Wal-Mart, a retail giant, handles more than 1m customer transactions every hour, feeding databases estimated at more than 2.5 petabytes—the equivalent of 167 times the books in America’s Library of Congress.
Only 5% of the information that is created is “structured”, meaning it comes in a standard format of words or numbers that can be read by computers. The rest are things like photos and phone calls which are less easily retrievable and usable. But this is changing as content on the web is increasingly “tagged”, and facial-recognition and voice-recognition software can identify people and words in digital files.
And this is creating a situation of “Analysis is paralysis”. It is leaving marketer’s confused about what data is valuable & what is pure noise.
Seth Godin put it simply in a recent post: Too much data leads to not enough belief.
Luckily in emerging markets the challenges are somewhat different:
- Yes data is growing rapidly. But a lot of businesses have not focussed on how they can convert data into information and then into knowledge.
- Huge opportunity exists to just create a simple “customer one view” and collate information at a customer level. A retailer could look at how an individual customer is shopping, what SKU’s does she buy and when does she shop. And then put it together with payment data –did she pay by debit /credit card or by cash.
- Retailers can then look at how simple data analysis can help build business. Some years ago as a Retailer, I had the opportunity of executing simple campaign experiments on loyalty program data. We sent a simple letter, from the store manager, to customers who had not shopped with the store for more than 6 months and who lived within a 5 km radius of the store. The campaign did wonders and got back many customers to stores across India.
Have a look at this interesting article about this issue