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data_quality_management_system:data_cleansing [2023/06/11 21:08]
peter 		data_quality_management_system:data_cleansing [2024/05/26 19:33] (current)
peter
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 === Definition === === Definition ===
-Data cleansing is the process of detecting and correcting [[data_quality_management_system/data_issue|data issues]] to improve the quality of data to an acceptable level.+Data cleansing is the [[general_term/process|process]] of detecting and correcting [[data_quality_management_system:data_quality_issue|data issues]] to improve the quality of data to an acceptable level.
  
 ===Notes=== ===Notes===
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   * [[data_quality_dimension/completeness|Completeness of records]]   * [[data_quality_dimension/completeness|Completeness of records]]
   * [[data_quality_dimension/completeness|Completeness of data values]]   * [[data_quality_dimension/completeness|Completeness of data values]]
-  * [[data_quality_dimension/compliance|Compliance]] of data with laws, regulations, and standards]]+  * [[data_quality_management_system:compliance|Compliance]] of data with laws, regulations, and standards]]
   * [[data_quality_dimension/consistency|Consistency]] of data values   * [[data_quality_dimension/consistency|Consistency]] of data values
   * [[data_quality_dimension/currency|Currency]] of data values   * [[data_quality_dimension/currency|Currency]] of data values
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 === Purpose === === Purpose ===
-To detect and correct [[data_quality_management_system/data_issue|data issues]] and inconsistencies.+To detect and correct [[data_quality_management_system:data_quality_issue|data issues]] and inconsistencies.
  
 === Life cycle === === Life cycle ===
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 === Methods === === Methods ===
-The next methods of correcting [[data_quality_management_system/data_issue|data issues]] can be distinguished:+The following methods of correcting [[data_quality_management_system:data_quality_issue|data issues]] can be distinguished:
 ^ Method                                    ^ Example or explanation                                                                                                                                                                                                                                                                                                                                                                                                            ^ ^ Method                                    ^ Example or explanation                                                                                                                                                                                                                                                                                                                                                                                                            ^
 | Abbreviation expansion                    | Abbreviation expansion transforms abbreviations into their full form. There are different kinds of abbreviation. One type shortens each of a set of words to a smaller form, where the abbreviation consists of a prefix of the original data value. E.g. “USA” stands for “United States of America.”                                                                                                                            | | Abbreviation expansion                    | Abbreviation expansion transforms abbreviations into their full form. There are different kinds of abbreviation. One type shortens each of a set of words to a smaller form, where the abbreviation consists of a prefix of the original data value. E.g. “USA” stands for “United States of America.”                                                                                                                            |
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 | Type conversion                           | Type conversion (also called casting) is an operation that converts a piece of data of one data type to another data type. Type conversion can be used to make sure that numbers are stored as numerical data types and that a date should be stored as a date object.                                                                                                                                                            | | Type conversion                           | Type conversion (also called casting) is an operation that converts a piece of data of one data type to another data type. Type conversion can be used to make sure that numbers are stored as numerical data types and that a date should be stored as a date object.                                                                                                                                                            |
 | Edit rules                                | Edit Rules, a new class of data quality rules, are rules that tells how to fix errors, i.e. which attributes are wrong and what values they should take.                                                                                                                                                                                                                                                                          | | Edit rules                                | Edit Rules, a new class of data quality rules, are rules that tells how to fix errors, i.e. which attributes are wrong and what values they should take.                                                                                                                                                                                                                                                                          |
-| Data lifecycle management                 | Data Lifecycle Management can be defined as the different stages that the data traverses throughout its life from the time of inception to destruction. Data lifecycle stages encompass creation, utilization, sharing, storage, and deletion.                                                                                                                                                                                    |+| Data lifecycle management                 | Data Lifecycle Management can be defined as the different stages that the data traverses throughout its life from the time of inception to destruction. Data lifecycle stages encompass creation, utilisation, sharing, storage, and deletion.                                                                                                                                                                                    |
  
 Note 4: Data issue prevention is far superior to data issue detection and cleansing, as it is cheaper and more efficient to prevent issues than to try and find them and correct them later. Note 4: Data issue prevention is far superior to data issue detection and cleansing, as it is cheaper and more efficient to prevent issues than to try and find them and correct them later.
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 === Relations === === Relations ===
 +|Data cleansing| is child of |[[general_term/process|process]]|
 |Data cleansing| is an element of a |[[data_quality_general:data_quality_management_system|data quality management system]]| |Data cleansing| is an element of a |[[data_quality_general:data_quality_management_system|data quality management system]]|
-|Data cleansing| resolves |[[data_quality_management_system/data_issue|data issues]]| +|Data cleansing| resolves |[[data_quality_management_system:data_quality_issue|data issues]]| 
-|Data cleansing|is the successor of|[[data_quality_management_system/data_quality_monitoring|Data quality monitoring]]| +|Data cleansing|is the successor of|[[data_quality_management_system/data_quality_monitoring|data quality monitoring]]| 
-|Data cleansing|can be performed by using|data cleansing methods| +|Data cleansing|uses|data cleansing methods| 
-|Data cleansing|wil be applied firstly to|[[data_quality_management_system/critical_data_element|Critical data elements]]| +|Data cleansing|wil be applied firstly to|[[data_quality_management_system/critical_data_element|critical data elements]]| 
-|Data cleansing|improves|[[data_quality_general/data_quality|data quality]]. +|Data cleansing|improves|[[data_quality_general/data_quality|data quality]]| 
 +|Data cleansing|needs|[[data_quality_management_system/data_quality_rule|data quality rules]]|
  
 {{:data_management:data_quality:data_cleansing.jpg?500|}} {{:data_management:data_quality:data_cleansing.jpg?500|}}
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 What is data cleansing? Guide to data cleansing tools, services and strategy. (2020, August 13). Talend Real-Time Open Source Data Integration Software. https://www.talend.com/resources/what-is-data-cleansing/ What is data cleansing? Guide to data cleansing tools, services and strategy. (2020, August 13). Talend Real-Time Open Source Data Integration Software. https://www.talend.com/resources/what-is-data-cleansing/
 +
 +{{tag>All DQMS}}
 +
data_quality_management_system/data_cleansing.1686517722.txt.gz · Last modified: 2024/03/08 13:33 (external edit)

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