Powering the Growth of Leading Companies in the Digital Economy
We are all stakeholders in the economic systems within which we live and work, and the better we can understand their dynamics, the more likely we are to navigate them successfully. For the most developed economies of today, this means understanding the transition from an industrial to a digital economy, and specifically, how economic power is migrating from familiar to unfamiliar sites.
To capture the magnitude of the shift underway, it really has to be put in a mega-historical context along the lines of the World Economic Forum, Thomas Piketty, and the like. Here is my version:
Each of these ovals seeks to capture the core of power in the economy of its era. In the case of the agricultural economies that held sway during most of human history, the epicenter of power was land, and the critical ingredients to control that power were military might to acquire and retain the land and captive labor to work it.
With the advent of better transportation and communication came the rise of trade, leading to the rise of manufacturing, culminating in the industrial economy. The epicenter of power here was capital to build and own the factories for manufacturing and to finance the inventory for distribution. This is the model that drove massive growth in the 19th and 20th century global economies, and it is hardly done yet. Indeed, for the next several decades at least it will continue to constitute the vast majority of the economic value created and consumed on our planet.
That said, the industrial model is losing power to an emerging digital economy at an astounding rate. This is most obviously reflected in the public stock markets where companies with digital business models are being valued at premiums that dwarf those applied to even the most successful industrial enterprises in the same category. It is also seen in the shift in capital expenditures within the industrial economy to fund the digital transformations that every constituency is demanding of every member in the value chain. And indeed, the whole idea of the value chain itself is being undermined by a new model of power that does not operate according to the old models. Hence the need for a new framework.
So how does power work in the Digital Economy? As the framework above suggests it is organized around “data with an asterisk.” Specifically, what is actually required is proprietary access to time-sensitive data that carries signals that matter. Just to be clear about what is entailed, we need to unpack this phrase as follows:
Proprietary access does not mean you have to own or retain custody of the data per se. Instead it means you must have privileged access to it, at least for a critical amount of time, the time needed to extract actionable insights from it and then to act upon them. This is the foundation of competitive advantage for a digital business.
Time-sensitive data typically refers to information in log files that come from systems of engagement or, looking ahead, from the Internet of Things. The idea here is that digital actions preempt more traditional actions because they intervene in advance of the opportunity being detected by more conventional means.
Signals that matter means that somewhere in all the noise of these log files there exists data patterns which, when decoded, enable the kind of value-creating, preemptive actions mentioned above.
In the digital economy such signals live at the intersection of two types of datasets—systems of record, which capture transactional data, and systems of engagement, whose log files capture all the peripheral interactions that occur in and around a transaction. If a company can gain and secure proprietary access to any meaningful intersection of these two datasets, then they can and should invest in the systems of intelligence necessary to extract actionable signals from them and feed them into a monetization engine that can extract profit from the actionable signals, be that through advertising, retail transactions, or a brokerage fee.
This is the formula that underpins the extraordinary valuations of Google, Facebook, Amazon, Netflix, Uber and Airbnb. Moreover, it reflects the investment philosophies and interests of virtually all the successful tech venture capital groups. Equally importantly, however, it does not underpin the business models of the leaders of the industrial economy, including such tech-sector icons as IBM, Microsoft, Cisco, Intel, Dell/EMC, Oracle, and SAP. That is why every one of these companies is investing so aggressively in digital transformations to reposition themselves as bridges between the industrial and digital economies. And make no mistake, such bridges will be needed for a very long time to come and thus are highly valuable to create.
But all this does not change the fact that the power base that underlies the global economy has shifted. And that means, the critical strategy questions that all companies need to answer have shifted along with it. In this context, the number one question every company should be asking is, Do we have access to signal? That is, do we have a viable means by which we can secure proprietary access to signal-bearing data that, if properly analyzed in a timely manner, would enable better economic decision-making in our ecosystem? For start-ups seeking VC funding in the present era, the answer should almost always be yes, granting, of course, that it will take time to attract a meaningful volume of data, not to mention to develop the algorithms that can make sense of it in real time.
For established enterprises, on the other, the answer is likely to be no, not at present. That then gives rise to the second question: How do we get it? The short answer to this question is, through strategic acts of generosity. We first saw this principle at work during the initial Internet Bubble expansion. There, much to the surprise of anyone raised in the industrial economy, the focus was all on attracting the eyeballs of consumers, with the motto being URL, standing for Ubiquity Now, Revenues Later. Rapid accumulation of huge populations of end users actively using the software for free was what allowed the first wave of leaders to establish large sustainable positions of power, ones they have subsequently monetized to an extraordinary degree.
Translating this principle to the marketplaces that host industrial business models, “free services” don’t normally have anything like this impact as there are just too many barriers to adoption, beginning with liability risk, security anxiety, and inertial resistance to change. Here generosity needs to be reframed as some form of new, exceptional value provided at no extra cost. The Internet of Things is already demonstrating how this can work. By instrumenting a capital asset, vendors like GE and others can monitor its state and preempt downtime through preventative maintenance—all at no additional charge. To do this, of course, they need proprietary access to the log files from the assets in question, data they can merge with logs from similar operating assets into a “data river,” which in turn can feed the development and refinement of algorithms in their systems of intelligence. As these algorithms get more and more predictable, GE can underwrite future operations by assuming the risk of downtime themselves, eventually offering “assets as a service.” The point is, none of this is possible unless and until an enterprise can acquire proprietary access to time-sensitive data that carries signals that matter.
Lacking such access, enterprises can still leverage a digital transformation, but their goal is a different one. Instead of enabling a new digital business model, it is to modernize an established industrial business model. These efforts are well under way in many industries, including mobile apps for traditional banking, traditional transportation, traditional home automation, and the like. Applying AI to customer logs in a traditional CRM system will improve its performance dramatically, as will applying it to supply chain information in your ERP. Blockchain technology will likely revolutionize supply chains that must authenticate quality and integrity, in sectors like health care, pharmaceuticals, collectibles, and the like. The point here is that digital services have a ton of value when they modernize an industrial operating model regardless of when or if a company subsequently engages with a digital business model.
What all industrial enterprises must come to terms with is a world in which products, which have historically been the organizing principle for the industrial model, are now being displaced by services, which used to be ancillary to the industrial model. To bring about that displacement those services must be digitally enabled. Such digital enablement underpins the bulk of the digital transformations under way in enterprises today. They are being undertaken to improve customer satisfaction, forestall customer churn, and increase productivity. As such they are evolutionary, albeit not achieved without considerable efforts on the part of all.
This stands in direct contrast to next-generation digital business models, regardless of whether they are funded by venture capital or as R&D within established enterprises. These models can give away industrial infrastructure because they can monetize data with an asterisk. The challenge, of course, is that these new models consume a lot of up-front investment before they generate material returns. Venture capital is designed to manage these sorts of J-curves; the balance sheets of publicly held enterprises are not. This creates the crisis of prioritization I have been blogging about for some time, the one addressed by the frameworks in Zone to Win, the one I will spare you from rehashing yet again here.
Instead, let me leave you with this thought. There are two types of digital transformation, one designed to incubate and scale a digital business model, the other to modernize and extend an already scaled industrial model. Both create exceptional value, but they do so in markedly different ways. As a result, the leaders, the culture, the funding, the performance metrics, the ROI, and the rewards for each are such that any team really good at one is likely to be pretty bad at the other. So it behooves you to get clear about which one you are undertaking and then proceed accordingly.
That’s what I think. What do you think?