How to make data IoT's new oil
If IoT is deeply concerned with data and “data is the new oil” (as UK mathematician Clive Humby asserted in 2006), does that make IoT the new oil?
The answer, perhaps unsurprisingly, is that it’s complicated. To see why, let’s return to the part of Humby’s original statement that’s less frequently quoted. He went on to say that “it’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”
How IoT uses data
IoT does, in fact, often involve vast amounts of data. Indeed, an important aspect of architecting IoT systems is deciding how and where the data is processed and filtered. Some data requires an urgent response. Stop the train! Other data may just need to be appropriately filtered and sent back to headquarters for later analysis. Perhaps a part from a particular supplier is failing more often than the historical norm.
But there’s a common thread here. It’s making intelligent use of data for business outcomes, whether it’s preventing accidents or optimizing operations in some way.
An obvious point? Possibly. But it highlights why simply aggregating data naively doesn’t create value. The right data needs to be used in the right way and in the right place.
Thinking about IoT data in this light has a more subtle implication as well.
A common platform?
When enterprise IoT was first getting buzzy in the 2000s, a lot of people assumed that IoT would develop in the form of standard platforms that was sold across a variety of industries. After all, while vertical applications for industries like retail were also common, much of what we thought of as platforms (such as operating systems and enterprise middleware) ran more or less unchanged everywhere from banks to home improvement stores.
We do see some industry-specific IoT development. This is particularly true when there are specific legislative or regulatory mandates that an industry needs to comply with, such as in the case of positive train control in the U.S.
However, it’s proven challenging to generalize IoT to a platform that can be deployed across a range of companies and a variety of industries.
Integrating IoT
That’s because IoT and its associated data don’t exist in isolation within an organization. Instead, IoT is often the bridge between existing information technology (IT) and operational technology (OT) systems. Perhaps it connects a maintenance dispatch system to sensors that inform a decision about when a repair technician should be sent and with which part.
Of course, there are common patterns and building blocks. Business rules engines, messaging, IoT gateways, enterprise service buses and data caching are just a few of the pieces that typical industrial IoT systems will need. But the arrangement of those building blocks and their interconnections will typically be customized based on the systems already in place and the specific business problems that an organization has prioritized solving.
Data is one of the means by which that can happen. But it can’t do so in isolation. It needs the context of IT, OT and business strategy. It’s within that context that data can have great value. Deprived of it? It probably has negative ROI.
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