4 Pillars of Analytics

There are four key pillars that rule the lifecycle of analytics projects, from data acquisition, processing to surfacing and actioning on the data. Each of these contribute to a significant part of the value chain of analytics.

#Pillar 1: Acquisition

The data acquisition pillar consists of a wide range of tasks, system and technology knowledge, one needs to possessed in order to be effective in acquiring the required data. What is required from analytics professional is very domain dependent.

#Pillar 2: Processing

The data processing pillar is responsible to transform raw data and refine it into informative data. It consist of different sub tasks that need to be processed on datasets, cleansing, combining and structuring the datasets, handling aggregation as well as performing any additional advanced analytics processing on top of the data.

#Pillar 3: Surfacing

Informative data needs to be surfaced in an effective manner to be meaningful. Different methods of data surfacing exists ranging from making data available into a dashboard or standard report, an analysis deck an OLAP cube or just opening data as a service.

#Pillar 4: Action

We can sometimes see analytics being divided within 3 separate subdomain Descriptive, Predictive and Prescriptive Analytics. This separation is in my view quite restrictive, analytics to be useful should be prescriptive but it can use statistical or modeling techniques that are descriptive or predictive for instance.