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Data Processing: Difference between revisions
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The accurate processing and safe storage of cattle data requires 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, | |||
# 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 [[Data Collection | collecting]] and processing data is the development of a [[Identification Systems | strong 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 [[Data Transfer | 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. | |||
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 DIfferences | genetic predictions]] and the ability to produce [[Stayability | cow fertility predictions]]. | |||
=Association Level Processing= | =Association Level Processing= | ||
=Data Ownership= | =Data Ownership= | ||
Revision as of 15:32, 26 June 2019
The accurate processing and safe storage of cattle data requires 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 strong 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.
