Imagine you are the product manager in Samsung or LG’s appliance division and you have decided to sell refrigerators at a discount because “the real money will come later,” by “monetizing” the stream of data that will be generated by all the new sensors included in the design.
But what if the ability to collect proprietary data gets legally and ethically complicated, a few years down the line? Say the government imposes a limit on data collection from durable goods that are not replaced in 5 years, deeming these to be “natural monopolies”—for example, products with high switching costs for consumers, such as home appliances, smart home devices (e.g Nest thermostat), TVs, or cars—and therefore subject to US anti-trust laws?
Business decisions about “big data” applications are not simply engineering or technology decisions. They are have philosophical, legal, and moral implications.
Markets, and the business models they support, are defined and sustained both by technology horizons as well the social, economic and political agendas of a certain moment in time. Understanding these contextual factors are equally important in figuring out how to position yourself on the winning side of data-enabled businesses.
The challenge for companies is that nascent markets built today around big data are going to change radically. We are now at a point in development similar to where internet business models were roughly 12 years ago. Since then, we have seen much back and forth about appropriate norms and rules regarding privacy and net neutrality, as well as dramatic shifts in how the public views and trusts some of the leading, innovative internet companies (i.e. is Google the “do no evil” company, or the “evil monopoly?”)
In a similar way, the fundamental decisions about what is fair have not yet been determined. And they may shift around a lot over the next five, ten, even twenty years. Some of the most important market-determining questions—like those in the financial services and appliance examples—haven’t yet even been clearly posed.
Why is this important? Because as with other newly emerging markets, the definition of the playing field will determine what is or is not a real opportunity, and which parts of a big data business will be the most advantaged and protectable. Anyone who is not thinking about this as part of their strategy may be left behind in some of the largest opportunities, or find themselves over-invested in fantasy, million-dollar businesses. This is a critical time for firms deciding which of their potential data-intensive business ideas to pursue, and in what form.
But while enabling technology may be “exponential” and future sources of customer value to be unlocked “boundless,” budgets and time are not. When everything can look at first glance like a billion-dollar opportunity, these can be hard choices to make.
Having a point of view on the broader context will help organizations evaluate these choices more clearly, and with more complete criteria. Such criteria include:
• Which applications and use cases offer the most sustained value to your company? For example, for a digital health company, how will your bottom line be affected if new rules for wearable computing are introduced that define wearables as medical devices? Compliance costs could make many business models unprofitable.
• What data will be most valuable, and which is worth owning versus buying? It may be much cheaper for others to collect and organize data than for you to create your own proprietary system.
• What kinds of data use will cross the lines of socially accepted behavior? The now famous Target pregnancy offer case shows there will be situations where you should not preemptively market to someone. But what if you do it based on other kinds of attributes—like having been admitted to Harvard? A robust strategy will need to understand the distinct reputational risks and returns for every kind of sale and try to position around the positive-attribute marketing versus the negative in many situations. But this requires human judgment.
Sure, in theory, hospital emergency rooms would run more efficiently with real time pricing—just like Uber does. But a decision such as this requires applying additional choice criteria about what kind of value capture the market will allow. The social, economic and political guide-rails that will ultimately shape where pools of value can be created are evolving just as dramatically as the technology.