CONTRIBUTOR
General Manager and Editorial Director,
Techstrong Group

Synopsis

In this Digital CxO Leadership video, Mike Vizard interviews Richard Watson, regents professor and J. Rex Fugua distinguished chair for internet strategy at the Terry College of Business at the University of Georgia, about why so many digital business transformation initiatives are stalling. He is also the author of “Capital, Systems, and Objects: The Foundation and Future of Organizations.”

 

Transcript

Mike Vizard: Hello and welcome to the latest edition of the Digital CxO Videocast. I’m your host, Mike Vizard. Today we’re with Rick Watson, who is a professor at the University of Georgia, and author of a book called, “Capital Systems and Objects.” Rick, welcome to the show.

Rick Watson: Thank you.

Mike Vizard: We hear a lot about digital business transformation. But you know, I think both of us have been around the block once or twice. What is your sense about, what are we seeing today with this whole transition to digital business, and how does it relate to the fundamental economics that you’ve been talking about for years?

Rick Watson: Yeah, and you know, this topic of digital transformation is the thing that got me started to write this book. I heard people say you need a blockchain strategy. You need you know, an AI strategy and so forth. And I thought, we can’t have people jumping here and there to the latest technology for a strategy. What fundamentally do you need to understand?

And I recognize that every organization is in the business of capital creation. So as you can see on the background here, I’m at a university. We’re in the business of creating human capital. Most of the organizations that you and I deal with are in the business of creating economic capital. I bet there’s someone like Rotary who’s in the business of creating social capital. So I started with the idea that every business is about creating capital.

And they need a recipe for capital creation that involves the six forms of capital that I identify in the book. So instead of focusing on digital transformation, I say to companies, first of all, work out what your capital creation model is. And then look at which of those conversions, you know how you convert human capital into economic capital, for example, would benefit from digitization.

Because the ultimate goal is to be the winner in the capital productivity guide. And if you are the most productive in your use of capital in retailing, then you will succeed. And we saw that’s what happened when Amazon came. It destroyed the traditional retailers because it used capital more productively. So that’s how I approach the digital transformation.

Mike Vizard: Do you think that we are chasing too many bright shiny objects, and we’re not sitting back having a strategy? Or do you think that there are cooler heads at the top of the C-level executive team who’ve seen these things before, and they’re kind of looking at it much more strategically?

Rick Watson: I think we get sold on the hype. There’s some data that shows seven out of eight digital transformations fail. So that’s not a really good story, is it, right? We must be doing something wrong. And I think we put technology first, and not capital productivity first. We focus on means and not on ends. And you must always keep the ultimate goal in mind and then work backwards as to how you’re going to achieve that goal, or how do you perform that goal better than your competitors.

Mike Vizard: Do you think that the way the C-suite interacts with each other needs to change? It seems like we still have a lot of these stovepipes where somebody runs marketing, there’s somebody running IT, there’s somebody running sales. And do we need a more efficient way of thinking about how the organization as a whole is structured to make it easier to maximize those capital investments?

Rick Watson: That’s a good point. Those siloes have worried us for a long time. You know, the second part of my book is about systems. And I identify five fundamental types of systems. And one of them is a system of framing. And that’s, how do you define your value proposition to your customers? And then how do you carry that through the organization.

And I think the way to get some sort of consistency and break down siloes is to have everybody clearly understand what the frame is for this organization. You know, if you look at Wal-Mart, for years they talked about everyday low prices. Well, that’s a message to the people in logistics, that you have to have a world class logistics system.

It’s a message for the people in marketing, how you need to think about pricing and so forth. And I have a wonderful example of this, is American Express. You might remember it’s the card that you take with you. The card that always works for you, all right? Don’t leave home without it. And I was traveling in Greece once, and my wallet was stolen between the airport and the city on a crowded train. So I went to my hotel room, contacted American Express, and said I’ve lost my card. It’s been stolen.

And they said yes, we’ll issue a new card for you. It will be with you when you get home. I’d just started wearing a smart watch, you know, the Apple Watch, like this. I had my AmEx card on it. Within five minutes, I had a new AmEx card on it. Well, you know, if you’re marketing the perfect card as AmEx described it, then the perfect card is one where you don’t do anything. When it’s stolen, the new one appears on your phone immediately. So I think if companies are really clear about how they frame what they deliver for customers, and everybody keeps that in mine in the C suite, I think it helps the alignment.

But so often when I go and look at websites for companies, there’s no indication of how they frame themselves. You know, what they do for you. And people go off in different directions, because they all assume something else, other than having the board agree clearly and communicating clearly what the purpose of the organization is.

Mike Vizard: Do you think that the C-level executives outside of IT need to have a deeper appreciation for the technology itself, or do you think that they’re seeing that in their everyday lives, they already have that appreciation and now they’re just trying to drive that through more from a business perspective? But they are more tech savvy than they were just a few years ago.

Rick Watson: I think they’re tech savvy enough. I think they understand that and they see the opportunities. I think business people are pretty innovative. What I think they need to be more clear about is how do the pieces fit together. What’s the recipe for creating capital in this organization? And you know, what do I have to do to make that successful? So put the technology aside. Get the recipe sorted out. And recipes make a huge difference.

I’ll give you another example. I think it’s important to have examples to illustrate points. So Ford Motor Company, when it was established, quickly took market share. At 60% market share, it was a pretty simple model. Build a big factory. Build one color car, you know, one model car. Hire people off the streets. And away you go. When Sloan took over GM, he had a different recipe. He was about strategic planning.

And he recognized that they could have five different models, each designed for a different price and purpose. And that the key to success was to have a strategic planning team which I would call organizational capital. That every year laid out the plans of the next year. What cars they were going to make, how they were going to price them, what colors they would have and what features they would have in that, so forth.

Within 18 months, General Motors had become the market leader in the U.S. car industry, and Ford never caught up. They had a better recipe. Now if you look at Tesla, Tesla’s got a wonderful recipe. It’s not only electric vehicles, but it’s showrooms in malls, rather than these huge dealer lots where you’ve got lots of capital tied up. It’s the connected car where you’re continually learning about the car. It’s the ability to send a fix to a car, or send an update to a car.

It’s a different recipe for creating capital that’s more productive than anybody else. And this is the huge market valuation that Tesla gets as a result. People see that this is a better way to run the car industry. So now everybody’s scrambling to catch up. So it’s essential I think that everyone in the C-suite has a very clear understanding of how we operate, what our recipe is, and how the various elements of capital fit in to making it a successful business.

Interviewer: For years we talked about the divide between IT and the rest of the business. A lot of IT guys back in the day could have been working for any company. They didn’t really affiliate themselves with a particular industry. Do you think that’s changing as well? Is that divide getting narrower, or are they as far apart as always?

Rick Watson: I still hear a lot of people putting IT first. In fact, I don’t even like the term IT. I think you buy technology. Everybody can buy the same technology. You build information systems, and they make the difference, right? If you go back to the Wal-Mart model, they purchased IBM equipment. Well, Kmart could have got exactly the same equipment. Sears could have got exactly the same equipment. And IBM would have fallen over to sell it to them.

But Walmart created a world class logistics system, which fitted with their everyday low prices framing. And that made them or contributed to their success. So, the focus should be on what are the systems that we need in order to be successful? Then work back and buy the technology. So, yeah. I think we lose focus, all right? And for some reason we get fascinated by means and not captured by ends.

Mike Vizard: Do you think organizations should think more about resiliency as a whole? A lot of people were talking about COVID as this massive disruption and of course we now have the situation in the Ukraine, and a lot of folks will go well, those are black swan events. You couldn’t plan for those things. But there’s other folks who say you should expect the unexpected. So is there some sort of way to think about resiliency that should be different post-COVID, post-these events where people are saying let’s build a business that can absorb these black swan events and not be so easily disrupted?

Rick Watson: Yeah, I think resiliency is becoming a more important topic. The global scientists who build these mathematical marvels of climate change have predicted for some years that we would have more severe weather. We have rising sea levels. When it rains, it pours, right? And we’ve got these wildfires. So there are more adverse effects, and there’s the data to support this.

So resiliency happens, is required more often. That means building some slack and redundancy into the organization. Which sort of upsets some people, because it’s seen as well, we’re wasting money now. But you’re going to save the money in the future when you can recover rapidly and actually help out in recovering from some disaster. So I think it’s an important problem. I think it fits with the chief operating officer to think of resiliency as part of their portfolio of activities.

And it’s something that technology can help with. You can build systems that will help you recover, and that’s part of the resiliency. But it’s, you need slack. You need redundancy. You need spare resources and spare capital in order to be resilient. There’s no way out of it, right? There’s not efficient resiliency.

Mike Vizard: We also hear a lot about artificial intelligence these days. It may be the ultimate buzzword of the moment. Of course what AI is and is not is debatable. But my question to you is, do you think we’re expecting too much of AI? I mean, at the end of the day it’s another piece of technology, and some might argue it’s not even new.

Rick Watson: You know, AI’s been around for about 50 years I think. I’m just writing a paper now because I’m working on a system. And we’re arguing against the people who say machine learning, we’re saying humans should code it. We are wonderful at painting visions of a future that’s much further away than we think it is. The AI people thought they would have it within one generation, that they would have general purpose AI. We’d have AI smart as a human. It’s taken much longer.

If you look at this, we were promised machine learning would diagnose people five or six years ago. It hasn’t happened yet. That was just around the corner. Autonomous cars are still coming, all right? We haven’t been able to get there. Machine learning is clearly here. It has some important applications in areas that are fairly well-defined. And fairly static. So that if you can get a good data set that describes your environment and captures the patents in their environment, and that environment doesn’t change much. Then that’s a good opportunity for machine learning.

But the big problem I see is that a lot of companies have very low quality data. And it’s just not good enough to build anything with, you know. So for example, we do student projects. I am the supervisor for several of our capstone projects for our master of science and distance analytics. And we deal with Fortune 100 companies. I just got off a call with a major company where we’re still struggling to clean up the data, and we’ve got three weeks left before the students have to deliver the results of the data analytics.

You know, we’ve got to spend 90% of our time cleaning up data. And that’s not unusual, you know. Another major company. We spent months cleaning up the data. Then we found out there were major problems in it, even when they gave us what they thought was clean data. So yeah, a lot of promise, but you need to get your data clean before you start trying to spend money on machine learning. Another data point for you, proof of concept of artificial intelligence projects. About 90% of the projects stop at proof of concept.

People get to a certain point, then realize the data’s not clean enough. It’s going to cost too much. We can’t get the outcomes we want and so forth. So, there are many people trying to sell you on machine learning. There’s a lot of money to be made from sales. And I think we need another group that says lets be realistic. Look at the record. Look at the failure of IBM to turn Watson into a huge money spinner.

You remember, Watson won Jeopardy. And then they had this separate Watson health which, being Rick Watson, I thought Watson was a great name for a computer system. But they’ve spent billions. And they’ve abandoned the attempt to capture all of the knowledge in medical journals and convert it into something that doctors can use. Human coders are still much better than machines at recognizing knowledge.

Mike Vizard: All right, so what’s your best advice to business and IT leaders out there? What’s the one thing you wish they would all focus more on?

Rick Watson: Well, if they’re interested in machine learning, get your data clean. And that means moving away from spreadsheets. And moving to databases as a way you manage and share data in the organization. I’m still surprised to see organizations making important decisions based on spreadsheets. Spreadsheets are prone to errors.

So you might remember the Enron disaster. All of the e-mail was captured as part of the court case, and is publicly available. Someone went through and looked at all of the spreadsheets attached to e-mails, analyzed them and found 25% of them had errors. Some of them extremely major errors. So, we’re still too fascinated by spreadsheets. And they sit on someone’s desk. There’s no auditing. There’s limited security.

If we can clean up all of those and get them into databases, we lay the foundation for machine learning that, getting a spreadsheet from here and there, which we, happened in one case in their student projects, the matching product categories or product types didn’t match in 10% of the cases. So one of the two spreadsheets was wrong. Maybe both.

Mike Vizard: Well, I think a lot of organizations probably don’t want to know what’s going on in the spreadsheets, but I agree with you. They could start over again and maybe have a clean slate and go from there.

Rick Watson: Yeah, there’s a great article in the financial times about four months ago, talking about the problems with spreadsheets. And there was a case in Britain when they were tracking COVID cases. And they kept adding data to the end of the spreadsheet, and it only stored 64,000 rows so it dropped all of the others. Which had major implications for healthcare in the U.K. Get rid of your spreadsheets.

Mike Vizard: All right, awesome. Hey Rick, thanks for being on the show.

Rick Watson: Thanks for the invitation. And I hope people found the ideas valuable, and they should feel free to contact me if they wish.

Mike Vizard: And by all means, check out his book. Hey, thank you all for watching this episode of Digital CxO. You can find this episode and other videos that we have on the digitalcxo.com site. And we’ll see you all next time.

 

Show Notes