Lesson plan
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. 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 by tagging it with keywords, descriptions, or assigning it to a category # Assess data quality, including whether the data conforms to particular standards or patterns # Assess the risk involved in integrating data in new applications, including the challenges of joins # Discover metadata of the source database, including value patterns and distributions, key candidates, foreign-key candidates, and functional dependencies # Assess whether known metadata accurately describes the actual values in the source database # Understanding data challenges early in any data intensive project, so that late project surprises are avoided. Finding data problems late in the project can lead to delays and cost overruns. # Have an enterprise view of all data, for uses such as master data management, where key data is needed, or data governance for improving data quality.
Article summary
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. 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 by tagging it with keywords, descriptions, or assigning it to a category # Assess data quality, including whether the data conforms to particular standards or patterns # Assess the risk involved in integrating data in new applications, including the challenges of joins # Discover metadata of the source database, including value patterns and distributions, key candidates, foreign-key candidates, and functional dependencies # Assess whether known metadata accurately describes the actual values in the source database # Understanding data challenges early in any data intensive project, so that late project surprises are avoided. Finding data problems late in the project can lead to delays and cost overruns. # Have an enterprise view of all data, for uses such as master data management, where key data is needed, or data governance for improving data quality.
Key claims
No claims recorded.
Discussion questions
- Summarise the article in your own words. Which single claim would you verify first, and why?
- Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
- If two sources disagreed about one of these facts, what would you need in order to trust one over the other?
Bibliography
No sources recorded.
Key claims and bibliography are generated from the same tracked claims and sources as the article page.