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June 09, 2014

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www.joc.com THE JOURNAL OF COMMERCE 51 most obvious" benefi t of Big Data analytics is to improve "operational effi ciency" — that is, to "optimize resource consumption, and to improve process quality and performance." Although these goals have always been the key benefi ts of automated data processing, Big Data analytics provide "an enhanced set of capabilities." A second high-level benefi t for providers of logistics services is to improve "customer experience," the report says. Typically, that means increasing customer loyalty, perform- ing more precise customer segmentation and optimizing customer service. "Including the vast data resources of the public Internet, Big Data propels CRM (Customer Relationship Management) techniques to the next evolu- tionary stage," while enabling new business models to create additional revenue streams "from entirely new data products." More specif ically, in the log istics industry "Big Data analytics can provide competitive advantage because of fi ve dis- tinct properties," the report says: 1 Optimization of service properties, such as delivery time, resource utilization and geographical coverage. "The more precise the information is, the better the optimization results will become." 2 The delivery of tangible goods. "Big Data concepts provide versatile analytic means in order to generate valuable insight on consumer sentiment and product quality." 3 Seamless integration of logistics solutions into production and distribution processes. As a result, "logistics providers feel the heartbeat of individual businesses, vertical markets or regions." 4 Using the transportation and delivery network as a high- resolution source of data, to "provide valuable insight on the global fl ow of goods." This "moves the level of observation to a micro- economic viewpoint." 5 Leveraging data from local and decentralized operations. Processing information from a fl eet of vehicles moving across the country "creates a valuable zoom display for demographic, environmental and traffi c statistics." To address these complexities, GT Nexus connects all of the shippers onto its own network, which provides its own software to do the analytics that suit the specific needs of each cus- tomer. Many companies, however, still prefer to buy the technology and build their own networks, using tech- nology provided by SAP, Oracle and others. One problem with building your own network, Kefer argued, is that it takes years to do. Another issue is that some multinational companies "have 30 versions of SAP around the world, and they can't even talk with each other," he said. Connecting with the GT Nexus network automatically links users with thousands of other compa- nies, including their own suppliers, carriers and third-party logistics companies. Scott Sangster, a Descartes vice presi- dent, said that if a member of its Global Logistics Network wants to take advantage of Big Data analytics, it can import the infor- mation it needs from the GLN and merge it with information from other Internet ser- vices, and from various kinds of software for business analytics. "Some companies are starting to do some analytics themselves, internally, and some are outsourcing" such analytics to outside specialists. "A lot of the sources of this data come from the GLN," Sangster explained. "Big Data involves gathering not only your own information, but real-time data from multiple sources." Companies need to prepare for the likelihood that their va rious internal departments will have different perspec- tives about how a nd when to use Big Data. IT managers often focus on using Big Data to "help move more informa- tion faster and help managers derive insights within the decision window," said Peter Krensky, Aberdeen Group's senior research associate for a n a l y t ic s a nd bu si ne s s intelligence. But the operational man- agers who actually use the data are 40 percent more likely "to be focused on the ability to incorporate data sources like social media and customer feedback into their Big Data initiatives," he said. This kind of "unstructured" information is typi- cally text-heav y but not organized in a pre-defined m a n n e r t h a t wou ld m a ke it easier to process with business intelli- gence software. Nevertheless, this kind of data "can offer perspectives on an organization's prod- ucts that traditional data sources cannot," Krensky said. "Combining structured and unstructured data offers users optimal vis- ibility to understand the business and take intelligent action." Overall, making a determination about what kinds of Big Data qualify for analysis can be something "very subjective," Sang- ster said. "What sources are relevant to you? And how do you weigh those different sources?" For example, a company whose supply chains pass through regions with a high probability of hurricanes or tornadoes — or other major weather events — will want to put greater weight on data about such "acts of God." And a company that depends on feedback from its customers to build its brand and generate repeat sales may decide that social media can play a signifi cant role in its mix of Big Data sources — even in the hard-nosed world of transportation and logistics. JOC Contact Alan M. Field at alanmf0@gmail.com. "THE FIRST AND MOST OBVIOUS" BENEFIT OF BIG DATA ANALYTICS IS TO IMPROVE "OPERATIONAL EFFICIENCY." "BIG DATA INVOLVES GATHERING NOT ONLY YOUR OWN INFORMATION, BUT REAL-TIME DATA FROM MULTIPLE SOURCES." 01001100 01101001 01101011 01100101 00100000 01101111 01110100 01101000 01100101 01110010 00100000 01100110 01100001 01110011 01101000 01101001 01101111 01101110 01100001 01100010 01101100 01100101 00100000 01100010 01110101 01111010 01111010 01110111 01101111 01110010 01100100 01110011 00100000 01101001 01101110 00100000 01110100 01101000 01100101 00100000 01110000 01100001 01110011 01110100 00101100 00100000 01110100 01101000 01100101 00100000 01100011 01100001 01110100 01100011 01101000 01110000 01101000 01110010 01100001 01110011 01100101 00100000 011100010 010000000 010011100 01000010 01101001 01100111 00100000 01000100 01100001 01110100 01100001 011100010 010000000 010011101 00100000 01101000 01100001 01110011 00100000 01100111 40 % agers who actually use the data are 40 percent more Overall, making a determination about what kinds of Big Data qualify for analysis can be something "very subjective," Sang- unstructured data offers users optimal vis- ibility to understand the business and take ucts that traditional data sources cannot," Krensky said. 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