In this special guest feature, Terry Kline, CIO of International Truck, Navistar’s commercial truck brand, discusses the overarching best practices that have enabled his company to drive strong business value from data and analytics. Terry Kline, International Truck’s CIO, is a 30-year veteran of the automotive and transportation industry, and has held a wide range of IT roles. Before assuming his current position, he was the Global Chief Information Officer for General Motors. Prior to his 12 years at GM, he spent 11 years with International Truck in multiple roles, including serving as director, Information Systems for Truck Engineering and Manufacturing at the company’s truck assembly plant in Springfield, Ohio.
While many companies today boast about the ability to utilize Big Data, the truth is that collecting data and analyzing it continue to be two very different things – and deriving real business value from that data is even more difficult. According to Forrester Research, while nearly 75 percent of enterprise architects say they aspire to be data-driven, fewer than 30 percent say that their firms are good at translating analytics into measurable business outcomes.
What best practices can be applied across industries to help IT and other teams go from simply collecting data to analyzing it effectively and, as a result, impacting the bottom line?
Mobility for Real-Time Relevance
One key precept is to utilize mobile applications for quality, as well as quantity, of data. In a world where the Internet of Things (IoT) enables enhanced automation and productivity, access to data in real time through connected devices can enable even greater improvements in speed and efficiency. The key is to sort data for its relevance to business outcomes – and mobile applications can both expand the universe of available data and add clarity to the picture by delivering the most relevant data in real time.
For example, in the healthcare industry, doctors who are making rounds in a hospital receive notifications on their smartphones containing the back-end data for each patient, based on what room they are closest to. This allows the staff to maximize the time spent treating patients, rather than searching for charts. Meanwhile, employees in the supply chain industry can optimize order fulfillment by using mobile data collection methods to assess inventory in real time. With immediate visibility into inventory, warehouses can reduce carrying costs and utilize space more intelligently.
Open Architecture for a Comprehensive View
Building on this approach, companies should consider an open architecture system. An open technology approach to analyzing data from multiple systems – regardless of the brand or owner – allows companies to draw a more complete picture of relevant data.
For example, today’s automotive industry practices indicate that to drive real business insights and value, cars must collect driver data from a variety of different sources, measuring everything from entertainment preferences to driving habits. With a more complete view of driver data – made possible by this open-source approach – auto makers can generate improvements and future models that more fully reflect the actual behaviors and preferences of drivers.
In a recent example from the defense industry, defense contractors are using solutions that allow real-time video to be streamed directly from an unmanned aircraft to the cockpit of a military helicopter. They use an open architecture approach, allowing the solution to be easily installed on all helicopter platforms across the military service. This allows the U.S. Army to gather critical data from each and every helicopter system.
Predictive Analytics for Prioritization
Building on the use of mobile data and open architecture systems is the added insight that can be obtained by leveraging predictive analytics. One classic example from the aviation industry is airlines’ use of both their own and third-party data to better understand seat-assignment and legroom preferences, how often customers fly and how price sensitive certain customers are, in order to anticipate what flight options consumers will consider most valuable. When combining predictive analytics with the use of mobile sources and open architecture, even greater real-time insights can be obtained.
In the trucking industry, for example, an unplanned equipment breakdown often results in costly downtime and expensive repairs. By using mobile telematics data to track vehicles’ health on a near-constant basis, truck fleets can be alerted to likely malfunctions on a given vehicle. By then using an open-architecture approach to pull data from multiple truck makes, fleet managers can stay abreast of their entire fleets through one portal. And by leveraging predictive analytics to assess the resulting data streams for their linkage to actual vehicle malfunctions, companies can predict when a given truck within their fleets will require maintenance, and prioritize repairs so they can be made during planned downtime – thus maximizing overall fleet productivity.
A review of these experiences in multiple industries suggests that with the Internet of Things offering compelling new vistas of machine functionality and productivity, companies can increasingly combine mobility, open technology and predictive analytics to turn big data into meaningful business outcomes.
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