Teletrac Navman US Ltd. was deeply into the “internet of things” long before it was a hot buzzword.

The fleet tracking and management software company gathers detailed information on more than 500,000 vehicles for 40,000 customer organizations across six continents. Black boxes installed in cars, trucks and heavy machinery continually poll embedded sensors to learn about everything from fuel efficiency to whether the driver’s seat belt is fastened. Teletrac Navman feeds that data back in reports that help its customers improve operational efficiency.

All of that forced the company long ago to confront an issue that’s only beginning to occur to most IoT adopters today: Who owns all that data?

In Teletrac Navman’s case, the customers do. “We want to put our customers in a position to be successful because of us, not beholden to us,”said Michael Bloom, the company’s director of product management for business intelligence. But another company might just as well choose differently.

In fact, there are no standards and few consistent practices for use of IoT data. As an estimated 50 billion connected devices come online over the next three years, questions about who owns machine-generated data, and under what terms it’s shared with others, will assume unprecedented importance, experts say.

“It’s going to become a white-hot issue over the next 12 to 24 months,” said Barry Libenson, chief information officer at Experian PLC, the consumer credit reporting giant.

Ownership and use of all that data seems certain to become an even more dangerous minefield for many large enterprises and their customers than the one in which consumer companies such as Google LLC and Facebook Inc. are mired today. Indeed, it’s not a stretch to say it could be a matter of life and death. “IoT data, unlike Facebook data, could get someone killed,” said David Knight, chief executive and chief technology officer of IoT data curation startup Terbine Inc. “I don’t want a machine telling a bridge to open while there are cars on it.”

The notion that data has economic value isn’t new, and companies have been leveraging information from connected devices for years. The rapid rise of Wal-Mart Stores Inc. in the 1980s has been credited, in part, to the company’s success at using data collected from cash registers to create buyer profiles that it then used to to squeeze discounts from suppliers. But several technology factors are converging to democratize the gathering and brokering of data in ways that were never before possible.

One is that a lot more data can now be captured from connected devices. Gartner Inc. has forecast that 75 percent of enterprise-generated data will be created outside the data center by 2022, up from 10 percent today. Much of that data will be stored in the cloud, where it can be easily shared and combined with other sources. Big data and analytics are also unlocking value in massive information stores that couldn’t be easily accessed just a few years ago.

The result is that machine-generated data is set to take on new value as organizations discover uses that previously didn’t exist. But who owns the data, and under what terms it’s shared with others, is still an open question. And the answer likely won’t be forthcoming until after problems blow up in unexpected ways.

Few standards

“There are no general laws around information property other than some regulatory rules in vertical industries,” said Douglas Laney, vice president and distinguished analyst at Gartner and author of the new book “Infonomics: How to Monetize, Manage, and Measure Information as an Asset.” “The courts are thoroughly confused about whether information is even property.” The picture is further complicated by rules and precedents that diverge by country.

In his book, Laney argues that organizations need to take a far more disciplined approach to valuing and accounting for data than they do now, effectively treating information the same way they do money. That means establishing clear lines of ownership as well as contractual terms for sharing or selling data to others.

As more devices connect, the number of potential constituents for the information they generate grows. Driverless cars, for example, will continually throw off data that interests urban planners, law enforcement officials, auto makers, tire producers, public works departments, event organizers, insurance companies, weather forecasters and radio advertisers, to name just a few. Owners of fleets of such vehicles, such as car rental companies, could turn these data streams into lucrative new lines of business.

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But that doesn’t necessarily mean new revenue streams. “The range of ways for any organization to monetize information is nearly endless,” Laney writes in “Infonomics.” He cites the City of Los Angeles’ “SmartPole” initiative (right), which is placing IoT-enabled LED streetlights around the city that can capture information about outages, accidents and traffic patterns to improve safety and cut maintenance costs. The city also earns $1,200 per pole per year by renting space on the light standards to providers of cellular and Wi-Fi services.

General Electric Co. is using sensor data to drive bigger and longer commercial contracts. The company charges its power utility customers for guaranteed cost efficiencies derived from instrumented equipment. Its Predix industrial IoT platform, which customers use to streamline efficiency through predictive and prescriptive analytics, generated $6 billion in revenue in 2015, and is expected to grow to $15 billion by 2020. “I think the sky is the limit,” John Magee, GE’s chief marketing officer, said in a recent interview with SiliconANGLE.

Data can also enhance customer value. Earlier this week, Workday Inc. rolled out a benchmarking service that gives customers of its financial and human capital management software comparative data from other Workday customers against which to measure things like employee retention and leadership effectiveness. That same approach can work for makers of solar panels, air conditioning systems and factory floor machinery, strengthening relationships by giving customers insight into how their equipment is faring compared to others.

Not least, data can serve as barter currency. Owners of vehicle fleets can exchange data about reliability and fuel efficiency with auto makers in return for discounts. When Experian’s Libenson was the CIO at Safeway Inc., he helped structure barter deals in which information from Safeway’s customer loyalty program was traded for syndicated research about its markets. “We’re so early in the process that there will be new use cases cropping up everywhere,” he said. “I think consumers and businesses will become very territorial” with their data.

New economy, new questions

As ownership and value lines are established, whole new lines of business are also likely to emerge. Insurance companies may want to fine-tune premiums based upon lifestyle choices as measured by devices such as fitness trackers or set auto premiums based upon sensors that measure speed, braking habits and seat belt use. That’s a potential revenue opportunity for the companies that own that data. Experian is exploring new data sources they could establish the creditworthiness of people who don’t have a long credit history, Libenson said.

Whatever the payoffs, the infrastructure to store and manage data, write contracts and track results is still in the formative stages, with few rules or standards. Laney thinks existing brokers and agents will diversify their services to accommodate new data types  and contracts, and some players are already in that process. “Large companies will become brokerages,” he said. “There will be specialized data brokers and there are data marketplaces emerging as well.”

But ownership raises new questions as well. “How do we create a genealogy trail of data? How do we manage the way others use and view it? And if you close your doors, how do you ensure the data doesn’t end up in someone else’s hands?” Those are just some of the questions John Boruvka is asking. A vice president at records-management firm Iron Mountain Inc., he oversees the intellectual property practice, which he believes will take on an increasingly important role for the company as data is traded like currency.

Iron Mountain’s services include software escrow, a practice in which a trusted third party takes ownership of software source code in order to protect customers against disputes, bankruptcies and other critical business events that disrupt access to important applications. The real power of IoT data comes from mixing and matching streams from different sources, he said, but the process requires trust and governance.

“When you start to plug in other data sets, will there be clear lines of ownership?” he asked. “If the maker of your fitness tracker gets bought, will the new owner be able to change the rules about how your data is used? A lot that people take this for granted.”

Libenson agreed. “As the amount of data grows explosively, there will be a need for independent third parties,” he said. “The last thing Bank of America wants is for their data to end up with Citibank.”

Third-party markets

Those third parties may be today’s household names, but new types of brokerages may emerge to handle IoT data specifically. David Knight is betting on the latter. His startup Terbine is building a “trusted sensor data exchange” for swapping IoT data between companies and industries. Rules regarding data ownership are so vague and contractual relationships so varied that traditional brokerages won’t work, he believes. “There’s all this kumbaya stuff and no one has actually tested the rules,” he said.

Knight sees an entirely new supply chain springing up around machine-generated data. “A data supply chain is one where the data moves across the chain and different players add value,” he said. “That means you have to worry about data preservation, money flowing back and forth and how data is packaged.”

Companies also must attend to data integrity, he said, because errors or omissions can have disastrous consequences, whether it’s that bridge opening while there are cars on it, or self-driving cars that must make accurate split-second choices on how to avoid killing a pedestrian. Liability rules will need to be put in place, and that ties back to data ownership.

Services may even emerge to help individual consumers negotiate and manage access to the data being generated by their increasingly instrumented homes. “I have solar panels on my house, and the manufacturer gets data from them every five minutes for free,” said Libenson. “It’s kind of infuriating because it’s my data. I think we’re going to see people pushing back.”

Regardless of whether IoT eventually sparks a consumer revolt, the questions of who owns data and how to value it will grow with the number of connected devices. Which is to say: very fast. Businesses will need to start preparing soon, or the value they hoped to get from all that data could vanish as quickly as it was generated.

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