At one point analytics meant MIS and MIS meant OLAP Cubes. For the uninitiated, OLAP is online analytics processing and OLAP cubes are aggregated cuts of various business KPIs across different time and business factor dimensions. A simple example could be sales across different products, geographies and different months.

Storing data in cubes meant easy access to analysis and MIS. Reports can be generated on the fly out of the aggregated data. OLAP cubes reduce the size of data for analysis as only aggregated data is stored instead of raw transnational data. This in part lead to the widespread use of MIS.

A parallel development in the field of analytics is the evolution of self service tools like tableau, that helped the analytics person (if not the business user) to build and publish dashboards out of raw data or OLAP cubes. This further fuelled boom in business analytics.

As computing power increased and the size of RAM increased, both of these approaches i.e. (OLAP Cubes) and preset dashboards are becoming redundant. More so the former. The new paradigm is self service, real time analytics on raw transnational data using in memory tools. Queries are directly run on raw data to give meaningful insights and discoveries. This is possible because of cheap hardware and smarter data processing software. Now there is no need for aggregating and storing data cuts. Everything can happen realtime. Further more, this new form of analytics can also be consumed by other software (like CRM, campaign manager etc.) through simple APIs.

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