Internet of Things Faces Three Difficult Data Dilemmas

Loraine Lawson
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Six Reasons Businesses Need to Pay Attention to Unstructured Data

Innovation always has a learning curve, and the Internet of Things (IoT) will be no different. It’s just a matter of figuring out where the problems are.

A recent IDTechEx report explored these problems as business opportunities. You can buy up to five copies of the full report for $3,995 report, but several technology sites have taken excerpts from it to identify the obstacles to the IoT.

ReadWrite’s Matt Asay and CIO Insight’s Karen Frenkel both covered highlights of the report. Reading them, it’s painfully clear that data — accessing it, integrating it, sharing it, managing it, and analyzing it — will be among the major hiccups as the IoT shifts from concept to deployment.

Here’s a look at three of the data-specific challenges the report identifies:

  1. The market rewards data hoarding instead of data sharing. As it stands now, “data hoarding” is more profitable than data sharing, and to that end, many IoT manufacturers make IoT devices using proprietary hardware and software. That needs to change, but can we find a business model that rewards sharing and interoperability over hoarding? If mobile devices — and PCs and servers and just about every other technology innovation — are any indication, I suspect the short-term answer will be no. After all, early adopters tend to support market leaders until it becomes too painful to continue with proprietary solutions.
  2. Even if there was a business model for sharing, there’s no single open and accepted data standard for IoT devices to support it. Efforts are afoot to create one — but without a business imperative, we’re more likely to see competing standards emerge. In fact, there’s already a cornucopia of protocols and standards, most of which are proprietary. Brian Proffitt explored this “IoT Wild West” in a June ReadWrite article.
  3. The IoT needs APIs and middleware. APIs played a key role in resolving interoperability issues for the cloud and mobile devices. They could do the same for IoT, which is no doubt why Frenkel includes APIs for sensors as one of the top 12 IoT obstacles.

APIs don’t negate the need for middleware, however. Middleware would support interoperability and integration between devices that use proprietary solutions or simply a different standard. Even so, as Asay points out, it’s unlikely any API hub or integration layer could translate all of the sensor data types.

It reminds me of the early days with the cloud, when a similar situation existed due to the variety of SaaS solutions. Cloud integration vendors solved it by allowing developers to build and sell APIs through their cloud shops. That may emerge as a viable solution for IoT devices. 

In an unrelated post, Gartner Research VP Earl Perkins highlighted another, perhaps bigger, problem that could affect IoT data: identity and asset management. That’s a whole other can of worms, and his take on it is well worth the read. I mention it because Perkins offers some assurance that IT services firms and others are already working on interoperability problems:

“Fortunately, there are companies that have already begun to tackle this issue, developing conceptual models for the Identity of Things and testing them. IoT application developers and integrators are also driving efforts to ensure their solutions have a world in which they can operate.”

Loraine Lawson is a veteran technology reporter and blogger. She currently writes the Integration blog for IT Business Edge, which covers all aspects of integration technology, including data governance and best practices. She has also covered IT/Business Alignment and IT Security for IT Business Edge. Before becoming a freelance writer, Lawson worked at TechRepublic as a site editor and writer, covering mobile, IT management, IT security and other technology trends. Previously, she was a webmaster at the Kentucky Transportation Cabinet and a newspaper journalist. Follow Lawson at Google+ and on Twitter.

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