Turning Data Into RevenueBy Guest Author | Posted 2013-11-12 Email Print
Savvy companies understand that data has real, lasting value and can be turned into an ongoing revenue stream. There's money in the data market. Are you in?
By John Lucker
The concept that a company’s data might actually be worth something to someone else isn’t new. Many consider data to be a strategic asset and one of the most valuable off-balance-sheet assets a company might have.
For several years now, online companies have put their data to work, and some have earned hefty returns by making it available to others in a way that adds value without cannibalizing their own business. Social media companies sell some of their data to advertisers, and online merchants use their data to link with other merchant sites to target their customers via a variety of tools for cross-selling, up-selling, next-best offers and attrition management.
These activities are old news, however, and a new breed of savvy companies understands that data has real, lasting value and can be turned into an ongoing revenue stream. How? One way is via direct or brokered data markets—a business model that has defined a new market category. Here, companies sell, buy or barter data for mutual benefit.
For example, let’s take a company that provides cloud-based services. This company would have a very expansive technical architecture. What if it could coalesce all the data it collected on the architecture’s performance and reliability, and then provide that data to hardware and software vendors that develop and market products to serve the cloud?
Those vendors would welcome the field-tested insights that data could provide. They’d be willing to pay a good price for it too—if not in hard dollars, then in the form of free or discounted product enhancements or service-level customizations in exchange for the feedback.
In a similar arrangement, a snack food maker might put its proprietary product serving and nutrition information on a data market. In exchange, it could receive aggregate consumer feedback on the product from data brokers whose role is to facilitate value-added exchanges for each party in a data exchange.
As that data becomes productized, a mobile software developer might buy the information to include in its newest app that compares the information on various products and offers healthful recommendations. Or perhaps the company uses the data for users to track daily food consumption as part of a diet regimen. With these beneficial exchanges, everybody wins—either financially or indirectly via quid-pro-quo scenarios.
The caveats here are at least twofold. First, privacy and identity preservation are increasing concerns for consumers, and companies should be diligent in complying with right-to-use data agreements: End User License Agreements (EULAs). Second, those in the C-suite might worry that they’ll give up their competitive edge by providing valuable information to current or future competitors.
If customer terms of service include salient details and appropriate opt-in or opt-out provisions that are coupled with worthwhile benefits to the consumer, privacy concerns can be satisfactorily mitigated. Companies also should think very carefully about how provisioned data could be manipulated to provide competitors with unanticipated benefits.
It’s clear that much careful thought and strategic decisioning is necessary for effective data monetization. However, pioneering business case studies have proven that the potential value can be great.
Special note: Mistakes or failure are not options here, and executives must take the time to become confident that the potential risks will be offset by even greater potential rewards. There's money in the data market. Are you in?
John Lucker is Deloitte’s Global Advanced Analytics & Modeling market leader and a leader for Deloitte Analytics. He is also a leader of Deloitte’s Advanced Analytics & Modeling practice. John provides clients with strategy, business, operational and technical consulting services in the areas of advanced business analytics, predictive modeling, data mining, scoring and rules engines, and other analytic business solution approaches.
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