Difference between revisions of "Data Processing"
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The accurate processing and safe storage of cattle data require a professional approach to | The accurate processing and safe storage of cattle data require a professional approach to | ||
[https://en.wikipedia.org/wiki/Data_quality data quality]. The goals of an organization or business that manages cattle data should include: | [https://en.wikipedia.org/wiki/Data_quality data quality]. The goals of an organization or business that manages cattle data should include: | ||
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* Consistent processing | * Consistent processing | ||
| − | A key to reliably [[Data Collection | collecting]] and processing data is the development of a clear and reliable [[Identification Systems | animal identification method]]. Animal identification is used to link disparate pieces of performance, pedigree and [[Genomic Data | genomic data]] on an individual. An important consideration for an animal identification method within a database is the transfer of the data to other organizations. For example, most modern [[Genetic Evaluation | genetic evaluations]] include data from multiple organizations and animals' information may be included in multiple databases. | + | A key to reliably [[Data Collection | collecting]] and processing data is the development of a clear and reliable [[Identification Systems | animal identification method]]. Animal identification is used to link disparate pieces of performance, pedigree and [[Genomic Data | genomic data]] on an individual. An important consideration for an animal identification method within a database is the transfer of the data to other organizations. For example, most modern [[:Category:Genetic Evaluation | genetic evaluations]] include data from multiple organizations and animals' information may be included in multiple databases. |
Most entities that process data either require [[Whole Herd Reporting]] or offer an option to their participants. Advantages of Whole Herd Reporting include reduced reporting bias in [[Expected Progeny Difference | genetic predictions]] and the ability to produce [[Stayability | cow fertility predictions]]. | Most entities that process data either require [[Whole Herd Reporting]] or offer an option to their participants. Advantages of Whole Herd Reporting include reduced reporting bias in [[Expected Progeny Difference | genetic predictions]] and the ability to produce [[Stayability | cow fertility predictions]]. | ||
Latest revision as of 17:51, 12 April 2021
The accurate processing and safe storage of cattle data require a professional approach to data quality. The goals of an organization or business that manages cattle data should include:
- Accurate linking of related pieces of information
- Timely retrieval of information
- Easy-to-understand reporting
- Reliable storage and backup
- Easy-to-use addition of new data
- Consistent processing
A key to reliably collecting and processing data is the development of a clear and reliable animal identification method. Animal identification is used to link disparate pieces of performance, pedigree and genomic data on an individual. An important consideration for an animal identification method within a database is the transfer of the data to other organizations. For example, most modern genetic evaluations include data from multiple organizations and animals' information may be included in multiple databases.
Most entities that process data either require Whole Herd Reporting or offer an option to their participants. Advantages of Whole Herd Reporting include reduced reporting bias in genetic predictions and the ability to produce cow fertility predictions.
