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Data profiling is the process of examining the data available in an existing data source (e.g. a database or a file) and collecting statistics and information about that data. The purpose of these statistics may be to: # Find out whether existing data can easily be used for other purposes # Improve the ability to search the data by tagging it with keywords, descriptions, or assigning it to a category # Give metrics on data quality including whether the data conforms to particular standards or patterns # Assess the risk involved in integrating data for new applications, including the challenges of joins # Discover metadata of the source database, including value patterns and distributions, key candidates, foreign-key candidates, and functional dependencies # Assess whether known metadata accurately describes the actual values in the source database # Understanding data challenges early in any data intensive project, so that late project surprises are avoided. Finding data problems late in the project can lead to delays and cost overruns. # Have an enterprise view of all data, for uses such as master data management where key data is needed, or data governance for improving data quality. == Data profiling in relation to data warehouse/business intelligence development == 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Data profiling」の詳細全文を読む スポンサード リンク
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