How to Cleanup Data Repositories with Semantic Technologies
Data Cleanup with Semantic Technologies
Often data is referred to as the ‘new oil,’ a view that is gradually gaining ground since hardly any business model functions today without data as a basis.
But having data doesn’t mean understanding your data. For example, compliance requirements, such as data protection, can only be fulfilled if you know your data. That is why most organizations are dealing with ever-increasing costs and user discontent because of data growth and chaotic data management processes.
As a result, completely obsolete data continues to be stored and managed unnecessarily, generating useless ‘noise.’ So a key question is: How can the valuable data in your organization be separated from the data which is duplicated, obsolete, or has no meaning for your business?
This white paper introduces you step by step to an innovative method for data cleanup based on semantic technologies. Semantic knowledge models enable the management and exchange of knowledge utilizing so-called knowledge graphs that can be used for automated analysis of documents and data objects. As a result, companies reduce the amount of time knowledge workers invest in searching for information and at the same time, benefits from increased data quality.
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