments that players can learn from, have that work with the training schedule to try to figure out what works and what doesn't and what drives results. And then of course, just really helping to make sure their digital presence to book tournaments, and book courts, and train works better. So, like all organizations digital can be a major enabler of success, and it can also be a major challenge for organizations that may not have the DNA to operate the way you'd expect a more digitally native organization to operate.

Shane Hastie: Going a little bit deeper, analyzing the video to look at moments to improve your tennis swing. What's the machine learning, what's happening there?

Raj de Datta: It's interesting. And, there's been some technology from Watson from IBM, in this area that the USCA partners with, but really if you think about a tennis match, there's a couple of critical moments and there's a lot of time spent in the middle. It's sort of walking between points, and changeovers, and the like. So, you're trying to, in a time compressed way, find the moments that matter. And interestingly, one of the ways by which the machine learning system operates is to analyze sound, because you either get a lot of applause, you get groans, you get these triggers that indicate what's happening in a point, you get pace that goes up between the ball gets hit a lot harder. You get crowd applause, and so detection of moments, it turns out sound is actually a really effective way of spotting where in the video there might be something to pay attention to, that's one example, is to trace the variances in the sound, and then associate that with video.

And then, there's the tagging aspect, because a lot of what players want to do is say, "Oh, well, I want to look at my serve, or I want to look at where I want to point in two shots or in seven shots." And so, there's all these cuts that are useful to players. And so, there's a lot of video analysis to tag different types of shots, so that you can look at your match more statistically and understand, did I lose more points when I hit it over there? Did I lose more points because of my own errors? Did I lose more points because I was on the run? What are the patterns that drive success or failure?

Shane Hastie: So, the AI is doing that tagging and the identification, and then presenting it, I'm assuming, to the player and the coach, or is the AI making the suggestions about, "Hey, this is how you should change."

AI as an assistant and guide [04:05]

Raj de Datta: Usually, it's more about the recommendations to try to identify the patterns, but coaches can then take it the last 20% of the way there, especially at this level, I would say, it's about efficient use of time and finding these patterns. And, "Hey, I always won whenever I got first serve in, and hit the second shot, I always lost when it went more than four rallies, four strokes longer." So, these patterns are hard for a human being to identify through a match, a machine can determine them really quickly. And then, you can talk about, from a coach to a player. All right, well, how do we make it more, the success category, and less the weakness category?

Shane Hastie: So, that's a good example of AI assisting and guiding, rather than taking over?

Raj de Datta: That's right. And, I think that, that's a great distinction because there's applications where the assist model, I think works a lot better, and applications where the fully automated model works a lot better. If you imagine a self-driving car, a self-driving car is going to have a case where, there's just a lot of sensors that are ingesting data and real time actions that need to be taken in order to turn, or to break, or to signal, or whatever it might be. A lot of that is better done in real time, if you think about the approval of a mortgage, where there are a lot of regulatory requirements around diversity, and costs of getting it wrong, it may be more beneficial for the system to detect fraud, to assess scores, to present data, and analysis, and let a human being then make sure that the practices that are in place are within the realms of regulation, goes at.

Shane Hastie: This starts to move us towards the book, because you talk in the book about the application of AI, and you have the three A's, which we will come to in a while, but the book is called The Digital Seeker. Why Seeker?

Understanding the digital seeker [05:58]

Raj de Datta: Well, I think it stems from the idea that we have been educated by people like Amazon, that the web is all about reducing friction, and all about how you can get online as a human being, look for what you're looking for really quickly, and have it show up in your doorstep. But, it turns out that, that's not how a lot of human interactions work. And, if we cast a critical eye on our digital lives, we find that our digital to-do list is exploding. We have all kinds of stuff to do, it's just that it's all on the computer or on the phone to get done digitally. So, why do we have so much extra work digitally than we ever had before? And, I think, the culprit is that most digital experiences make the assumption and that you the consumer, are going to do all the work to figure out exactly what you're looking for, and just show up and buy the thing, or show up and look for the experience.

And, that it turns out is not what makes the winners win digitally, it's really about delivering the kind of experience that goes to the underlying need, or motivation of the consumer, or the customer, and solves for that. Rather than, just makes the transaction a lot easier. So, to just make this very real, if I was out building a deck because I wanted to entertain some friends for a barbecue, then my core motivation, what I'm seeking is to have a place that I can entertain friends and family, and I'm building the deck for that purpose. By the time I show up online, I might be searching for plywood, but it's not the plywood that I'm seeking. It's the experience with my friends and family. So, the question is, do you build the website, the app, the digital experience to sell more plywood or to build a deck. And, I would argue that if you build the experience for what the human being is seeking, not what they're buying, that is actually what creates really distinguished experiences, because underneath every interaction, and every purchase is a deeper need or want that represents what I'm seeking.

Shane Hastie: So let's extend that build the deck analogy. How would my experience differ if the hardware store that I happen to land on knows that what I'm doing is trying to create that entertainment space versus I just want three sheets of plywood?

Optimizing for experience vs efficiency [08:05]

Raj de Datta: Let's just imagine two completely different experiences. If you were optimizing for selling plywood, you put a big search box, you have a huge catalog. You go in, and expect somebody to type exactly the very detailed size, and make, and the material for the plywood. And, they would have to specify all those things, and you just surface the product and say, "Buy, ship to home", and be done. That's the transactional experience.

The seeker-centric experience might start by saying, "Hey, what are you looking to do?" And, you'd in natural language say, "I'm looking to build a deck." And, from there, it would take you through a unique personalized journey just for you about building a deck for entertainment space. Maybe, ask you some questions or perhaps imply some of those answers from your previous browsing, even, and guide you to, "Well, here's the plywood you need. Here's some contractors that you should probably reach out to. Here's some furniture that might belong on that deck. Here's an easy way by which you could get the whole thing simulated in an AR oriented experience for my home", by the time I'm done with that second experience versus the first, the quality of its match to what I'm seeking is fundamentally different. In one case, I do all the hard work as a consumer. In the other case, you do all the hard work as an experience, and you make my life easier.

Shane Hastie: How does it know to ask those questions?

AI and analytics to understand the right questions to ask [09:20]

Raj de Datta: Well, that's a great question. And, that's where a lot of technology can play a critical role. There's the first piece of it, which is, even in building the thesis behind the experience, how do I understand what my customers are generally seeking in the first place? And, I would say that, historical market research techniques have been very much survey oriented, and focus group oriented. Modern market research techniques look at a lot of data. And then, they couple that with qualitative analysis, as well. But, they look at what are people purchasing? What was it made for? What's the frequency of those purchases? Is it mostly for this case or that case? The data gives us a lot of clues about the intent behind the purchases. And then, I start to apply judgment, focus groups, interviews, analysis, surveys, other things to reinforce that. So, I've got to have a theory first, but with that theory, aided by data, I can then start to envision what experiences feel like, but then I've got to test a lot too.

It's not like, I'm going to figure it out and provide the answer, it's that I'm going to iterate a fair amount. And, what I'm going to discover through that, as many have, is that actually there's distinct needs among my user base. And so, that's when personalization technology tends to come in, right? Because, you may be looking to build a deck of entertainment space. I may be simply looking for a place I can go have a cup of coffee in the morning, and we may both be looking for plywood. So then, it starts to take us down the road of personalized experience that might be unique for each of us, and using data and data science to assemble those personalized experiences, understanding intent, that all then gets into how do I deliver the experience? Because, even if I know what the experience should be, assembling is no easy task. So, the journey continues, but the stem of it is to be continuously trying to understand why somebody's buying my product and find every clue imaginable to serve that.

Shane Hastie: If I think about this from the technologist who's being tasked to, "Hey, we need this website, we need a platform to service our customers." Where do I start?

Raj de Datta: Let me say, before we get to the technologist, that I think one of the misconceptions about digital is that it's the job of just technologists, because indeed it's not, it's a team sport, and it turns out marketers, and finance people, and certainly technologists, and product managers, and all of these security folks, and legal folks, they're all important. We have had the misconception that digital equals technology. And, that's the thing that the techie guys go to. That's actually not how it works. The successful companies are going to put digital at the center of their business. And so therefore, they're building many businesses, and they're really ushering in these experiences. That's the first thing, but from a technical perspective, I think there's some amazing questions, and amazing areas of exploration that I'm sure we'll get into with three A's. But, really the place I start is first, in helping present the data that helps me identify the seeker.

So, that comes with great analytics, real understanding of being able to cut, and explore data in all kinds of different ways. It's a discovery process. I don't think it's the kind of thing where the answer just spits out. It's the type of thing where I look at it this way, and look at it that way, and look at it this way, and to do that, I need the kind of data platforms, and data in those platforms that enable me to first understand it. So, that's the first place I think, is assembly of the data in a way that helps me discover the secret. And then, I can go from there, and start to build the experience.

Shane Hastie: Where do I find that data?

Finding the right data to pull together in a manner that is useful [12:48]

Raj de Datta: There's actually much more data in most businesses than they think they have, to use some very practical examples from BloomReach, we power almost a thousand of the largest brands in the world. And, every click, every mouse over, every search, every event, every non-event is a data point. And then, when you throw in ambient devices, and sensor generated data, and purchase data, and historical data, and margin data, and product data, and revenue data, the level of data is not the problem, it's really in pulling it together in a manner that's consumable, and valuable.

Shane Hastie: I was going to ask that, because all of these data sets typically exist across the organization in dozens of different silos, in multiple different applications, and tools, and formats. And, some of it is structured, a lot of it is unstructured, some of it's a spreadsheet on the CFO's computer. How do we turn that into something that we can do this analysis?

Raj de Datta: One of the things that BloomReach does, my company, is we offer a customer data platform, and its job in life is to provide a real time inventing infrastructure for real time analysis of customer behavior, because we believe that's the key to seeker centricity. And, what it does is it's got integrations with all kinds of systems. It assembles that in real time, in an in memory oriented way, so that it can indeed process that data incredibly fast. And then, it visualizes that through some great tools. So, I think whether it's this type of system or other such systems, the need for a real time mechanism, by which to collect data from multiple sources, and then visualize it for analysis, and for action is mission critical to get started.

Shane Hastie: Let's explore those three As we've mentioned a couple of times, what are they?

The three a’s – Ambient Technology [14:38]

Raj de Datta: If we think about the journey of building that incredible experience that we talked about for the deck, it starts with understanding the seeker. But, I believe there's really three technology trends that make this all possible, and make it possible for Uber to build what they're building. I'll get you from point A to point B, or the deck experience, or the many experiences that are profiled in the book, that are very secret centric. And, the first is, I have to collect the data and that's where ambient devices come in, that's the first day. So, we used to live in a world where we just simply didn't have the data, but because of mobile devices to start with, but sensors and other IOT devices, there's just a lot of data out there to collect. So, ambient devices are like your arms and legs.

They sense what's going on in the environment around. And, they explode the number of such sensors that are possible to collect that data, which then gives us more interpolated data points about what the consumer actually doing. Because, I used to only know when they logged their run. Now, I know every heartbeat on their run, that is occurring.

The three a’s – Artificial Intelligence

The second piece of that is then to make sense of all of that, and that's where the AI and the machine learning comes in. It is to make sense of it, to identify patterns, to identify segments, to identify cohorts, to identify propensity, to identify affinities, there's a lot that BAI and BML does in the analysis phase, it also plays a key role in automation. So, we talked about personalization, when you go and look for a movie on Netflix, it's a real time recommendation that comes back from a system.

And like that, BloomReach does the same thing on a wide range of shopping websites and apps. And so, therefore, the AI also plays a role taking it all the way through the automated flow to figure out, okay, what product do I put in front of somebody? What creative do I put in front of somebody in this moment of truth? So, the AI and the machine learning is the brain piece in many ways.

The three a’s – Application Programming Interfaces

And then, you tend to find very quickly, that if you're trying to fully satisfy the needs of a seeker, then you probably can't build everything that's necessary to serve that need. Let's take Uber as an example, for Uber to deliver you a single ride from point A to point B, they needed mapping software, they needed payments to occur, they need to find a driver, they need to build a customer, they need to maintain an account.

So, we have lots of services, some of which are internal APIs and microservices, but a lot of which are external third party APIs, that they're calling in real time in order to deliver you a ride. And, that model is important because Uber could have said, I'm only in the business of just providing the ride. It's somebody else's problem to deal with the payment and the mapping, but then that transfers the burden to the consumer. So, the APIs, which is the third A, are really important because they provide the assembly of services, some built in-house, and some outside to then present an experience that's compelling to an interest without needing to build everything out there.

Shane Hastie: So, as a technologist, thinking about the InfoQ audience, this is probably where they're saying, "But well, yeah. Tell me what the API needs to do, and I'll do that for you." Is that enough for the technologist?

Raj de Datta: Well, look, I think there's technology problems in all three of these As. There's technology problems, certainly in sensors, and noise cancellation, and collecting the right data. There's technology problems, certainly in the modeling, and in the data platform around the AI and the ML layer, there's technology problems in the API layer, certainly to collect data from API, but to do it fast, to do it reliably, to do it with the right level of fairness, to do it with the right level of throughput, all of which is incredibly important in that process. And then, there's the front end technology piece of assembling that into an actual application that an end user can consume. So, I think there's interesting technology problems all through the journey.

Shane Hastie: This brings us to the concept of the digital experience platform. The DXP, I think is the acronym in the book, what is a DXP, and what does it mean for me, again, as a technologist?

Creating a Digital Experience Platform (DXP) [18:36]

Raj de Datta: I think to understand DXPs, we have to sort of know where we've come from, and where we've come from as a world, where the backend services, and the front end rendering were part of the same system. And, that traditionally has been true in most content management systems. If I'm building a website, Server 2010, what I would do is I would go get a content management platform, I'd dump my content in there. I might custom code certain components that might belong on the website, and then I would have the system serve my website, but here in 2021, the front ends are numerous. I've got Instagram plugins, I've got mobile devices, I have multiple websites, have multiple apps. And, that is decoupled from the backend services, the transactional systems, and services that need to assemble it. So, I need a decoupling of the front end from the backend, and a DXP is really a platform that I can build on to assemble that digital experience and publish it to any front end.

And so, we think of it as a collection of services, APIs, that are readily available for you as a developer to build the experience of choice, therefore it might contain a customer API, it might contain a search API, it might contain an authentication API, and on, and on, and on, all the building blocks that I might need to build any experience. And so, I would use those APIs to then build the kind of that I would want. And then, I would push that into a wide range of client side front ends typically, that would then render that experience. And so, the DXP is a platform that I can use to build really compelling experiences, and not just build them, but test them and optimize them over time.

Shane Hastie: What can go wrong? You have a whole chapter about disaster.

Preparing for what can go wrong [20:14]

Raj de Datta: A lot, just as the opportunity has gone up, the risks have gone up, and the stakes have gotten higher. And so, there is a chapter about all the many things that one needs to watch out for, raging from the fact that security, which was never a consideration, and I think to the degree that it is today, and probably will increasingly be so, has to now be thought of from the ground up, to reliability, which we're dealing with a level of reliability, an expectation that's higher than ever before, to performance, and really making sure that this all happens fast before somebody's attention shifts to another website, or another app, or another experience, to considerations like diversity, which we have to be cognizant of, that the more we leave to the machine, the more our historical biases get codified into the machine. And so, how do we account for that in the kind of experiences that we present? So, there's a wide range of gotchas, that we used to think of, of as the last 10%. And, I think we now need to think of as central to the way we design things.

Shane Hastie: Two of the gotchas that stood out to me when looking at them in the book, and through our conversations today, the first one privacy, with that ambient environment and the so much data available, as you say, every pulse, the heartbeat in my run, do I really want to share that with the sports goods manufacturer?

Issues around privacy [21:36]

Raj de Datta: My view on data privacy is, there's a regulatory dimension to data privacy. And, we are seeing with CCPA in California, and GDPR in Europe, that there's some things that you just have to do in 2021 that you may not have had to do before. And, there are systems to make that possible. But, the real question is, what's the spirit behind data privacy? Which, is the question you're asking in many ways. And, my view is that consumers are not as concerned about sharing their data as we make it out to be, they're most concerned about how that data is used. And so, we over index, over rotate on worrying about sharing and access, and under rotate on thinking about use. And, to just use an example, if we think about why there's an uproar over Facebook, there's lots of reasons. But, one of the reasons is that when I signed up for Facebook, I expected it to be a place I could keep up with my friends.

I did not expect it to be a place that advertisers could use to manipulate my behavior, and target me in all kinds of different ways. So, the motivation for why I engaged with the platform, and the use of that data are at odds. Now, if I sign up for Spotify, and I would say, Spotify knows a lot about me, as does Apple, but let's use Spotify as an example, Spotify knows a lot about the music I listen to. And, nobody seems to worry about the fact that they use that data, listening to what I'm listening to, to then say, this is a song or a genre we recommend for you.

Why is that? Because, the use and the benefit, and the reason I signed up for the service are symbiotic. Both are about listening to music, they're not at odds. So, the interesting thing is, in both cases, you could say, "Well, personal data's collected in both of those cases", but I think we're going to go to a world where it's most important to be careful about why you're using the data, and how you're using the data. Because, if used productively, it's actually a huge benefit to the consumer. If used unproductively, than you run into trouble.

Shane Hastie: There's a trust equation in there. And, one of the things we've seen is the trust in technology and in technologists has eroded substantively.

Building back trust in technology and technologists[23:43]

Raj de Datta: I agree. I agree. And, I think the trust has eroded. And, it's for reasons like this, which is we've made the assumption that human beings... We can do anything with what they give us, their job to give us the oil, it's our job to use the oil, to do all kinds of interesting things. That's not, I think the way we should be looking at things, I think we should be looking at things as, if they give us fresh oil and we're going to burn some fossil fuel in the process. It better be in a highly productive way to benefit that.

Shane Hastie: Delving deeper into this one. How do we win back that trust? So, you've started on that. What does the technology industry need to do?

Raj de Datta: I mean, I'll make some controversial statements here that many will not agree with, but the first one in my mind, I mean, it does have to come to some degree of regulation. I don't think self regulation's going to really work. The second thing is, I think that regulation has to separate advertising as a business model from platforms that collect data. And, what I mean by that is, if you were in the business of targeted advertising, and you were in the business of collecting personal information, there needs to be a Chinese wall between those two enterprises, because otherwise, fundamentally there's a misset set of incentives.

If the way I make money is for advertisers to target more and more deeply, then my incentive structure is always to collect more and more deep information, and pass it on to them. So, the only way I think we can break a lot of the trust issues is to create different incentive schemes, as an advertiser with permission, you can go advertise to people, but you can't be using an app or a website to collect personal data for ostensibly purpose A, to then sell it to advertisers who are going to use it for purpose B. That is the root of a lot of trust issues, I think, on the web, at least with a larger internet properties that then cascades down to the rest of the technology industry.

Shane Hastie: Another one of the elements that tweaked my attention was explainability in the AI space. This is one that's certainly, becoming more prevalent today. How do we achieve that?

The need for explainability in AI systems [25:41]

Raj de Datta: Well, I think the good news is, there's a lot of new technology in the area of explainability, and there's startups that are trying to solve this problem. And, it should be solvable, right? Any model that can be built, one should be able to build an equivalent explainability system to explain how the model comes to its conclusions. And, that's harder and harder with deep learning, and neural networks, but it is possible. And, I think it is mandatory in cases where we have to sanity check that things don't go haywire. So, explainability is key, especially in high stakes choices. And so, we talked about the mortgage example. We might talk about denying health coverage, we might talk about assessing disease through looking at x-rays. You've got to have explainability in those cases, the consequences are too high, so that's one case. The other case is, the cases where they touch our everyday lives.

And, this is the stuff that we may not think is a big deal, but there's an example in the book around LinkedIn, where I might go in and say, "I want to go look for an engineer in my network", and find that everybody looks like me, and lives near me, and thinks like me, because that's how LinkedIn has come up with this recommendation algorithm, which is by definition anti-diversity. So, explainability is important for us to even understand that's a problem, because in many cases, people may not even be aware that such a problem exists.

Shane Hastie: Again, thinking from the technologist perspective, how do I ensure that when building these seeker centric solutions, I take that explainability and make it core?

Raj de Datta: Yeah, I think you do it at design time. You instrument, a lot, you output a lot of tests of the outputs of your model under different circumstances, so that you can assess the factors that are driving its outcome. You don't just go optimize for the outcome. You optimize for an understanding of how the outcome is going to come to be in the design process. And, you iterate until you can have some understanding of what it is. Now, you might need to build an equivalent explainability model in order to have a better understanding of what you've built on the optimization side, or on recommendation side, or the like.

Shane Hastie: And, who do we expose that to?

Raj de Datta: I think we expose that, starting with developers to ourselves, but we also expose that to all the consumers of that model, which could be an application, but could be a business user, but we have to make that visible because otherwise we're not equipping them to make good decisions.

Shane Hastie: You have a chapter in book, where you talk about what's happened over the last 18 months, the acceleration as a result of COVID, what is the bottom line of that?

The rapid increase of digital transformation because of COVID-19 [28:12]

Raj de Datta: I think the bottom line is if digital was changing our lives at a certain rate before, and as I say, the R0, or the rate of change has just accelerated tremendously. And so, the urgency, the embeddedness of digital, and everything we're doing, whatever would've happened in five years has now happened in one. And, I don't think we're going back. So, that's the core takeaway from the pandemic? I think the interesting question, which we explore a bit in the book is what will that mean? So, okay, great. Digital has been accelerated. We all agree with that. What will that mean for work? What will that mean for the technology industry? What will that mean for businesses outside of technology? What will that mean for human happiness? I think those are the interesting questions that are the next level questions from the implications of the pandemic.

Shane Hastie: What are your predictions?

Raj de Datta: I believe in the twin happiness boom and economic boom thesis. I think that what we know about economic growth is that the number one factor that drives economic growth is productivity. The more productive human beings are, the more we grow, because we do a lot more with the extra hours that we have, and digital is the ultimate productivity turbo charger. So, I think that through this acceleration, we are seeing every industry increase its productivity tremendously. And, the result of that, I think is a lot more economic growth, which means more jobs, which means more man for trained people in these areas, and which hopefully means healthier economies in the longer term. So, I think it's going to be an instigator of growth, that's the first thing. The second thing is, I think it's going to change the way we work and live in a more profound way than it ever has before.

Raj de Datta: We still, if we took the average white collar worker and we should certainly talk about blue collar as well, but from a white collar perspective, we had this antiquated notion, largely of, we go to work, we work with our colleagues. They are generally physically proximate. We finish work at five, and we come home at night, and then we live our personal life. And, we've already seen that start to change with mobile devices and the like, but now it's gone. And so really, I think if we use it right, we can do a lot less random travel. We can do a lot less random commuting, we can do a lot less car breaks down on the way, a lot less driving through the storms, and a lot more productive work via video.

And then, episodic physical getting together, which I think is absolutely necessary for human connection, but not with the frequency that we did it before. And so, we will still get together, but then we will be highly productive in Zoom and Slack, and everywhere else in the intervening periods of time. And, that will open up more time for us to hopefully be fitter, and spend time with family, and deal with mental health, and go to the neighborhood bar. So, I see the economic benefits, and I also see the happiness benefits that could come from all of this.

Shane Hastie: Taking the cynics view. How do we make sure that the benefits are evenly spread?

How do we ensure the benefits are evenly spread? [31:09]

Raj de Datta: I think that's the key question, because anything that is an accelerator is also a concentrator, and that's what we're seeing in wealth and opportunity, in everything else. And, we saw that firsthand, the story of the pandemic for the people that kept us alive, the food service workers, the nurses, some of the teachers that were teaching in schools, and delivery people, and restaurant workers, and the like, they did not have the experience I just described.

They had exactly the opposite experience, and hopefully it's brought some awareness to that, but it also gives us the opportunity to make sure we harness these technologies, and this opportunity to transform the great upward mobility drivers, which tend to be things like education, and healthcare, and things of that type. And, there are great opportunities to do that, by democratizing online education, by democratizing telemedicine, by really making opportunity much more accessible in ways that were never before. So, the hope would be that digital, isn't just a turbo charger for the people that already had digital before, but rather it opens up a set of possibilities that were not possible, because they were not economic choices previously, to now democratize that to uplift the lives of a broader swath of people.

Shane Hastie: So, Raj, if people want to continue the conversation, where do they find you and where do they find the book?

Raj de Datta: Yeah. So, you can find the book, The Digital Seeker on Amazon. You can find it on any other book store, Barnes and Noble and the like, you can find me on LinkedIn, Raj de Datta, and you'll find me there, or on Twitter @rdedatta. And, you can also find my company BloomReach, which plays a key role in driving software, and has some of the best engineers on the planet working to apply these techniques in real life. You can find us on B-L-O-O-M-R-E-A-C-H.

Shane Hastie: Thank you so much.

Raj de Datta: Thank you.


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