How companies can monetize big data with IoT data control Posted on : Feb 28 - 2018

The oil and gas industry, auto manufacturers and more can maximize the value of IoT deployments with IoT data control. A Cisco exec explains how to manage data control and why it's necessary.

IoT data control is a key way that companies can maximize the value of their Internet of Things deployments. It allows companies to mine the data that IoT devices generate and monetize the information.

TechRepublic Senior Writer Teena Maddox talked to Cisco's Director of IoT Strategy, Theresa Bui, to dig deeper into this topic.

Teena Maddox: Can you explain what IoT data control is?

Theresa Bui: IoT Data Control means the ability for companies to maximize the value of their IoT deployments. For most companies, one of their biggest challenges is mining the data that these IoT devices are generating and making it usable. We hear from our customers that they feel like they're only using a fraction of the data that they collect.

A good example is in the oil and gas industry, where the data from sensors in one oil rig will generate 12 petabytes of data a month. It's a lot of data. It's a lot of data to be stored. It's a lot of data to be moved. But that's a more extreme example.

The big problem that companies have is when they say, "Okay, we've got all of these devices connected, they're up and running, they're sending and receiving data. How do we utilize that data in the most cost-effective way, and in a way that will give us the biggest ROI? And what are the challenges that keep us from doing that?" And one of the first challenges is actually extracting the data.

Imagine you are a factory owner. On your factory floor, you've got robotic arms from ABB. You've got an assembly line made by Siemens. You've got fan belts made by GE. All of those devices are connected, they're sending and receiving data, but they're made by different device manufacturers. And guess what? They each have a different data model. And you have to extract the data from them in a different way, number one.

And number two, you've got to make that data useful. You want to compare and contrast the data from the robotic arm, to the assembly line, to the fan belt. If the assembly line is over-rotating or moving too fast, you want to know that, so you can slow down the robotic arm that's working on it. Right? Extracting the data from those devices and getting those three different data formats into one unified model, so you can do the compare and contrast, is hard. View More