Last week we covered how automation can improve the analytics workflow by identifying correlative and predictive relationships in single data sets to accelerate data discovery. This week we’re looking at expanding that workflow to other data sources for Enterprise Data Discovery.

The next concept we’re covering is the possibility of creating even more value by identifying relationships from many different data sources. We showed how EmcienScan automatically detects predictability across variables in a single data set, but it can also be connected directly to your data sources like relational databases, distributed storage frameworks like Hadoop, and others, that allow views across many different tables.

This is the concept of Enterprise Data Discovery, the ability to identify predictive relationships across all the data in the enterprise. The views that are created in these different systems, even from messy data lakes, can be easily connected to EmcienScan for point and click data discovery. 

Integrating the predictive capabilities of EmcienScan into your data stack brings about a unique advantage, opening up new opportunities for discovery across very wide data mashups, creating more value from already available data. Whether you are using a traditional data storage or more modern distributed systems like Hadoop, direct connections to a data source makes it possible to see predictive relationships across tables and even across silos before doing any data cleansing.

Where the traditional approach to data analysis could be slowed by exploring and joining data, these built-in connections help users quickly discover connections across any data they can join together. Scanning these different kinds of data together in a single view lets users discover even more value from relationships across all of the data across the enterprise. 

With EmcienScan, Enterprise Data discovery becomes a reality, letting users discover new connections and predictive relationships across previously siloed data. The result of this change in the data workflow is a dramatic reduction in the time and effort that it takes to conduct data discovery, a significant increase in your ability to turn enterprise-wide data into value, and more time spent in analysis.