All retailer with active big data efforts are analyzing transactions (100 percent) and two out of three use log data (67 percent).
This is machine-generated data produced to record the details of every operational transaction and automated function performed within the retailers’ business or information systems – data that has outgrown the ability to be stored and analyzed by many traditional systems. As a result, in many cases this data has been collected for years, but not analyzed.
Retailers are focusing initial big data efforts on transactional and log data, both key internal sources, as well as data that comes from their point-of-sale systems and supply chain Integration.
Retailers were more likely than global cross-industry respondents to use RFID scans and point of sale (POS) data (57 percent of retailers versus 41 percent of global respondents).
This is a natural area for retailers to lead given their history and dependence on POS data for transactions. Unlike other Industries, retailers severely lagged in analyzing free-form text data.
Examining those retailers engaged in big data activities reveals that they start with a strong core of analytics capabilities designed to address structured data, such as basic queries, data visualization, predictive modeling and data mining (see Figure 5).
Retailers were on par with other industries in their use of simulation, natural language text analytics, geospatial analytics, streaming analytics, and video and voice analytics.
But, when details survey was done, it was cleared that most of retailers are not well equipped for unstructured data. Which is almost 80% & come from various electronics media.
Hence Big Data had the real potential for the retail sector growth in next level .