In order to provide useful information on data attributes like type, frequency, and length, along with their discrete values and value ranges, data profiling focuses on analyzing individual attributes of data. It gathers data, analyzes it, and performs quality checks on it to evaluate the source data’s structure and quality.
Examining, analyzing, and producing helpful summaries of data is known as data profiling. The procedure produces a high-level overview that helps in the identification of data quality problems, threats, and broad trends. Data profiling generates vital data insights that businesses can then use to their advantage.
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Data profiling specifically sorts through data to assess its reliability and caliber. To examine data in great detail, analytical algorithms find dataset characteristics like mean, minimum, maximum, percentile, and frequency. After that, analyses are carried out to find metadata, such as frequency distributions, key relationships, foreign key candidates, and functional dependencies. Finally, it makes use of all this data to show how these factors relate to the standards and objectives of your company.
Data profiling can get rid of expensive mistakes that are frequently found in customer databases. Null values (also known as missing or unknown values), values that shouldn’t be present, values with unusually high or low frequency, values that deviate from expected patterns, and values outside of the expected range are examples of these errors.