Analytics is a systemic way of discovering data for aligned or non-aligned patterns and presenting it in a visualized manner that the human mind cannot miss the apparent trends that presentation is highlighting. The summarised highlights of data presented in charts and graphs pave the way for easy understanding. Analytics became popular right since accumulatio of business data coupled with the introduction of specialist analytics tools in the marketplace that claimed exclusive data discovery familiarity.

As these tools became popular, we noticed corporate houses performing various analytics for top management by way of relevant charts. This kindled human reaction to those visualization and thus was born data-driven decision making. Senior management makes strategic decisions based on data visualization presented to them. However, in the early days of these tools, the analytics visualization was restricted to senior management, and scarcely, the operational team had access to these analytics platforms. Dashboards were not used by the operational team, least to create it by themselves for their own data analytic needs. However, all the analytics were batch driven then, not providing helpful insights on a real-time basis. It is like the report card of our children which we get every quarter from the school to know what scores they have got and being blank until the end of the quarter to see how the child is progressing in studies.

With the advent of the Millennial and Gen Z workforce, the appetite for self-service and drive for making decisions on their own gained momentum. Their independent cum collaborative working styles required that they own their data, network about their thoughts on data analytics in close circles, and pursue actions from their consideration. Self-service analytics and on-demand analytics came into play to their advantage. On-demand analytics is not possible in batch mode, and the absence of embedded analytics was an apparent frustration to them. All the frontline analytics vendors rushed in to fill the gap with connectors that can export data almost in real-time to provide real-time analytics. Some created common platforms to connect reputed ERP, and other popular cloud software and others provided a generic model of configuring connectivity between systems to pull data almost instantaneously. The Embedded Analytics became the basic norm, not even as a unique feature beyond the primary offering.