1. A computer-implemented method of data management, comprising: recognizing of a natural structure, relationship, association and correlations within a data present in a given environment as a content data, wherein the content data is a combination of an atomized data, a reconfigured data and a new data, wherein the data management is an enterprise data management; capturing the content data and a context of the content data and a relevant external data residing as at least one of an internal data and relevant external data; running a layered asynchronous context enrichment software on a machine-readable medium on the content data, internal data and relevant external data present in the given environment to transform as an atomized grain by ingesting the content data, internal data and relevant external data present in the given environment and atomizing at a lower and higher level for preliminary correlation; evaluating and enriching the atomized grain dynamically and asynchronously based on a contextual content and adding value by correlating the atomized grain to create an atomized and correlated data; segmenting the content data, relevant data and atomized and correlated data based on predefined rules for security and dividing them into a sensitive and non-sensitive data; storing the content data, relevant external data and atomized and correlated data, sensitive data and non-sensitive data in at least one of a same location and different location in a data storage; retrieving a reconfigured data from the database to present to a user as the new data; dynamically annotating using layered asynchronous context enrichment software on the new data and presenting to the user based on a budget and rule constraint; and presenting the new data to the user after meeting the budget and rule constraint to the user.