Are You Ready for IoT Data Integration?
As the Internet of Things (IoT) continues its explosion, the numbers of practical applications are beginning to rise. Many consider this space to be all about devices that communicate. In reality, it’s about massive amounts of data, and how we manage and analyze that data.
For example, drones fly over a cornfield to gather data that will determine the effectiveness of irrigation. This process collects gigabytes of data that can be analyzed to determine where the farmer needs to address issues that reduce the yield of the field. In another example, an MRI gathers massive amounts of data in a single scan that are analyzed along with past diagnoses and outcome data to determine what’s going on now, as well as what will likely go on in the future, based upon patterns that it sees in the scan. In yet another example, a jet engine produces data during a flight that, when analyzed using predictive analytics, lets the pilots know that it’s about 5 hours away from a complete failure.
The use cases for IoT are expansive. They are all data driven, and just like applications that produce data, care must be given as to how the data is integrated with other systems. Thus, the two most important concepts of IoT include data integration and data analytics.
These days, anyone who builds IoT systems needs to understand a few new requirements for IoT data integration technology:
First, the volume of data will increase significantly, and the speed with which the data is transmitted will be near-time or real-time streaming. Message-based data integration approaches may not scale well using older data integration approaches.
Second, at the same time, the data quality must be checked at rest and in flight, and must be placed in a data store where it can be analyzed. Typically, immediately. Bad data ruins the value of IoT. Considering that devices produce all types of data in all types of unstructured states, the ability to place policies to perform data quality checks and data governance is an imperative.
Finally, in many instances, data integration approaches and technology will have to combine data on the fly. For instance, the ability to assign rankings for level of irrigation out of an existing database, using data gathered in real time from the drones flying overhead.
IoT meshes nicely with both cloud and big data. Indeed, most IoT applications and data will find that they are more cost effective when hosted in the cloud. Real time data analytics that allow us to gather value from IoT systems come directly from emerging big data technology.
IoT is changing the game as to how we gather and deal with real-time data. It’s changing our lives, in terms of having a better relationship with technology, and finally gives us the data to proactively solve problems.
Key to making IoT work is having a sound data integration strategy and technology implementation. If IoT is in your future, now is the time to figure this out.