In the classic baseball movie A League of Their Own, one of the players is counseled with the statement “If it wasn’t hard, everyone would do it.”
That insight applies to many situations. Among them is the task of turning big data into big rewards. A better understanding of the challenges associated with that task – and, more importantly, of the steps that can help to overcome them – comes from the experiences of the information industry.
Insights about how to gain rewards from big data initiatives emerge from studying two clusters of Information industry businesses.
First are clusters built around intrinsic value information products, involving situations in which the information is of direct and explicit value to the customer that buys it.
In business markets, the prospect lists bought by the sales and marketing departments of most companies represent the classic example of an intrinsic value information product.
Those customers are willing to pay for lists that better enable them to target their outreach and sales efforts, with the value of the prospect list determined by its quality and the degree to which it matches the customer’s business. Numerous information industry companies such as Dun & Bradstreet and IMS Health have grown and prospered by selling such intrinsic value information products.
Firms trying to develop revenue streams linked to big data initiatives frequently think of the business model that is appropriate to intrinsic value information products.
Selling Big Data
A common mindset is embodied in the charge given by one executive to his business team: “We now have more data than I could have ever imagined. Come back and tell me how we can sell it.”
In fact, however, only a fraction of the big data concepts that emerge in business markets fall within this cluster. In an earlier IndustryWeek article, I described three categories of growth strategies built upon big data concepts.
Selling the data, in fact, was only a relevant concept in a few of them, involving as an example instances in which embedding data as part of the product created a competitive advantage and allowed the firm to gain a price premium as a result.
Examples involve big data contributions that yield energy efficiencies, savings in maintenance costs, or other such operational gains.
But even in those instances in which the data has documented and intrinsic value, in business markets, there often remain challenges in capturing that value. Many of the applications that I’ve observed in this category involve savings that accrue over the life cycle of use of the product involved.
In such instances, there is frequently a disconnect between the direct customers that make the product purchase decision and the actual users of the product that subsequently realize the savings.
Sometimes both groups are part of the same company (e.g., purchasing vs. operations), and the challenge is only that of getting them onto the same page. But there are other instances in which two separate organizations can be involved.
In either case, even selling an intrinsic value information product that can make a clear and explicit contribution to the customer can require a carefully crafted strategy to reach all relevant players along the customer chain.
A Carefully Crafted Strategy
Other opportunities that involve selling data can run into complications regarding ownership of the data. Many firms considering big data initiatives have developed business models that parallel those of the information industry firms that sell prospect lists.
Such firms recognize the potential to collect information about the use of their products and translate it into databases that could be sold to third party organizations such as service contractors and parts distributors.
Such initiatives have potential, but often fail because the customers that purchase the products from which the database is generated claim ownership of the data and thwart plans for compilation and redistribution. Addressing data ownership is a prerequisite to any success with such initiatives to compile and sell such data.
The second cluster of information businesses involves interpretive value information products, those that provide insight and understanding relevant to decisions that the customer must make.
Market research products represent a traditional example within this cluster, helping, for example, product development teams better understand unmet needs of their target markets or priorities among the features that might be incorporated into the next generation of products. Information industry firms such as Forrester, Gartner, and Nielsen have grown by offering such interpretive value information products.
Many big data business models involve such interpretive value information products, more, in my experience, than have involved intrinsic value information products. And, even more so than in the case of intrinsic value information products, selling data represents a challenge and is often not the appropriate business model.
In many instances, such interpretive value information products create value not through sales of data per se, but do so either by enabling the creation of new revenue streams in adjacent businesses or by strengthening the firm’s own decisions.
In both of these cases, the big data user is internal, not a third party customer. An example in the first category involves building upon big data concepts to provide aftermarket services to customers that are better, cheaper or faster than what was possible without the big data contribution.
In those instances, the big data reward comes from the new aftermarket service revenue stream, not from selling the data per se. Big data can be of tremendous value in unlocking adjacent business opportunities.
Identifying the Value of Big Data
Even more big data contributions in business markets are linked to improvements in the firm’s own operations, paralleling many of the consumer market big data success stories reported in the business press.
Newly gained insights about how customers use products, about the problems they experience and about the operating environment in which the firm’s products are being used can all contribute to product development strategy and strengthen a firm’s customer targeting and messaging.
Such contributions are as real in industrial markets as in consumer markets, but in no way do they involve selling data.
Big data can be translated into big rewards, but doing so successfully requires that firms think broadly about the value capture mechanism. Only in selected instances is the reward going to involve selling data, and even in those instances it is critical to think about which customer along the customer chain is going to see value in the data that can be provided to them and to address data ownership issues.
Far more frequently, the reward will involve success in creating a new revenue stream in an adjacent business or improvements in critical internal operations that result in stronger bottom line.
The wise counsel offered in A League of Their Own concludes with the statement “It’s the ‘hard’ … that makes it great.” In the case of big data initiatives, the hard involves thinking broadly and creatively about the ways in which value can be created and captured.
George F. Brown, Jr. is the cofounder of Blue Canyon Partners Inc., a consulting firm working with leading companies on growth strategy. He is the coauthor of CoDestiny: Overcome Your Growth Challenges by Helping Your Customers Overcome Theirs, published by Greenleaf Book Group Press of Austin, TX. He has published frequently on topics relating to strategy in business markets, including articles in IndustryWeek, Industrial Distribution, Chief Executive, Business Excellence, Employment Relations Today, iP Frontline, Industrial Engineer, Industry Today and many others.
