• Big Data
  • Michael Riemer
  • JAN 23, 2018

Turning big data into deep data

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The big data explosion has presented companies with an interesting juxtaposition. Organizations possess an enormous amount of information upon which to base better business decisions, but they’re challenged with being able to share and interpret this data—to make it actionable—in order to reach effective outcomes.

We’re seeing this play out in the transportation industry, which is using telematics data from connected sensors to enhance fleet operations. For example, UPS is using telematics to turn their trucks into “rolling laboratories” that provide a wealth of deep data, enabling the organization to gain valuable insight into the overall performance of their vehicles—everything from mileage to fuel consumption to engine hours and beyond. Collectively, this data can be used to accurately assess wear-and-tear and enhance preventative maintenance schedules.

Telematics data can also be used to warn drivers and fleet managers about problems through diagnostic trouble codes (DTCs) that are generated from various onboard electro-mechanical devices, including engines, refrigeration units, and other connected components. DTC information can be aggregated, analyzed and prioritized by severity level to help drivers, managers, mechanics and others involved in the maintenance and repair process to better understand the issues they may be facing. 

However, making this information actionable—enabling access to everyone involved in the service supply chain to quickly address a mechanical problem—can be an onerous process. A typical scenario includes numerous phone calls, emails, and lost time looking for required paperwork and other critical information. To ensure effective and efficient servicing of an asset, key information such as diagnostics and service history must be shared with drivers, fleet managers, maintenance technicians, service providers, and equipment manufacturers, to name a few.

Integrating telematics data with a closed-loop service-relationship-management solution is a more effective approach. SRM turns raw data into actionable and intelligent information that can be quickly shared with all members of the service supply chain. SRM data resides in the cloud, making it accessible to everyone via any device and from any location. This process eliminates the need for time-consuming paperwork, emails, or phone calls, which greatly reduces triage time and eliminates repair delays to ensure faster return to service.   

Although telematics is inherent to the transportation industry, the same principles of combining sensor data with SRM can pertain to any commercial, industrial or manufacturing asset. Organizations with these assets can take a cue from transportation companies, which have begun successfully turning their big data into deep and highly actionable data, which allows them to keep their operations running efficiently.

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