The Future of Data with Ray Wang from Constellation Research - Data Forward Podcast
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Ramon Chen:I am super excited today because I have Ray Wang on the podcast today. And Ray, if you don't know him, is an industry leader. He's the CEO and founder of Constellation Research, an amazing guy. Ray and I have known each other for 20 years. Can you believe it?
Ramon Chen:It's been a long, long time. He's also an author. His book that was published 3 years ago, Everybody Wants to Rule the World, apart from being a great eighties tears for fears song, has all of the sort of things that we wanna talk about today on today's podcast, which is, the future of data. Welcome, Ray.
Ray Wang:Hey, Ramon. Thanks a lot for having me. So
Ramon Chen:Excellent. Excellent. So, Ray, you know, when you wrote this book 3 years ago, I I reread sort of the book, and and it it was funny because you described your sudden epiphany about how these digital giants were, taking over the world because of how they behave, their their ecosystems, and their platforms. And it's really played out over the last 3 years, even more so than you may have even have imagined. How are you seeing these things today?
Ray Wang:Well, that was both a market thesis and an investment thesis. Part of the reason that was put out there is because we thought in a digital world, we're only gonna see duopolies merge, and the top player was probably gonna take maybe 50, 60% of the market. The next player might take 25, and then after that, it was just a bunch of folks in in the scraps. And and that's really happened because these companies really knew how to master data. They really understood how data was going to be a competitive advantage and they built networks to support that.
Ray Wang:And those networks were not like a 100,000,000 people, there are 1,000,000,000 of people that are constantly creating new data, utilizing these new data, and and really building that entire life cycle that allows them to, create new products, to identify risks, to handle pricing, to understand logistics. It's all happening and it's happening at rate even more quickly. Now that was the Internet age, and we have to remember that was the Internet age. And all these derivatives, whether it was IoT or 5 g or anything else, but now we've had the demarcation of about a year as we entered the age of AI. And I'm gonna be clear.
Ray Wang:Right? I'm gonna be controversial intentionally. There was no 4th industrial revolution, World Economic Forum people. It didn't happen. Right?
Ray Wang:It was just vestiges of the digital age, but the AI age is real, and that AI age actually is different. The Internet age was decentralized. It was open. It was cheaper. There are a lot of players.
Ray Wang:This AI age by design, unfortunately, by design is closed. It's centralized. It's more expensive, and only a few are going to win. And it's going to be up to us to change that because if we don't democratize the data, we will be lost in the dark ages of the Internet for a long time.
Ramon Chen:Wow. That that that's really sort of prescient and and somewhat scary, but huge opportunities, right, are to be found in this age of data. You talked about, as I mentioned earlier, in the book about data mastery. Right? Being able to monetize that data, being able to use it, but it's all about the quality of the data as well.
Ramon Chen:Right? Right? The trust in the data. How are you seeing sort of companies, corporations trying to improve their data quality? It's been a never ending thing.
Ray Wang:Every organization isn't very good at it. Right? We know that, and that's just been the history of the way data's worked, for a lot of organizations. Right? You and I started in the world of master data, maybe even data warehousing.
Ray Wang:Right? That's way, way back there. Right. But the goal of every organization is still the same. How do we access all the information inside organizations?
Ray Wang:How do we break down functional systems? How do we take those silos of data and actually do some good with it? And the good news is the technology has advanced that we can actually point to the data, look to the data. We can take any data from any source. It could be in the cloud, it could be on premises, it could sit remotely at the edges, and and we can put them into time series.
Ray Wang:We can actually apply that information and insight to get better context. And and that's where we are at the moment. Every organization wants to take their data, untrap it, bring it to life. They wanna apply it to a business process so that they can take action on it, and they wanna serve up something, whether it's an experience, a physical or digital experience. That's where we are, and and the good news is the tools are better.
Ray Wang:We're working much more quickly to do that, and the companies that have a data strategy are actually winning. Yeah. Let me give you an example. We looked at the Uber earnings from the last quarter and it was very apparent that the company had a strong data strategy. They can do dynamic pricing.
Ray Wang:Okay, great. But here's the piece that was interesting, they have optimized ride batching and ride batching means you can put, like, you know, you can share rides, you can pull people together into the same car. And when you have ride batching working, that means you have optimized supply chains, which means every driver can make more per ride and you can actually have more trips per driver. Yeah. That's a company that gets data strategy.
Ramon Chen:That's that's incredible. So the data itself drives the applications in the business, but people are taking this sort of almost ambient data and monetizing it in different ways. Right? Sort of mobile telco companies are taking sort of location data and trying to sort of personalize advertising. How do you see that playing out, especially with increased regulatory requirements around what you're allowed to do with personalized data and so on and so forth.
Ramon Chen:How do you see that shaping up?
Ray Wang:You know, the ambient experiences that we talk about, which is, you know, the stuff that happens in the background, like a suggestion or a nudge or some context or an experience that just happens naturally are really built on give get models. Right? You basically are doing a to x testing. Right? You react one way, system understands that, hopes to actually provide the same experience in the future based on the context, and continues to build memory over that based on the number of transactions.
Ray Wang:We're gonna see that level of mass personalization at scale continue to evolve, and I think that's that's an important piece. But the regulatory piece that you're talking about is a little bit different. I have figured out to bypass all this regulatory. I'm not worried about cookie apocalypse. I'm not worried about how your data is used.
Ray Wang:Here's why. On the consumer level, you will give it up. You will give it up every time. You will trade privacy for convenience. You'll trade privacy for value.
Ray Wang:You'll trade privacy for like a free offer, you'll trade privacy for anything and I'm gonna count on that, because I'm gonna offer you a loyalty program where you're gonna sign away all your rights. And that give get on data means you get benefits, you get better personalization, you actually will have positional levels of value you haven't seen, and most people will probably choose that option. Now as a privacy freak, I won't, but a lot of people will. And and that's where the context comes into place. People are willing to pay for that, and and they will pay for with data.
Ray Wang:They'll pay actually money to do that. They'll pay by trading information and insights. And so we actually think the future is going to be a series of data collectors. And let me explain why. And this is coming from the new book, I'll never write, that I really need to write.
Ray Wang:So we get to LLMs and we're 85% accurate. And 85% accurate in CX is that's not bad. It's pretty exciting. Right? Yeah.
Ray Wang:Eighty 5 percent accuracy in supply chain? Oh my god. You can't do that. 85 percent accuracy means you're losing 1,000,000 of dollars per minute. 85 percent accuracy in finance?
Ray Wang:Someone's going to jail. 85% accuracy in health care? That's not happening.
Ramon Chen:Not gonna happen. Yeah.
Ray Wang:So so so we are going to partner for data across industry value chains. Retail, manufacturing, and distribution are gonna come together, and they'll show pricing best signals. They're gonna understand demand. They're gonna understand, like, logistics. They're gonna understand weather.
Ray Wang:They'll put all that together, and they're gonna share that information. So the next 10% of data is just as important, the first 85%, and the last 5% is gonna be SUPERVALU as we get to precision decisions. Because if we can't trust the data, we can't trust the AI. If we can't trust the data, we can't trust the decision. And you know what?
Ray Wang:All this AI talk is a waste of time. This is about decision automation, decision intelligence, future.
Ramon Chen:Absolutely. I mean, so so do we see that because sort of I've evolved from working in master data management now to this concept called data observability, which kind of gives you a lens on how data is flowing through the system, whether it's a high quality or not. But the other thing, Ray, that people often sort of tend to forget about is the cost of producing high quality data. Right? It's not a cheap proposition to be able to maintain the quality of this data.
Ramon Chen:How do you people think now more about the data supply chain and the cost it takes to manufacture and maintain that data quality?
Ray Wang:Well, they're treating it with their life. Right? They haven't done that before. They actually were giving away for free. They didn't even put a value to their data.
Ray Wang:They didn't even worry about where the data was headed, right? And for a lot of them, they didn't think about why that data had a half life and why was that gonna be important if they had the wrong information and insight there. As we get to higher and higher levels of precision, you're gonna need that quality. You're gonna need to understand what that lineage is. You're gonna understand, you know, how this all comes back to reliable information and insight, and data observability is gonna play a big role.
Ray Wang:Right? So is this different than testing? Yes. Is it different than monitoring? Yes.
Ray Wang:Is it different than qualiabilit? It's flat. Yes. Is it different than reliability? Yes.
Ray Wang:It's it's a whole brand new field that people are actually jumping into.
Ramon Chen:Yeah. We're seeing actually that this is the second most requested inquiry in in in in in people's minds beyond AI. But let's talk about AI for a second because, you know, would you have ever imagined we'd be here so fast once the chat GPT was released and how we were talk we'd be talking about sort of responsible AI and sort of all of this. I mean, did when you wrote the book, I mean, this hadn't even started yet. If you were to fast forward or look back actually on on what you were thinking at the time, is this something that you could have ever have imagined?
Ray Wang:We definitely imagined. We thought it was gonna happen 5 to 7 years from now.
Ramon Chen:Yeah.
Ray Wang:Not now. And the fact that the advancements were made but keep in mind, AI has been around for a long time. Generative AI is what captured everyone's imagination. Yeah. Our challenge is different.
Ray Wang:If you want 1 plus 1 to equal to 99.7% of the time, then do Gen AI. Yeah. But I want 1 plus 1 to equal to all the time. Oh, that's right. Right?
Ray Wang:And so we have ML and other forms that are there. But but the point being is generative AI captured imagination. It's made language very important, and it's really given us the ability to think about multimodal ways to actually, you know, replace AI and make AI the new UX for almost every interface. And that that changes that's a big paradigm there. And then the second paradigm around that is the fact that we've gone from moving to augmentation to acceleration to automation very quickly, and now we're talking about agents.
Ray Wang:And in the future, it's gonna be more about advisors. And so we are very advanced in what we are expecting of AI, and we are very, very much behind in terms of what we actually deliver. And so this is gonna be the gap that everyone's gonna be trying to overcome for the next 3 to 5 years.
Ramon Chen:Yeah. I mean, you you are cat you have the catbird seat. I see you on CNBC, Bloomberg all the time talking about, you know, NVIDIA's earnings and Tesla and and, like, you know, we're always rubbing shoulders with the likes of Benioff and Satyam. And and, you know, what do you think who's making the strategic moves in your mind? Right?
Ramon Chen:And and, of course, you know, everybody's doing the right things, but, you know, does Salesforce have the leg up with Agent Force? The does Microsoft with their copilot? Will that evolve? What what's your perspective? I'm sure everybody here who's listening to the podcast is dying to know.
Ray Wang:So I'm not as excited about the hardware and the GPU providers because I think that game is pretty much down to about 2 or 3 players.
Ramon Chen:Mhmm.
Ray Wang:I'm not excited about what the hyperscalers or cloud vendors are gonna do because I think it's not there. Mhmm. I am excited where it's gonna happen in software, but what I'm most excited about is companies that are using AI. Mhmm. Right?
Ray Wang:Uber was one example. Airbus is another one. They've been using some amazing things to take flight information and data to calculate revenue per mile, revenue per kilometer, or cost per per kilometer. And that used to be something Boeing would use as their competitive advantage in contract negotiations. What they did was they democratized it and gave it away.
Ray Wang:Every every person who's flying, every plane that's flying is now providing data back into the model and sharing it. And it's a great example, these data collectives that I'm talking about, because those data collectives built around industry value chains are gonna be important because that's how we share information to get to precision decisions. The second one is actually interesting to me. We were interviewing we had half the world's chief AI officers in one place in, New York about 4 or 5 weeks ago. And what was interesting about that conversation is we had the CEO of Sam's Club, which is a Walmart company, come in and tell us they got rid of a 100,000,000 tasks.
Ray Wang:And the next thing you were expecting was not this, and then they hired 3% more workers on the frontline so they can improve human engagement. Wow. Right? That is where the excitement is, companies that are using AI. And this will probably be the first intro chapter for the book I'm not writing, which is, you know, in the twenties.
Ray Wang:Right? And I know someone might have used this example as well, but in the twenties, there was this thing called artificial refrigeration. And that's great. Right? They were gonna take over the role, change the future of work, improve cold supply chains, improve health and safety.
Ray Wang:Right? And the winners were Kelvinator and GE and Refrigidaire. But the real winner when you look at stock earnings was Coca Cola because they figured out how to use cold supply chains to actually their advantage. And that was the innovation. It wasn't the refrigeration or refrigerated news or or artificial, you know, artificial, you know, refrigeration.
Ray Wang:It's was really the fact that someone figured out how to build it into their business model. And that's where the next set of winners are going to be is organizations that build what we call data inc businesses. And there's 5 types of them, but we'll talk about those in another time.
Ramon Chen:Yeah. I can't wait. And, you know, I'm really, really excited to think about it. I actually have your first book here, the original. Right?
Ramon Chen:Disrupting Digital Businesses, and that was a game changer. And then, everybody wants to rule the world was also an amazing read. Just to close off, Ray, I I really wanna thank you again for coming on the show. It's it's really a pleasure and an honor. I've known you for many, many years.
Ramon Chen:You're you're an incredible, sort of mind in terms of your, businesses that you've started and and continuing to share your information with all of us. And I also wanna congratulate you. I believe you're running for Cupertino City Council. Congratulations there. Doing good as usual.
Ramon Chen:Really, really appreciate that.
Ray Wang:Driven approach is all we're gonna say.
Ramon Chen:Oh, okay. Well, you definitely, you've only got my imagine. My vote and my contribution, and I've just been plugging you on LinkedIn just before we got on here, for that. So, Ray, a pleasure. And, you know, we've come full circle.
Ramon Chen:I remember 8 years ago, I was on, Distruqtv, when you interviewed me with Val, and now I get to do it with you. And and I really hope you'll come on again sometime soon, promoting your new book perhaps.
Ray Wang:Well, hey. Thank you very much. And, yeah, we'll do that again. And thanks for having me. So good luck with the podcast.
Ramon Chen:Thank you so much, Ray. Thank you. Take care.
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