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Concept
Data profiling
Summary
Data profiling is the process of examining the data available from an existing information source (e.g. a database or a ) and collecting statistics or informative summaries about that data. The purpose of these statistics may be to:
Find out whether existing data can be easily used for other purposes
Improve the ability to search data by tagging it with keywords, descriptions, or assigning it to a category
Assess data quality, including whether the data conforms to particular standards or patterns
Assess the risk involved in integrating data in 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 refers to the analysis of information for use in a data warehouse in order to clarify the structure, content, relationships, and derivation rules of the data. Profiling helps to not only understand anomalies and assess data quality, but also to discover, register, and assess enterprise metadata. The result of the analysis is used to determine the suitability of the candidate source systems, usually giving the basis for an early go/no-go decision, and also to identify problems for later solution design.
Data profiling utilizes methods of descriptive statistics such as minimum, maximum, mean, mode, percentile, standard deviation, frequency, variation, aggregates such as count and sum, and additional metadata information obtained during data profiling such as data type, length, discrete values, uniqueness, occurrence of null values, typical string patterns, and abstract type recognition.
Official source
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