5 Hurdles Companies Can Overcome to Optimize Big Data

With social networks, sensors, cameras and smart phones, people are now walking data generators. 

Morgan Swink

More data crosses the Internet every second than was stored in the entire Internet 20 years ago, said Morgan Swink, the Eunice and James West Chair of Supply Chain Management and director of the Supply and Value Chain Center at the Neeley School of Business. 

Big data refers to the massive amounts of information captured every second by point-of-sale systems, sensors, web server logs, Internet clicks, social media activity reports and smart phone records. This avalanche of data requires new methods of transforming all that information into something useful.

“Big data analytics gives us the tools to examine [information] using advanced technologies to provide data management, open-source programming, statistical analysis, visualization tools, in-memory computing and more,” Swink said.

More than simply gathering information, analytics turns big data into a powerful tool for forecasting, improving logistics, optimizing inventory, designing networks and improving warehouse operations. Both large and small companies use big data, and business-to-customer industries, such as restaurants, clothing stores and video game companies, are among the major users. 

Yet big data is largely going untapped. Why? Swink outlined five major hurdles:  

Lack of Vision

Companies thriving on big data start with a targeted approach that delivers marketing tailored toward individual consumers.

Swink cited a major retailer as an example. “Sears uses big data to influence its pricing strategy. At one time [the company] followed a nationwide pricing strategy, which was then reduced to a regional pricing strategy,” he said. “Today, with approximately 4,000 stores and more than 100 million customers delivering a stream of big data, Sears’ objective is to deliver personalized pricing and offers.” 

Similarly, Swink pointed to another major retailer that uses big data for its mobile marketing strategy. “Customers who use the Wal-Mart app spend 40 percent more per month at Wal-Mart than customers who do not.” 

Gibu Thomas, global head of Wal-Mart’s Mobile Division, said the company is leveraging big data to develop predictive capabilities to automatically generate a shopping list for customers based on what they and others purchase each week. 

Lack of Access to Data

Swink said that, to make real impacts, big data analytics must deliver precise information, available almost immediately, in usable formats that generate visible results.

 “In the supply chain, we already have several measured elements of visibility,” he said. “From customers, we can derive, share and track sales, demand forecasts, inventory levels and promotional plans. From suppliers, we can monitor inventory levels, lead times, delivery and advanced shipment notices, as well as check on the status and location of finished goods.”

But how accurate is this data? How timely is it? And does it come to users in an easily used format? 

Lack of IT Infrastructure

Swink said only about 25 percent of big data users say they have the mobile technology capabilities needed to deploy the information. And that’s among the heavy users. Among the low users of big data, the percentage drops below 20 percent. 

Among high users, they remotely track assets, check process status and conduct operational transactions. The top hindrance to low users is the lack of ability to conduct remote operational transactions. 
“Acting on big data requires execution technologies,” Swink said. Those companies maximizing the possibilities of big data employ transportation and warehouse management systems, enterprise resource planning, advanced forecasting, and supplier and customer relationship management. 

Lack of Analytics 

Capabilities Swink said consumers who use big data regularly deploy dashboard applications, data visualization techniques and advanced analytical techniques that combine and integrate information. 

“Low users have the most competency in data visualization techniques, but they tend to lack systems that automatically make operational changes,” he said.  

Lack of Organizational Structure  

The effectiveness of big data and data science is moderated by domain knowledge, Swink said. “A company must not only collect information, it must decide how to best use it.” 

For collected information to be useful for decision-making, it must be available to managers who have relevant business knowledge. “So, a key to the effective use of big data is the company’s level of internal [cross-functional] integration,” Swink said. “Internal integration allows the information to flow quickly to the right decision-maker and aligns the information needs of the company with the business processes.”

Internal integration is vital to sharing and using big data. 

“Companies should ensure that functional teams are aware of each other’s responsibilities, goals, metrics and data sources,” Swink said. “They should develop integrated planning and a common prioritization of customers’ needs across functional teams. And they should facilitate the regular exchange of operational and tactical information between functional teams.”  

Big Data is for the Taking.

Even with the above challenges, companies have plenty of time to stake out a leadership position for a competitive advantage.  “Success requires a clear vision and business case coupled with complementary assets, such as a supporting technological infrastructure … analytics capabilities and supporting integrated organizational structure,” Swink said.

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