Thought Leaders in Big Data: Interview with Dale Skeen, CTO of Vitria (Part 4)

Sramana Mitra: Would you like to do a few more use cases?

Dale Skeen: The second thing I wanted to talk about is a customer, who for competitive reasons doesn’t want to be named. It is a large telecommunications carrier that had sensor networks, which I mentioned before. Most mobile carriers would like to give the best service to their best customers and give better service to all their customers. But to do that, they have to manage their cellular networks and be able to [understand] the faults and problems that occur with them. Then they have to prioritize, act on, and mitigate problems.

These networks are huge – hundreds of thousands of cell towers. These towers produce large streams of what we call big data in motion – hundreds of thousands of readings per second informing the carrier about things that go right and things that go wrong. The challenge they have with this tsunami of big data coming at them is being able to analyze it in real time, being able to connect that information to customers and the customer experience. Which customers are having problems? What is financial relevance of the customer to them? And so on.

Then they have to prioritize their actions: the best customers get the best service, and they also want to improve service for all customers. What we were able to do is connect to these big data streams coming off their cellular networks, correlate this information in real time back to business-level data – for example, customer data from a CRM system – and correlate this data back to their device databases, so they would know what type of device a customer was using. This provided them with insight into who was being affected by which events. We then went further and provided a framework in which they could automate actions when certain problems occurred and prioritize these actions based on the relevance of the customer who was being affected. By doing this, they were able to provide a better level of service to all customers.

SM: OK.

DS: A third example is American Electric Power, which is one of the largest distributors of electricity in the U.S. They are located in Ohio. Their problem is monitoring their smart grid. Now, they have a large penetration of smart meters among households. Of course, these meters provide many advantages, but there are two new threats. First of all, there are new ways of fraud in smart meters. You have people fraudulently trying to buy electricity. That can be detected. The second thing, which is of even greater concern, is cyber security threats. When you had dumb meters in a dumb grid, you didn’t worry about cyber security. But you do need to worry about it with smart grid and smart meters. Moreover, there are new requirements concerning the ability to track threats across multiple grids.

These are new problems that we as an industry are trying to get our hands on to solve. Traditional cyber security for regular networks is not enough for a smart grid. What we were able to do with our software was to install an oversight capability over the smart grid that would look at fraudulent behavior, such as intrusion alerts or other types of threats within the smart grid, detect anomalies from what we consider correct behavior, and track threats across multiple networks: we see an attack originating from one region or network, and then we see the same hacker or the same IP address trying to attack different networks. We can now track this activity across networks so we can see the extent and the result of these attacks in real time and take counter measures. In this case, we gave American Electric Power better security for their smart grid.

This segment is part 4 in the series : Thought Leaders in Big Data: Interview with Dale Skeen, CTO of Vitria
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