Big Data has two implications on retail:
1. The retail industry, which has been traditionally good at capturing and using data, needs to up the game thanks to the increased flow of data. It has to familiarize itself with the challenges the torrent of data brings with it and the tools required in managing this data.
2. Given growing cross-channel data volumes, it must quickly learn to capture the right data for analysis. Failing to do this could mean slowing down decision-making, sometimes making the wrong decisions and often just paying a lot more to extract actionable information than is truly required.
Given that Big Data is going to be the next frontier for retail, I believe that the focus should be to ensure that adoption is based on the required output. It is easy to get lost in sourcing and capturing every single data point, in every available format, defining complex data standards and governance processes, building the IT infrastructure around this, appointing data stewards, storing the data, cleansing it, shipping it, securing it and mixing it with legacy data or publicly available data and then finding that the investment isn’t producing expected ROI.
A study by the McKinsey Global Institute said that retailers could improve operating margins by 60% using Big Data[iii]. But you don’t want the benefits eroded by over-investment or poor investment in the technology to enable the use of data.
This means retailers must first decide what capabilities, benefits or insights they want to unlock using Big Data. Do they want to optimize their marketing investments, reduce the cost of maintaining their online presence, increase their mobile dependency, understand consumer needs, provide the customer with better shopping experience, personalization, re-do the product mix, gain higher control over their supply chain, reduce cost of meeting regulatory requirements, etc? Each of these requirements will spell out the data requirement, how to source it and how/ when to analyze it.