The rise of big data is very much driven by a craving for getting more insight on your (prospective) customers. However, the coin has a (better) flip side.
Looking at it from the other side
As a customer, we will strike back. We do not need to be told what to buy. But we do want to know what we are buying. This means we want to be able to see rich product information when making a self-service purchase. This subject was examined in the post You Must Supplement Customer Insight with Rich Product Data.
Many companies who are involved in selling to private and business customers are ramping up maintenance of product data by implementing inhouse Product Information Management (PIM) solutions as told in yesterday’s guest post on this blog. The article is called The Relation of PIM to Retail Success.
One further challenge is that you have to get product information from the source, usually being the manufacturers.
Big data approaches work for both
As data lakes are used to being the place to harvest customer insight, the data lake concept can be the approach to provide product insight to end customers as well.
The problem with having product data flowing from manufacturers to distributors and merchants is that everyone does not use the same standard, format, structure and taxonomy for product information.
The solution is a data lake shared by the business ecosystem. It is called Product Data Lake.
Liliendahl on Data Quality 