The Internet of Things without data analytics is like using your ATM card but never looking at your bank balance. Whered the money go? When is any money back coming in? How do bills get paid? How can you save up for a vacation or emergency? Sure, some people might live like that, but its no way to run a business.
As more devices talk to each other over the Internet and more machines are connected via networks, more data is generated about consumers and business. Were awash in Big Data, but only 13% of enterprises are using analytics to predict business outcomes and only 16% are using analytics to optimize their processes and strategies, according to an Accenture/GE study. M2M analytics applications are at the crux of the connected business evolution, and as Accentures Managing Director for DigitalMobility, Abhijit Kabra, explains, To truly realize the value of the IoT, they [enterprises] will need to make the data being collected accessible and useful.
Analytics for IoT/M2M Deployments
Much like checking in on your bank account status when you use your ATM card, analyzing the data from your IoT-connected devices lets you be proactive in your operations and decision making. You need to see what is happening with your M2M connected devices, when theyre up or down, and if somethings going wrong, so you can prevent any problems from reoccurring. An IoT analytics application would need to provide alerts, anomaly detection, and predictions for relevant debugging and actionable information so you can better manage your IoT/M2M device deployment.
At the systems level, IoT data analysis tells you how your devices and your network are working together. When an enterprise has thousands of machines in the field generating gigabytes of data, sifting through raw reports would be tedious. Ideally, youd be able to depend on real-time deployment monitoring applications to troubleshoot problems and identify erratic behavior. As Bill Roberts, Director in the Global Manufacturing and Supply Chain Practice at SAS, points out, Advanced analytics can drive significant value by helping organizations greatly reduce the time and effort involved in identifying and resolving product defects that make it to the field.
Control Costs With IoT Analytics
Analytics applications can also be useful for controlling costs in device deployment over the lifetime of a product. For example, if you can track device use patterns (such as roaming), manage data usage rates, and set billing thresholds, you can more effectively manage the expenses associated with maintaining devices on an IoT/M2M network. Visibility and insight into your IoT data are essential for making better business decisions in a constantly changing environment. According to Andreas Mai, Director of Smart Connected Vehicles for Cisco Systems, successful IoT enterprises need the right infrastructure to support this level of Big Data and analytics. And if they dont factor IoT analytics into the equation, Mai believes, They will fall behind.
So are you keeping tabs on your bank account as you use that ATM card? Likewise, what kind of IoT analytics are you using to understand the data your devices generate?