The Evolution of Data Integration with the Internet of Things

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Loraine Lawson, always does a wonderful job covering the world of integration, and this article that covers the integration challenges and opportunities of the Internet of Things (IoT) is no exception. Besides big data, I see the IoT as one of the most interesting data integration challenges coming down the road. Perhaps it’s time that we thought a bit more about it.

You can think of the IoT as the concept of having traditionally dumb devices, such as thermostats, car ignition systems, even your power tools, begin to communicate with systems outside of those devices. These systems gather information that the devices produce, and then analyze that information to take action, or to understand more about the device itself.

For example, my motorcycle gathers and transmits data using devices that I integrated into the bike’s core systems, including ignition, fuel management, transmission, etc. The devices produce and store data that allows me to determine how the bike is performing, and can even spot forthcoming problems, and perhaps take preventive corrective action.

This information can do a few things. It can prove direct feedback to the core systems on the motorcycle to correct problems proactively using pre-directed processes (e.g., send a text if the oil temp is out of range). Or, the data is gathered in mass and analyzed to determine trends, such as data that indicates a failure to one of the air sensors is likely to occur in the near future. It’s handy to have these sorts of conversations with your motorcycle, versus the days gone by when you basically reacted to things as they failed.

If you get this, even if you don’t have a motorcycle, you get what IoT is all about. Just substitute MRI machine, home thermostat, your car, an aircraft engine, or industrial robot for my motorcycle, and you’ll find that many of the concepts remain the same. Indeed, the data these devices and machines can create and transmit allows us to understand more about how these devices work, and take proactive action to increase the value of using these devices or machines.

So, what about the data integration problem? As Loraine puts it, “IoT devices are becoming little silos of data, which makes it hard to share access.” We need to get good at sharing data with these devices, else the value that the IoT is supposed to bring us won’t materialize.

The integration issues are much like the integration issues we dealt with back in the old days. IoT devices send data in very different formats, using very different interfaces. In many instances, they communicate with the outside world as an afterthought. Planning, or a lack of planning, for integration shows in how well the devices participate within a data integration approach, and how well they work and play with data integration technology. While device and machine vendors now deliver APIs, those APIs have a tendency to be proprietary. Thus, you end up writing very different interfaces to communicate with the different IoT devices.

As with any data, establishing open data standards is the key to resolve the issues of making data produced by devices more consumable. That’s why HyperCat is big news, and holds the promise of getting these devices on the same page, when it comes to data communications and integration.

HyperCat is an open standard developed by a UK-based consortium of 40 technology companies. Like other new standards that hit the industry, there is much additional work that needs to be done to get more IoT device providers onboard, and get the standard finished and implemented.  However, this is a step in the right direction.

On a technical level, HyperCat a catalog of specifications that provides a common way to describe the information stored on data hubs, or devices. HyperCat tells developers what they need to include in the data to make it easy for apps to search and identify it.

To me, HyperCat seems more like a data abstraction layer that provides a common way to view and understand the data. It’s missing things that deal with the complexities of communicating with the different devices, which I think is a much harder problem to solve.

No matter if it’s HyperCat, or some other standard, there needs to be some common sets of data services that IoT devices provide. Until that occurs, those who integrate with these devices will have to use whatever APIs and/or services that the devices are able to provide.  It will be a bit like the 90s, all over again.

In the meantime, it’s prudent to assemble your data integration strategy. Be sure to include IoT devices, and how the use of this technology will enhance your efficiency. For most enterprises, the ability to include devices and machines into the core business processes is something that’s long overdue, and the data integration technology is ready to get you there. Time to get started with IoT.

About David Linthicum

Dave Linthicum is the CTO of Cloud Technology Partners, and an internationally known cloud computing and SOA expert. He is a sought-after consultant, speaker, and blogger. In his career, Dave has formed or enhanced many of the ideas behind modern distributed computing including EAI, B2B Application Integration, and SOA, approaches and technologies in wide use today. For the last 10 years, he has focused on the technology and strategies around cloud computing, including working with several cloud computing startups. His industry experience includes tenure as CTO and CEO of several successful software and cloud computing companies, and upper-level management positions in Fortune 500 companies. In addition, he was an associate professor of computer science for eight years, and continues to lecture at major technical colleges and universities, including University of Virginia and Arizona State University. He keynotes at many leading technology conferences, and has several well-read columns and blogs. Linthicum has authored 10 books, including the ground-breaking "Enterprise Application Integration" and "B2B Application Integration."

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