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Today’s enterprise organizations receive and process data from a variety of sources, including silos generated by web as well as mobile applications, social media, artificial intelligence solutions in addition to IoT sensors. That said, the efficient processing of this data at high volume in an enterprise setting is still a challenge for many organizations.
Typical challenges include issues such as the integration of mainframe data with real-time IoT messages and hierarchical documents.
One of such issues being that enterprise data is not clean and might have contradicting characteristics as well as interpretations. This poses a challenge for many processes such as when integrating customers from multiple source systems.
Though, data cleansing could be considered as a solution to this problem. However, what if different data cleansing rules should be applied to the incoming data set? For example, because the basic assumption for “a single version of the truth” doesn’t exist in most enterprises. While one department might have a clear understanding of how the incoming data should be cleansed, another department, or an external party, might have another understanding.
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