Big Data set to hit the global equipment finance industry

Big Data is making significant inroads in corporate decision-making today, leading to profitable results for many businesses in many industries.

As a result of mounting interest in Big Data within the equipment leasing and finance industry, members of the Equipment Leasing & Finance Foundation believed it would be useful to explore the concept of Big Data and provide an application roadmap that could be used by businesses in the industry.

The result is: “Big Data: A Study for the Equipment Finance Industry”  –  a forward-looking study published by the Foundation in conjunction with Genpact.

Big Data in equipment finance companies is driven by the implementation of predictive analytics, which analyzes large amounts of data to help firms better manage risk, improve operations, increase profitability, and make more insightful decisions.

Tapping these capabilities with Big Data can enable equipment leasing and finance firms to excel in a variety of ways, including:
• increasing shareholder value;
• increasing customer satisfaction;
• evaluating potential new market opportunities;
• developing new products and services; and
• staying ahead of competition.

As an example of Big Data in action in the asset finance sector the Foundation quotes an article recently published in Bloomberg BusinessWeek: GE’s Billion-Dollar Bet on Big Data

General Electric was reported to be investing $1bn in a facility in San Ramon, California, that will be staffed with as many as 400 people. Bill Ruh, the man running the venture, was lured away from Cisco Systems in 2011 to join GE.

In the article, Ruh explained that he wanted to tie Big Data in with some of GE’s biggest businesses. He saw an opportunity to help airlines that buy GE jet engines monitor their performance and anticipate maintenance needs, reducing costly flight cancellations.

The technology could also help companies that lease commercial vehicles from GE Capital to optimize delivery routes and provide early warning that a truck may need a trip to the repair shop.

“If I can begin to see that something is starting to deteriorate and get out there and fix it before it breaks, that’s a foundational change,” Ruh said. “In the end, what everybody wants is predictability.”

When it comes to Big Data, GE is apparently playing catch-up to IBM. The world’s biggest computer-services company is working with energy companies to extend the lives of oil and gas fields by improving oil recovery through analytics. IBM also is working with Vestas Wind Systems to find better locations for wind farms.

Newer entrants are jumping in as well. Splunk (SPLK), a San Francisco-based startup that just went public, says its customer rolls exceeded 3,700 as of the end of January.

Ruh stressed that GE is counting on its expertise making industrial equipment—from gas-fired electrical turbines to locomotives—to give it an advantage over rivals focused on exclusively providing data solutions. “If you don’t have deep expertise in how energy is distributed or generated, if you don’t understand how a power plant runs, you’re not really going to be able to build an analytical model and do much with it,” he said. “We have deep insight into several very specific areas. And that’s where we’re staying focused.”

The study further defines Big Data for the leasing sector and how it can be profitably be applied to the industry.

Copies of Big Data: A Study for the Equipment Finance Industry can be acquired from:
http://www.store.leasefoundation.org/cgi-bin/msascartdll.dll/ProductInfo?productcd=BigData