In 2012, the federal government announced $200 million in research for Big Data computing. Why? Each and every day an estimated 2.5 quintillion (that is 17 zeroes after 2.5) bytes of data are created, and nearly 90 percent of the world’s data has been created in the past two years, according to IBM. In the four years since the term Big Data was originally adopted within the IT community, the number of data sources has exploded, now including smart phones, social media posts, web interactions and much more. As a result, experts predict that by 2015, the global market for Big Data technology and services will swell to $16.9-billion, from just $3.2-billion in 2010. So what does that mean for retail? A lot!
Understanding Retail Big Data
Within the next two years, 30 percent of retailers will run out of storage capacity, according to The Edgell Knowledge Networks, and while nearly 80 percent are aware of the Retail Big Data concept, less than half understand the implications it has on their business. One immediate implication is clear, if retailers continue to ignore this issue they will be overrun by it.
Beyond the amount of data, the types and sources of the data are key to understanding and managing this growing stream of information. There are two types of data, structured and unstructured. Structured data is typically numbers and letters laid out in a consistent format that is readable and searchable by a computer or person. In retail, this data can come from the POS, alarms and access control, RFID and EAS, returns management, customer loyalty programs and much more. Unstructured data can be much larger, and can spawn an entirely new category of structured data. The primary form of unstructured retail data is video. But don’t be fooled, that single source is a huge data contributor. To provide some context, storing 30 days of footage from a high definition (HD) camera, running at 15 frames per second, requires nearly 1TB of space. And that’s just one camera!
With that being said, the Retail Big Data concept becomes a bit clearer, but it still lacks a formalized definition. Not anymore.
Retail Big Data (n.) – The collection of large amounts of (structured and unstructured) data from multiple, disparate, and often unrelated systems into a single repository to enable retailers to more efficiently and effectively understand their business and their customers in a timely manner.
Managing Retail Big Data
Armed with a definition and a thorough understanding of the Retail Big Data epidemic, it’s time to take action. Here are four steps to help retailers turn their Big Data headaches into Big Data profits.
One way retailers can combat Big Data growth is through cloud storage solutions. While some retailers may be hesitant to transition their entire system to cloud, due to the loss of data visibility, there is a middle ground that many retailers are taking advantage of – summary cloud storage. Many retailers are utilizing the cloud to store summary data from individual stores and enterprise headquarters for an enterprise-wide view of the retail chain. This approach allows retailers to retain their store-hosted data, to meet the individual store needs, yet also gain the larger bird’s eye view of integrated data in real-time, through cloud computing.
In one instance, a grocery chain’s inventory and TLog were not adding up following a recent bottled water promotion. With siloed data, the retailer would have needed to identify each of the item transactions, and then watch hours of surveillance footage to locate each bottled water scan. However, with a fully integrated system, a retailer can identify each of the bottled water transactions, and with the click of a button see the individual item being scanned. This technology allowed the grocer to uncover a major scanning issue. Rather than scanning the case of water that was on sale, the scanner read an individual bottle’s barcode, thus accounting for the inventory discrepancy and thousands of dollars in lost revenue.
For example, one client set an alert for drops in average department sales. Since sales in the produce department had dropped below the specified figure, the manager got an immediate alert sent to his phone and email. Rather than finding out a week later and searching for an answer, he was able to call the store manager and determine that the department manager hadn’t come in, so the department had never been fully stocked for the day. The managers were able to rectify the situation in real-time and prevent further revenue loss.
No matter how you look at retail data, one thing is clear – it’s on the rise. In fact, the world's total data is more than doubling every two years, according to a 2011 IDC study. Retailers cannot afford to wait. Now is the time to get a handle on Big Data and prepare your company for the data surge. Is your store ready?
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