Due to the IT technology of the mobile internet and things of internet, the business would be induced by the expert from the social network.
Philip Evans
I’m going to talk a little bit about strategy and its relationship with technology. We tend to think of business strategy as being a rather abstract body of essentially economic thought, perhaps rather timeless. I’m going to argue that, in fact, business strategy has always been premised on assumptions about technology, that those assumptions are changing, and, in fact, changing quite dramatically, and that therefore what that will drive us to is a different concept of what we mean by business strategy.
Let me start, if I may, with a little bit of history. The idea of strategy in business owes its origins to two intellectual giants: Bruce Henderson, the founder of BCG, and Michael Porter, professor at the Harvard Business School. Henderson’s central idea was what you might call the Napoleonic idea of concentrating mass against weakness, of overwhelming the enemy. What Henderson recognized was that, in the business world, there are many phenomena which are characterized by what economists would call increasing returns — scale, experience. The more you do of something, disproportionately the better you get. And therefore he found a logic for investing in such kinds of overwhelming mass in order to achieve competitive advantage. And that was the first introduction of essentially a military concept of strategy into the business world. ( Note: Bruce Henderson’s reference on business strategy and Michael Porter Value chain )
Porter agreed with that premise, but he qualified it. He pointed out, correctly, that that’s all very well, but businesses actually have multiple steps to them. They have different components, and each of those components might be driven by a different kind of strategy. A company or a business might actually be advantaged in some activities but disadvantaged in others. He formed the concept of the value chain, essentially the sequence of steps with which a, shall we say, raw material, becomes a component, becomes assembled into a finished product, and then is distributed, for example, and he argued that advantage accrued to each of those components, and that the advantage of the whole was in some sense the sum or the average of that of its parts. And this idea of the value chain was predicated on the recognition that what holds a business together is transaction costs, that in essence you need to coordinate, organizations are more efficient at coordination than markets, very often, and therefore the nature and role and boundaries of the cooperation are defined by transaction costs. It was on those two ideas, Henderson’s idea of increasing returns to scale and experience, and Porter’s idea of the value chain, encompassing heterogenous elements, that the whole edifice of business strategy was subsequently erected.
Now what I’m going to argue is that those premises are, in fact, being invalidated. First of all, let’s think about transaction costs. There are really two components to transaction costs. One is about processing information, and the other is about communication. These are the economics of processing and communicating as they have evolved over a long period of time. As we all know from so many contexts, they have been radically transformed since the days when Porter and Henderson first formulated their theories. In particular, since the mid-’90s, communications costs have actually been falling even faster than transaction costs, which is why communication, the Internet, has exploded in such a dramatic fashion. Now, those falling transaction costs have profound consequences, because if transaction costs are the glue that hold value chains together, and they are falling, there is less to economize on. There is less need for vertically integrated organization, and value chains at least can break up. They needn’t necessarily, but they can. In particular, it then becomes possible for a competitor in one business to use their position in one step of the value chain in order to penetrate or attack or disintermediate the competitor in another.
That is not just an abstract proposition. There are many very specific stories of how that actually happened. A poster child example was the encyclopedia business. The encyclopedia business in the days of leatherbound books was basically a distribution business. Most of the cost was the commission to the salesmen. The CD-ROM and then the Internet came along, new technologies made the distribution of knowledge many orders of magnitude cheaper, and the encyclopedia industry collapsed. It’s now, of course, a very familiar story. This, in fact, more generally was the story of the first generation of the Internet economy. It was about falling transaction costs breaking up value chains and therefore allowing disintermediation, or what we call deconstruction.
One of the questions I was occasionally asked was, well, what’s going to replace the encyclopedia The Wikipedia, of course, is an encyclopedia created by its users. And this, in fact, defines what you might call the second decade of the Internet economy, the decade in which the Internet as a noun became the Internet as a verb. It became a set of conversations, the era in which user-generated content and social networks became the dominant phenomenon. Now what that really meant in terms of the Porter-Henderson framework was the collapse of certain kinds of economies of scale. It turned out that tens of thousands of autonomous individuals writing an encyclopedia could do just as good a job, and certainly a much cheaper job, than professionals in a hierarchical organization. So basically what was happening was that one layer of this value chain was becoming fragmented, as individuals could take over where organizations were no longer needed. when Britannica no longer has a business model? And it was a while before the answer became manifest. Now, of course, we know what it is: it’s the Wikipedia. Now what’s special about the Wikipedia is not its distribution. What’s special about the Wikipedia is the way it’s produced. But there’s another question that obviously this graph poses, which is, okay, we’ve gone through two decades — does anything distinguish the third? And what I’m going to argue is that indeed something does distinguish the third, and it maps exactly on to the kind of Porter-Henderson logic that we’ve been talking about. And that is, about data. If we go back to around 2000, a lot of people were talking about the information revolution, and it was indeed true that the world’s stock of data was growing, indeed growing quite fast. but it was still at that point overwhelmingly analog.
We go forward to 2007, not only had the world’s stock of data exploded, but there’d been this massive substitution of digital for analog. And more important even than that, if you look more carefully at this graph, what you will observe is that about a half of that digital data is information that has an I.P. address. It’s on a server or it’s on a P.C. But having an I.P. address means that it can be connected to any other data that has an I.P. address. It means it becomes possible to put together half of the world’s knowledge in order to see patterns, an entirely new thing. If we run the numbers forward to today, it probably looks something like this. We’re not really sure. If we run the numbers forward to 2020, we of course have an exact number, courtesy of IDC. It’s curious that the future is so much more predictable than the present. And what it implies is a hundredfold multiplication in the stock of information that is connected via an I.P. address. Now, if the number of connections that we can make is proportional to the number of pairs of data points, a hundredfold multiplication in the quantity of data is a ten-thousandfold multiplication in the number of patterns that we can see in that data, this just in the last 10 or 11 years. This, I would submit, is a sea change, a profound change in the economics of the world that we live in.
Now, what does that imply in terms of business? Well, I got a hint of this some years ago. Back in around 2003 or so, I was doing some consulting for the Pentagon, of all august institutions, on the subject of network-centric warfare, and in that context I met a gentleman called Jeff Jonas, a brilliant engineer who had made his fortune designing the security systems in Las Vegas. Jeff said to me, “Next time you’re in Las Vegas, Philip, why don’t you stop by and I’ll take you on the tour. You can meet NORA. NORA will show you a good time.” NORA was not his girlfriend. NORA is the Non-Obvious Relational Awareness system, a real-time fraud control system developed by Jeff, which supports all of the casinos in Las Vegas. We were in the security room of the Bellagio Hotel in Las Vegas, and on the monitor I saw this happen. A woman was playing blackjack against the dealer. There was nobody else at the table. She was winning too much. They know how likely that is, and this wasn’t likely. So the first thing they do is they use facial recognition, see if she’s staying at the hotel. She wasn’t. Then they can kind of run the cameras backwards, tracing her movements back through the hotel to the parking garage, where they found her car. They could then run NORA to find who owned the car. The car was owned by Hertz Las Vegas. Within a second or so, NORA pulled down the Hertz Las Vegas application. Now they knew who the woman was. Where was she staying? Well, they pool the data across the hotels. It turned out she was staying in a hotel across the street. Had she gambled in that hotel? No. Very strange behavior, staying in one hotel, gambling in another. Then came the really interesting thing. NORA looked for a connection between the woman and the dealer, because a very high fraction of fraud in Las Vegas is committed when the staff are actually in illicit collaboration with customers. It turned out, what NORA did was to look through 6,000 databases, public and private, some owned by the Bellagio, some by other hotels, some police records, and so on. It turned out that 10 years earlier, this woman’s brother had been the dealer’s roommate. And it took NORA six seconds to work that fact out. It cost the woman and the dealer six years. This was NORA in action. It’s what today of course we would call big data, long before the term had been formulated.
Now notice some very interesting things about this, most of all the fact that NORA runs as a cooperative across the entire of the strip. These casinos, which are otherwise competing aggressively with each other actually collaborate when it comes to the management of their security systems. They pool data into a common database that is run essentially as a co-op for this specific purpose. Why? Because the scale of NORA, what NORA is trying to do, blows past the scale that even a very large casino can possibly do for itself. The value chain is not big enough to accommodate the economies of scale that are inherent in this particular activity. And that principle, I would suggest, is actually a fundamental and pervasive one. In essence, what happens is that because of these colossal economies of scale in data, what used to be value chains that ran separately are compelled, in order to achieve those economies of scale, to create some kind of common utility, some common resource, a co-op, a pool, a vault of data within which those insights can be gathered.
Now, NORA is a relatively trivial example in the sense that if NORA failed, it wouldn’t exactly be the end of civilization. But consider something vastly more important, where the logic in fact is exactly the same, the logic of healthcare. The first human genome, that of James Watson, was mapped as the culmination of the Human Genome Project in the year 2000, and it took about 200 million dollars and about 10 years of work to map just one person’s genomic makeup. Since then, the costs of mapping the genome have come down. In fact, they’ve come down in recent years very dramatically indeed, to the point where the cost is now below 1,000 dollars, and it’s confidently predicted that by the year 2015 it will be below 100 dollars — a five or six order of magnitude drop in the cost of genomic mapping in just a 15-year period, an extraordinary phenomenon. Now, in the days when mapping a genome cost millions, or even tens of thousands, it was basically a research enterprise. Scientists would gather some representative people, and they would see patterns, and they would try and make generalizations about human nature and disease from the abstract patterns they find from these particular selected individuals. But when the genome can be mapped for 100 bucks, 99 dollars while you wait, then what happens is, it becomes retail. It becomes above all clinical. You go the doctor with a cold, and if he or she hasn’t done it already, the first thing they do is map your genome, at which point what they’re now doing is not starting from some abstract knowledge of genomic medicine and trying to work out how it applies to you, but they’re starting from your particular genome.
Now think of the power of that. Think of where that takes us when we can combine genomic data with clinical data with data about drug interactions with the kind of ambient data that devices like our phone and medical sensors will increasingly be collecting. Think what happens when we collect all of that data and we can put it together and use precisely the NORA-type techniques in order to find patterns we wouldn’t see before. This, I would suggest, perhaps it will take a while, but this will drive a revolution in medicine. Fabulous, lots of people talk about this.
But there’s one thing that doesn’t get much attention. How is that model of colossal sharing across all of those kinds of databases compatible with the business models of institutions and organizations and corporations that are involved in this business today? If your business is based on proprietary data, if your competitive advantage is defined by your data, how on Earth is that company or is that society in fact going to achieve the value that’s implicit in the technology? They can’t.
So essentially what’s happening here, and genomics is merely one example of this, is that technology is driving the natural scaling of the activity beyond the institutional boundaries within which we have been used to thinking about it, and in particular beyond the institutional boundaries in terms of which business strategy as a discipline is formulated. The basic story here is that what used to be vertically integrated, oligopolistic competition among essentially similar kinds of competitors is evolving, by one means or another, from a vertical structure to a horizontal one. Why is that happening? It’s happening because transaction costs are plummeting and because scale is polarizing. The plummeting of transaction costs weakens the glue that holds value chains together, and allows them to separate. The polarization of scale economies towards the very small — small is beautiful — allows for scalable communities to substitute for conventional corporate production. The scaling in the opposite direction, towards things like big data, drive the structure of business towards the creation of new kinds of institutions that can achieve that scale. But either way, the typically vertical structure gets driven to becoming more horizontal.
The logic isn’t just about big data. If we were to look, for example, at the telecommunications industry, you can tell the same story about fiber optics. If we look at the pharmaceutical industry, or, for that matter, university research, you can say exactly the same story about so-called “big science.” And in the opposite direction, if we look, say, at the energy sector, where all the talk is about how households will be efficient producers of green energy and efficient conservers of energy, that is, in fact, the reverse phenomenon. That is the fragmentation of scale because the very small can substitute for the traditional corporate scale.
Either way, what we are driven to is this horizontalization of the structure of industries, and that implies fundamental changes in how we think about strategy. It means, for example, that we need to think about strategy as the curation of these kinds of horizontal structure, where things like business definition and even industry definition are actually the outcomes of strategy, not something that the strategy presupposes. It means, for example, we need to work out how to accommodate collaboration and competition simultaneously. Think about the genome. Think about NORA We need to accommodate the very large and the very small simultaneously. And we need industry structures that will accommodate very, very different motivations, from the amateur motivations of people in communities to maybe the social motivations of infrastructure built by governments, or, for that matter, cooperative institutions built by companies that are otherwise competing, because that is the only way that they can get to scale.
These kinds of transformations render the traditional premises of business strategy obsolete. They drive us into a completely new world. They require us, whether we are in the public sector or the private sector, to think very fundamentally differently about the structure of business, and, at last, it makes strategy interesting again.