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  2. Data reduction - Wikipedia

    en.wikipedia.org/wiki/Data_reduction

    Data reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. . The purpose of data reduction can be two-fold: reduce the number of data records by eliminating invalid data or produce summary data and statistics at different aggregation levels for various applications

  3. Data analysis - Wikipedia

    en.wikipedia.org/wiki/Data_analysis

    The process of data exploration may result in additional data cleaning or additional requests for data; thus, the initialization of the iterative phases mentioned in the lead paragraph of this section. [31] Descriptive statistics, such as, the average or median, can be generated to aid in understanding the data.

  4. Data cleansing - Wikipedia

    en.wikipedia.org/wiki/Data_cleansing

    Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [ 1 ]

  5. Data profiling - Wikipedia

    en.wikipedia.org/wiki/Data_profiling

    Data profiling is the process of examining the data available from an existing information source (e.g. a database or a file) and collecting statistics or informative summaries about that data. [1] 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 ...

  6. Extract, transform, load - Wikipedia

    en.wikipedia.org/wiki/Extract,_transform,_load

    Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collected from one or more sources and it can also be output to one or more destinations.

  7. Data quality - Wikipedia

    en.wikipedia.org/wiki/Data_quality

    Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing [17] [18] activities (e.g. removing outliers, missing data interpolation) to improve the data quality.

  8. Data editing - Wikipedia

    en.wikipedia.org/wiki/Data_editing

    Data editing is defined as the process involving the review and adjustment of collected survey data. [1] Data editing helps define guidelines that will reduce potential bias and ensure consistent estimates leading to a clear analysis of the data set by correct inconsistent data using the methods later in this article. [ 2 ]

  9. Data validation - Wikipedia

    en.wikipedia.org/wiki/Data_validation

    Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]