Nearly 30 years ago, Stephen Brand made one of the more prescient observations about the unfolding data revolution: "On the one hand, information wants to be expensive, because it's so valuable. The right information in the right place just changes your life. On the other hand, information wants to be free, because the cost of getting it out is getting lower and lower all the time. So you have these two fighting against each other."
This assessment is spot on. Data promises a lot of new value, from insights that lead to better-targeted advertising, to ideas for new products, to "this changes everything" discoveries — the "expensive" half of Brand's observations. But realists fully appreciate the "free" half. Translating those insights into profitable new and improved services and sustained competitive advantage is another matter altogether.
So how can a company spend more time on the valuable, expensive side and less on the free side? The key lies in developing and exploiting "proprietary data" — data that you and you alone possess. Without such data it is simply too easy for competitors to let you do the hard work of innovation, then copy your insights and erode your competitive advantage.
I use the term "proprietary data" after the 2003 HBR classic, "IT Doesn't Matter," by Nicholas Carr. There, he introduced the contrasting notions of "infrastructure" and "proprietary" technologies. An infrastructure technology diffuses throughout the economy in support of numerous industries and, in time, becomes available to all. It is difficult to sustain a competitive advantage via infrastructure technologies.
Proprietary technologies, on the other hand, can be protected, at least for a significant time. Because they are protected, their owners can gain and sustain a strategic advantage. This logic applies directly to data. Unless you can create and protect a measure of "proprietary data," your path to riches will be fraught. You probably don't need a lot of proprietary data. Just enough to distance yourself from the other guy.
Data earn proprietary status in two distinct ways — through structure and content. Think of a blank meeting calendar as a structure. It divides time into days and hours, and calls for details such as meeting place, duration, and attendees. A useful structure but with no actual meetings. Day-in and day-out, transactions populate the calendar. A phone call may yield, "Tuesday, March 20, at noon: lunch with Pete at Olive Garden" — actual content.
All data possess both components, and they provide companies tremendous opportunity to capture (and exploit) the subtleties of their environments. To appreciate the significance of this point, consider the many ways that you are known to those who do business with you. Your doctor thinks of you as a patient, your banker as an account, your lawyer as a client, and the department store as a shopper. You are the same person, but each employs a different structure because they are interested in different information.
Companies should seek to create advantage via proprietary data structures. For example, Facebook and LinkedIn have found ways to gather interesting data about people through their "friends" and "connections," respectively, and secured an advantage. Others don't have access to these data. And network lock-in may help them maintain that advantage for some time. Another example is the CUSIP, a means of identifying securities and process trades efficiently. It is owned by the American Bankers Association and administered by Standard & Poors and has provided a long-term advantage to S&P.
Even without a proprietary data structure, companies should still seek to create advantage through their content. Only you have the specific transaction, "John Smith bought peas, bread, and grape nehi at 9:27 AM on March 11, 2013," and the tens of thousands like it each day! Retailers, such as Amazon, Kroger, and Target, use this data to tailor their advertising for John Smith. Further, they can combine John Smith's transaction with others to better understand buying patterns, improve their supply chains, lay stores out more effectively, and so forth.
It is easier for competitors to copy your successes with transactional data. One retailer develops an insight into customer behavior and others follow suit. But don't avoid this avenue on that score alone. After all, you conduct transactions every day, enriching the base of "things the competitor doesn't know" each time. And there can be solid advantage therein. For example, pharmacies build a patient's prescription history to better identify possible drug interactions, suggest cheaper generics, and get customers into their stores. It is no coincidence that patients must walk all the way to the back to get to the pharmacy!
The key to exploiting proprietary data lies in identifying which data offer the most potential for profit and sustained advantage. All data are not created equal; some are far, far more important, especially, into the future. It is vital to develop a deep understanding of what is essential for the future. Thus, innovators may seek competitive advantage in their product and service data; the low-cost providers on operations and process data; and those aiming for customer intimacy on customer data.
Finally, you must give data on the intersection of proprietary and important special attention. Ensure they are of the highest quality, enrich the data associated models, and keep them safe from the prying eyes of competitors and pirates. These data must capture the lion's share of the dollars you devote to data. And, more than anything else, you must constantly look for new ways they can help distinguish you from your competitors.
Those who appreciate history will find something age-old in the recommendations above. After all, since time immemorial, the general who knew something the enemy didn't had a better chance in battle; just as today every salesperson knows she gains an edge when she learns that a special pinot noir over lunch helps seal the deal. Proprietary data don't guarantee success. But those who find such proprietary nuggets on a large scale have a huge leg up.