There has been considerable buzz about the Internet of Things. IoT is certainly a hot space, with Gartner saying that by 2020 as many as 21 Billion “things” will be in use.

Obviously, 21 Billion is a large number.  With the 2020 global population predicted to be 7,716,749,042 people, that works out to nearly three devices for every person on the planet. So, yes, this is huge.

That said, it seems far too much focus has been on the devices, when the real value from IoT is in the data generated by these things. “Big Data” doesn’t really do justice to the massive amount of data which will be generated by 21 Billion devices.

Of course, predictions are just that – predictions. There is no guarantee that these will be the actual numbers in 2020, but even if Gartner is off the mark by 50%, the fact remains that there will be unprecedented amounts of data generated by IoT. The problem won’t be the number of devices; the problem will be to make use of this data in real-time.

Real-Time or Near Time?

While the IoT enabled devices in the consumer space may get a lot of love and a lot of ink – think IoT thermostats, refrigerators, and other appliances. But IoT has applications outside the home which could prove to be much more interesting.  In 2013, Cisco suggested that “the list is endless”, but would include “…tires, roads, cars, supermarket shelves, and yes, even cattle.”

With that in mind, the use cases for IoT equally endless. A municipality might be interested in IoT enabled traffic signals combined with data from IoT enabled roads.  A logistics and supply chain company could use leverage that municipality’s data and combine it with generated from its own IoT-enabled fleet and equipment to monitor vehicle locations, inventory, and warehouse space availability. The Supply Chain provider could offer data to its retail customers where it is processed and analysed along with many other data sources to better predict supply and demand needs.

So IoT is everywhere, and its all producing data. Te problem is that there is no set data standard for IoT enabled devices. Manufacturers can deliver data as they see fit. This makes it challenging to both work with IoT data, but even more so to uncover interesting aspect to the IoT data which could relate to other data sources.

For example, in the Logistics and Supply Chain space, with a fleet of connected trucks carrying loads of consumer goods, IoT enabled RFID readers can work in conjunction with GPS geofencing data to cross reference where, when, and what items might be removed from a truck at any given point in time or location.

Deviations from any of approved locations and time for any item ideally might warrant an alert as possible, Cross referencing data from GPS tracking with the data from IoT enabled devices is just the beginning. Don’t forget that there may be mitigating reasons for the deviations. Data from real-time traffic sources, weather forecasters and could provide an explanation as to why a truck veered from the approved route and delivery plan.

Similarly, think of a power company monitoring a power grid. Should a power surge occur which could bring down multiple transformers, getting that information in real-time could allow the system to shut down the impacted area before an entire region goes dark. Does that sound like a stretch?  In 2003, the United States and Canada suffered a massive blackout.  In just 30 minutes a 3,500 megawatt power surge shut down over 500 generating units at 265 power plants from New York City to Toronto, and as far west as Michigan.

While this was in the pre-IoT days, it does highlight how an IoT enabled power grid combined with a real-time data platform would go a long way in minimizing the impact of an event like that in 2003.  It could also provide a layer of security against actors with a sinister agenda, like a foreign adversary or a terrorist organization.

Cinchapi is the Data Platform for Real-Time Data

This is why we are building the Cinchapi Data Platform (CDP), with a focus on working with real-time data emanating from disparate sources. The CDP can stream data from any connected source, including IoT generated data, in real-time.  It features a machine learning component which makes sense of data without the need to do that tedious data prep. Literally, as soon as the connected data begins streaming, developers can begin making ad hoc queries and uncover interesting data.  It doesn’t impose any schema on the developer, so working with multiple data sources and formats is a breeze.

Applications can be created to work with real-time data which can allow users to act in real-time, when it matters the most. As desired, automated responses to real-time incidents can be developed to do things like hitting the brakes on a bus before a tire blows out, or to shut down a section of a power grid before a system wide failure occurs.

There are countless possible use cases where the Cinchapi Data Platform is ideally suited to work with real-time data, as well as to work with definitive data – data that absolutely has to be absolutely accurate at a specific time. That time may be real-time, or it could be in reference to a specific time in the past.

Does this sound interesting? If so, be sure to take a moment to view a 60 second video overview, and if you would like a deeper dive, register for a live demonstration of the CDP. Should you have any relevant thoughts about working with real-time data, please use the comment section below.