Reexamining the Data Money Tree

By Russ Banham, Contributor

While success stories around transforming data into dollars get a lot of media attention, the truth is that not every business can make money by selling its data. For one thing, though precious internally, its data may have little value to others. For another, rushing forward with an initiative that lacks a clear strategy—much less the requisite people, processes, and capital to manage the new business—is foolhardy at best.

“Data monetization is an exciting idea, but profitable results are hard to come by,” explains Chris Brahm, leader of management consultancy in Bain & Company’s advanced analytics group.

Regrettably, many companies are convinced that monetizing their data in a separate data-as-a-service business is a guaranteed pathway to riches. They get so caught up in the anticipated financial killing, they neglect the vastly more important value of leveraging their internal data to improve operations, productivity, and customer experience.

“It’s like they get these dollar signs in their eyes, thinking they’ll make a bunch of free money from this unmonetized asset,” Brahm says. “When things don’t pan out as they expected, they’re genuinely surprised.”

Searching for Gold

When done right, data monetization can be an untapped source of third-party revenue, seeing that it provides value to customers or partners. Pedro Desouza, chief data scientist for Dell EMC Consulting Services, defines data monetization as “the process of leveraging data insights to achieve tangible results.”

A more encompassing definition allows organizations to include use cases in which the stated goal is not an immediate monetary benefit, but could eventually lead to one, such as improving customer experience. Brahm and other advanced analytics practitioners like Henna Karna, chief data officer at the large global insurance company AXA XL, agree. They advise companies to focus on the internal value of their data before thinking in terms of how to sell it.

“Our data team’s focus is to capture and analyze internal and external data for reuse and scalability across the insurance value chain,” explains Karna. “By doing that, we can derive optimal insights in risk mitigation, portfolio balancing, claims analysis, product development, and distribution.”

“Our data team’s focus is to capture and analyze internal and external data for reuse and scalability…”—Henna Karna, chief data officer, AXA XL

In effect, these insights are “monetizable,” as they reduce costs and result in better products and services that can contribute to more revenue. Yet much of the hoopla surrounding data monetization involves developing a separate business unit to sell this new set of information.

There have been notable successes across industries. Payroll provider ADP provides predictive HR workforce analytics through its ADP DataCloud product. The cloud software packages data from more than 30 million employees across 95,000 ADP clients to give companies a better understanding of how their human capital management metrics measure up.

Retailer Tesco has, since 1996, collected insights on customer behavior by analyzing shopper Clubcards. Its subsidiary, dunnhumby, collects and assesses this data from more than 16 million active Clubcard members, along with other retailers, to produce predictive analytics and personalize the shopping experience through various digital marketing campaigns.

The possibilities to make money are limited only by the imagination, the business media trumpets, and the message is compelling. Such sentiment convinces many company leaders that if they fail to monetize their data, they are squandering the opportunity to generate a viable revenue stream. The reality of this endeavor, for most, eventually sets in.

“Selling data to third parties is work—it’s no different than any other big business decision.”—Chris Brahm, management consultancy lead, Bain & Company

“Compared to a few years ago, there is a greater sense of realism,” Brahm says. “Selling data to third parties is work—it’s no different than any other big business decision.” What’s more, he chimes, “it’s not the golden goose it’s made out to be.”

Three’s a Party

Before companies jump headfirst into third-party data monetization, they need to have a considered strategy in place, Karna says. In some cases, it may make more sense to pool data with another company’s than to go at it alone.

Stock analysts, for example, might be interested in buying aggregate data on credit- or debit-card transactions, which may suggest a slowdown or uptick in consumer spending. They also may be interested in buying data on the volume of commercial trucks that pass through a privately owned tollbooth over a period of time, which may suggest a region’s economic vitality or sluggishness. While these separate data elements have value to the same buyer, their aggregate analysis—through an algorithm—may provide sharper insights.

Consequently, it may be prudent for a credit card company and a private tollbooth owner to create a joint venture for this purpose. “By locking in all their data, companies may relinquish the opportunity to drive even greater value by partnering with another business in a data ecosystem,” Brahm says.

A smart data monetization strategy also must take into account the likely buyers for the data, not to mention how to provide this information to these companies and what to charge for the data-as-a-service.

“The first step is to determine which data can add value to another organization’s business process,” Brahm states. “Once you have figured this out, you need to contemplate how to capture this data and apply the right mathematical calculations to it to be of value. Then, you have to create an actual business unit to sell the data.”

Fox in the Forest

Many organizations jump feet first without considering true, realized value for their partners. “We once worked with a company in the accounting technology space that had access to customer information that would, in theory, be valuable to a bank’s loan decisions,” Brahm recalls.

Yet when Brahm and his team reached out to a bank that was interested, its leaders came back and asked what percentage of the tech firm’s customers they could get data on. The answer? Five percent. While the bank felt this data would be interesting, it was too small of a percentage to base important loan decisions on. “While the data was valuable to a bank in theory, in practice its use was limited,” he explains.

Another challenge companies face is the possibility that the government or another third party could surface data that is more valuable and, in extreme cases, more affordable or free.

To avoid challenges like these, Desouza pulls from his group’s standard Vision Workshop value proposition and recommends organizations focus on their internal processes to ensure they are targeting high-impact use cases. Desouza also stresses the importance of building a cross-functional team to find the data’s real value. These teams need to consist of data scientists, data analysts, operational roles, and last, but not least, data stewards to keep watch over the ethical considerations of the data at hand. “The real value is in the analytics, not the raw data,” he says. “It’s not about the data; it’s what you do with the data.”

“The real value is in the analytics, not the raw data. It’s not about the data; it’s what you do with the data.”—Pedro Desouza, chief data scientist, Dell EMC Consulting Services

Brahm urges companies to take seriously the growing number of laws surrounding consumer data privacy. “In the consumer data space, the European Commission’s General Data Protection Regulation [GDPR] rule severely reduces the ability to make money out of the concept,” says Brahm. “And it augments the possibility of losing a lot of money.”

He’s referring, of course, to the GDPR’s sharp teeth—and hefty fines. As regulations that govern businesses that deal with personal data increasingly move to become a global standard, companies “must tread very carefully,” Brahm warns.

And while building a cross-functional team that analyzes and operationalizes data is critical, Brahm takes it a step further. If companies are serious about launching a separate monetization arm, he recommends they also establish a separate organization with individuals entrusted to handle sales and marketing, payables and receivables, and other traditional business processes. “This is not something you want your employees doing in their spare time,” Brahm says.

Lastly, another impediment to data monetization is legacy data storage systems that hinder sharing, integration, and analysis. Desouza recommends companies put together a roadmap with well-defined milestones to ensure modern technology is in place to handle these aspects.

Only the Beginning

In the meantime, companies can still yield significant monetary value from their data, leveraging it for internal purposes to power a competitive advantage. This is the strategic objective in place at AXA XL, which has no plans at present to sell its data to third parties. “We’re effectively `monetizing’ our data by reducing the marginal costs on our data, and enhancing our profits by creating more customized risk management solutions for our customers and partners,” Karna says.

In other words, the insurer’s data is a seedbed of insights into ways to reduce cost, improve products, and provide greater value to customers. Brahm advises companies to consider this intelligent use of data first, before considering its external sale. “For businesses that really give this the time it deserves, data-as-a-service can be profitable,” he says.

With a clear strategic roadmap for how to turn data into internal and external value, the really hard work of launching a business can begin.

Russ Banham is a Pulitzer-nominated financial journalist and best-selling author.