This week I was working on a test recommendation report for a customer. This reminded me how important it is to have the required data available and ‘ready-to-go’ before undertaking any advanced analytics projects.
Understandably, most organizations want to jump into ‘big data’ without first realizing that the first step is to organize, catalog, and standardize data sets.
Machine learning and Artificial Intelligence is only as good as the underlying data it must learn from. Skipping this cumbersome, but necessary preparation is why many advanced analytics projects fail.
This is also why more than half of the report I was working on contained recommendations for data fields to store, storage frequency, naming conventions, and a common database for joining related, but disparate information sets.
Data quality and availability are the key!!
