Managing that type of network and gaining insights via analytics from such a network is a newer challenge. It's what GE Transportation is doing with its network of devices to track train locomotives. GE Transportation leader Garret Fitzgerald joined SAS Chief Marketing Officer Randy Guard on the main stage at the SAS Analytics Experience event in Washington this week to talk about the challenges and benefits that the Internet of Things presents. (SAS is sponsor of the AllAnalytics site.) The use case Fitzgerald discussed is really a classic one for the IoT and analytics at the edge.
That use case is also an example of something else -- what Randy Guard says is the emergence of the analytics economy.
"What really defines the analytics economy is the acceptance and pervasiveness of embedded analytics," he said in his presentation this week. "In this new economy each insight sparks the next insight and then these insights compound, just like our investments."
Guard said that the key to the analytics economy are three things -- data, analytics, and collaboration. And collaboration includes both people and machines.
Fitzgerald spelled out a use case for this new economy by describing GE Transportation's work to create IoT, management, and analytics solutions for its locomotive customers.
Fitzgerald started by sharing some data points. In North America alone there are 26,000 locomotives pulling freight. The longest running locomotive in the world is now over 7 kilometers long and can pull 100,000 tons of freight.
Assets like these locomotives can now be equipped with sensors that generate data. But they travel in and out of range of the data network.
"This is not an environment like your data center where it is temperature and humidity controlled at a fixed location," Fitzgerald said. "So as we think about deploying analytics to the edge, we are not just thinking about the outcomes we are trying to achieve. We are also thinking about how to do it in an effective way."
Fitzgerald said that there are two decision points to consider when it comes to determining whether to run analytics at a centralized data center or at the edge. The most important is data availability.
"Mobile assets are constantly moving in and out of coms," he said. "It just wouldn't be feasible to stream all that analytics off board. So you run your analytics at the edge, make decisions, and then send that alert back."
The second decision point hinges on where you need to take action. If you need action to happen in close to real time, then you need those analytics at the edge.
"We are trying to do the same as automotive, go to drone trains, go to autonomous train operation," Fitzgerald said. "Especially when you are talking about things like autonomous train operations you need the decisions made at the edge. You need the insights acted upon by the assets at the edge."
Another application from these solutions can come in the form of fuel cost analysis leading to cost reductions, according to Fitzgerald. He said that railroads spend between $10 and $14 billion a year to fuel locomotives.
"You can think of how even a 1% improvement in efficiency, driven through IoT and edge analytics, could dramatically impact profitability."
Fitzgerald's use case is just one example of technology that can be applied across many different industries, according to Guard.
"The analytics economy will bring a new era of change," he said. "It's incremental in some cases, but it's also very transformative. It's across the board from technology, to business process, to new aspects of industry that we haven't seen yet."