The Path to Predictive Analytics and Machine Learning

Chapter 2. Processing Transactions and Analytics in a Single Database

Historically, businesses have separated operations from analytics both conceptually and practically. Although every large company likely employs one or more “operations analysts,” generally these individuals produce reports and recommendations to be implemented by others, in future weeks and months, to optimize business operations. For instance, an analyst at a shipping company might detect trends correlating to departure time and total travel times. The analyst might offer the recommendation that the business should shift its delivery schedule forward by an hour to avoid traffic. To borrow a term from computer science, this kind of analysis occurs asynchronously relative to day-to-day operations. If the analyst calls in sick one day before finishing her report, the trucks still hit the road and the deliveries still happen at the normal time. What happens in the warehouses and on the roads that day is not tied to the outcome of any predictive model. It is not until someone reads the analyst’s report and issues a company-wide memo that deliveries are to start one hour earlier that the results of the analysis trickle down to day-to-day operations.

Legacy data processing paradigms further entrench this separation between operations and analytics. Historically, limitations in both software and hardware necessitated the separation of transaction processing (INSERTs, UPDATEs, and DELETEs) from analytical data processing ...