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.
SEE: The Power of IoT and Big Data (Tech Pro Research download)
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.
And then, deciding where are we going to send that data, who gets to see that data, how often, and how, is the third kind of big obstacle in terms of data management. Meaning, what is the policy we're going to put on this data, and who are we going to share that data with. For instance, you might want to share some of the data on the ABB robotics arm back to ABB, because you want ABB to be able to do remote monitoring and management of that robotic arm. Right? But you don't want to share everything back to ABB. You might only want to share the GPS rotation of that robotic arm. You might only want to share how many sessions that robotic arm has, you know, how often it turns on and off. But you don't want to share any information that might provide an inkling of what that robotic arm is doing in your factory. Right? That is one of the biggest challenges around data management.
Teena Maddox: Can you explain to me what the Cisco Kinetic solution is and how it helps?
Theresa Bui: Seven months ago, Cisco announced the Cisco Kinetic solution, which is distributed software that helps companies extract data from multiple device types, put all that data into one unified data model, and then put policy on that data, on those data streams, in terms of where they want to send that data, who gets to see, who has permission to see that data, and how often that data gets shared. And that is, I'll pause there, because that's what we mean by IoT Data Management. The ability to get the most value out of all of this data these connected devices now represent.
This is an overly simplified example, but in the world of car manufacturing, the number of layers of paint that you put on a specific car model could be considered proprietary to that car manufacturer. And it's a robot arm that's doing the spraying. Right? If a car manufacturer believes that six layers of paint on their car is proprietary, they want to make sure that when they're sharing information back to that robotic arm manufacturer, that they don't share any data that might give any indication of how much paint that robotic arm was spraying on a daily basis, or on a specific car. So, yeah, there's a data government aspect to that, as well.
And it ties into Cisco's larger IoT solutions. We have the Cisco Jasper Control Center which is all about managing the connectivity of a device. You couple that, now, with Cisco Kinetics, which is about the ability to, once those devices are connected, about extracting, and computing, and moving data from those devices. You add that to Cisco's networking capabilities, existing IoT networking solutions that help companies provision and manage their devices, the actual devices themselves, and you couple that, finally, with the fourth element of Cisco's security. Right? That protects IoT devices at the network level, all the way down to the device level.
It enables us to turn to our customers and say, "We have a really good end-to-end solution to help solve different aspects of your IoT deployment needs." From connecting the devices themselves, making sure they're secure, making sure that the network they're on is secure, and now, being able to mine data from your devices and move that data wherever you want it to go, and making sure that's secure. That's what we bring to bear in terms of the complete IoT solution set for our customers.