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  2. Data editing - Wikipedia

    en.wikipedia.org/wiki/Data_editing

    Verifying that the data is unique is an important aspect of data editing to ensure that all data provided was only entered once. This reduces the possibility for repeated data that could skew analytics reporting. See the example below. In the above table is an example of data with duplicate entries. See Sr.

  3. Data analysis for fraud detection - Wikipedia

    en.wikipedia.org/wiki/Data_analysis_for_fraud...

    The process can be performed based on algorithms or programmed loops. Trying to match sets of data against each other or comparing complex data types. Data matching is used to remove duplicate records and identify links between two data sets for marketing, security or other uses. [3] Sounds like Function is used to find values that sound similar.

  4. Record linkage - Wikipedia

    en.wikipedia.org/wiki/Record_linkage

    Record linkage (also known as data matching, data linkage, entity resolution, and many other terms) is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and databases).

  5. Data deduplication - Wikipedia

    en.wikipedia.org/wiki/Data_deduplication

    In computing, data deduplication is a technique for eliminating duplicate copies of repeating data. Successful implementation of the technique can improve storage utilization, which may in turn lower capital expenditure by reducing the overall amount of storage media required to meet storage capacity needs.

  6. Data cleansing - Wikipedia

    en.wikipedia.org/wiki/Data_cleansing

    Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").

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  8. Data analysis - Wikipedia

    en.wikipedia.org/wiki/Data_analysis

    By splitting the data into multiple parts, we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as well. [144] Cross-validation is generally inappropriate, though, if there are correlations within the data, e.g. with panel data . [ 145 ]

  9. Data redundancy - Wikipedia

    en.wikipedia.org/wiki/Data_redundancy

    The additional data can simply be a complete copy of the actual data (a type of repetition code), or only select pieces of data that allow detection of errors and reconstruction of lost or damaged data up to a certain level.