In my last post, I wrote about the fast-growing areas of machine-to-machine (M2M) technology and the Internet of Things (IoT). In this post, we move beyond the headlines and into the nitty-gritty of M2M. Two key challenges and opportunities in M2M are security and predictive analytics. The first of these – security – is growing in importance as M2M makes its way into critical applications. After all, using M2M to remotely turn off the kitchen light you forgot is one thing; using it to run a water treatment plant, a power grid or an automobile is another. Here are a few of the security questions that arise in M2M:
- How do devices know they can trust one another without constantly entering logins and passwords?
- What kind of messages are moving among these devices?
- How deep into the enterprise will these connections reach?
- How do you keep thousands or millions of M2M connections secure?
Understanding security trends in M2M
The biggest challenge to M2M security is yet to come. A report from Frost & Sullivan points out that there is security built into both the communications network and the hardware used in M2M, such as chips, SIM cards, and modules. But as trends like mobility, cloud computing and big data pull M2M deployments closer to internal business systems, risk increases. To develop for the Internet of Things and get the most value out of it, companies will have to connect internal and external networks and move data among them, and that requires more security.
Consumers are mostly concerned that their information will be compromised, according to ISACA’s 2013 IT Risk/Reward Barometer. Business and IT are not blind to security issues but 41 percent of IT professionals believe the benefits of IoT outweigh the risk for enterprises.
By the way, none of this is lost on the government: the Federal Trade Commission is exploring consumer privacy and security issues posed by the growing connectivity of devices and IoT.
Gaining foresight with M2M Analytics
What is the first thing businesses want to do once they’ve met their security needs and are pulling in vast amounts of data about machines and their environments? They’ll want to try to predict the future.
In the same way that businesses use data from finance, operations, supply chain, and sales to make better decisions, they will soon be able to base decisions on predictive analytics from M2M data.
Imagine making decisions that take into account the performance of machines like wind turbines, diesel engines, medical devices, and farm equipment. Better yet, make decisions that take into account what’s going on around the machines, like weather, workload, patient conditions, and moisture in the soil.
M2M combined with predictive analytics leads to what real-time intelligence company JackBe calls Machine-to-Enterprise (M2E). Sensor data rolls up into the dashboards, graphs, and maps that give companies the current status of their machines, then connects with other data sources so that managers can react to changing conditions without delay. In fact, as more companies learn about the value in these predictive analytics, M2M will spread to even more business cases, according to ABI Research.
Which M2M Security questions do you want answered? And how do you see M2M data shaping future business decisions?