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Let's take a look: Integrated Systems: Systems that have been tightly integrated may have unanticipated dependencies around datastructures and output files. Reports: Integrated systems may also be generating reports that rely on data from both/all systems involved. This is also known as metadata enrichment.
Technical Specialists: A migration could require technical support for the movement of data and questions about implementation and datastructures. Determine the Value of the Information in the Source System: Does the source system store formal business records such as financial data, personnel files, contracts, etc.?
Much of the data we manage today is semi-structured, so why have separate solutions to manage each one? Making Unstructured Data, Structured. “80% 80% of data is unstructured.” In your efforts to manage your unstructured data, did you know you are actually making unstructured datastructured?
Auto-classification can leverage AI using RPA and ML / MT to analyze the digital content and categorize it, including redundant, obsolete, and temporary (ROT) [4] content. Much of the content will never be accessed, yet the cost to manage ROT content will continue to increase. Figure 1: Auto-classification and AI.
AI can also reduce the human effort required to “crunch” vast amounts of content – generated and stored in an organization’s content repositories – in order to identify redundant, obsolete temporary (ROT) [2] content that has no business value. Blockchain, Provenance, and Authentic Information.
For too long, data cleanup initiatives have focused primarily on technologydeploying bigger, better, or newer tools to find and delete ROT (redundant, obsolete, trivial) data. As Joe Shepley noted, technology today is sophisticated enough to handle large-scale data identification and classification.
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