M2M, Internet of Things, and Big Data

Storage, analysis, and action bring machine, Internet and big data closer. AT&T and IBM integrate data storage, analysis and action for cities and utilities.

by The AT&T Business Editorial Team

Amid all the talk about machine-to-machine (M2M) communications and the Internet of Things (IoT), the topic of Big Data has started coming up more often. What does Big Data have to do with M2M and IoT? How are these trends related?

The following three business imperatives are gradually brining M2M, IoT and Big Data closer together.

1. Storage

As more and more assets become connected, the amount of data they generate and send – GPS fix, fluid pressure, running time, temperature, blood sugar level, altitude, moisture, etc. – continues to grow. When the data comes into an organization, the first problem is to figure out where to store it securely.

As Frost & Sullivan points out, “The threat of network resources getting overwhelmed from this big data phenomenon is quite real.” At the moment, storage capacity in the public cloud is rising in capacity and falling in cost. Organizations with big data are waiting to see just how big their big data will get and are holding onto it in the short run without incurring serious capital expenditure.

Of course, the organization’s storage problem goes away when it uses an outside service provider. The provider manages all the network connections, data storage, and security.

2. Analysis

The data is only as good as the decisions a company can make with it, so the next step is to choose tools for putting the data in front of decision makers in a format that makes sense to them. Analysis begins here, and this is where M2M blends into IoT. In fact, Syed Hosain of Aeris Communications points to this as the main difference between M2M and IoT: “M2M is the correlation and synergy of information from [connections]. But it’s the application of the information, in order to make actionable business decisions, that really makes up the IoT.”

This requires up-front filtering to find the needle in the haystack of data from so many connected sensors, devices, and machines.

3. Action

Organizations are used to having an endless stream of data, and business intelligence (BI) has evolved to make that data useful. The kicker with M2M and IoT is that the real-time nature of the data demands quick – and I mean quick – action. Sure, you need to act fast when you see a spike in last month’s sales, but you need to act a lot faster when you see a sudden spike in temperature on an oil rig.

Getting to the needle in the haystack

As the business imperatives of storage, analysis, and action gradually bringing M2M, IoT, and Big Data together, the alliance with IBM that Mobeen Khan mentioned in his recent post is our latest move in that direction.

We’re starting with solutions for two verticals: city governments and large utilities. They sit squarely among the physical industries that will derive the greatest benefit from IoT because they maintain assets like mass transit vehicles, meters, traffic sensors, power substations, and treatment plants that generate data constantly.

Together with IBM, we’re putting in place all the moving parts needed for this storage, analysis, and action:

Our goal is to enable cities to evaluate patterns and trends for better urban planning and to give utilities the insight to reduce costs in managing their equipment. Stay tuned for more updates and announcements on how M2M, IoT and Big Data are coming together.

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