Analytics in a Fragmented Data Landscape

The idea of a product recommendation engine is simple—count sales per customer and product and fill this in a matrix. Add correlated dimensions to the data, such as the customer’s industry sector, geography, and size. Train a model to learn different rules, like “customers buying three or more CCTV cameras are likely to subscribe to a security service contract.” Finally, predict the likelihood of blanks in the matrix to identify promising opportunities.

What if the data landscape in our company is fragmented? Products and services reside in different ERPs, separated from the CRM containing the customer contact details we need to run marketing ...