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Data Cleaning with EMu

Data Cleaning with EMu. Warren Hindley. Why Bother?. Minimise data replication, EMu is relational. Speed. Data entry sorting and selecting records. Processing of large numbers of records For reports or labels. Lookup Lists are populated from data in records.

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Data Cleaning with EMu

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  1. Data Cleaning with EMu Warren Hindley

  2. Why Bother? • Minimise data replication, EMu is relational. • Speed. • Data entry sorting and selecting records. • Processing of large numbers of records • For reports or labels. • Lookup Lists are populated from data in records. • Quality Data – Turn data into information

  3. Lookup Lists

  4. How long does it take? • Ages! • Make all users aware. • Find commonalities. • Don’t rush, keep narrowing your result set. • Save Groups of Records

  5. Examples - Query

  6. Results Duplicate Records

  7. Sort Sort results for easier viewing

  8. Sort Summary Duplicated Records

  9. Merge

  10. Merge

  11. Why not? • Worried about deleting records. • Export the data (backup). • Change record status. • Change record permissions. • Easy to corrupt data. • Use a small numbers of records. • Experiment with test data, most institutions have training or test systems.

  12. Changing the Record Status Summary Data and Record Status updated.

  13. Attachments Search every attachment field in every module for this particular record.

  14. Attachments 870 Attachments To the catalogue.

  15. Global Replace • Replace a value in one or more fields with another. It can be used to: • Correct spelling mistakes. • Change the case of text. • Replace Attachments. • Remove Spaces from between text.

  16. Regular Expressions • Special text string for describing a search pattern. • Advanced wildcards. • They are ugly.

  17. Global Replace Simple Task: Replace: Ben with Benjamin

  18. Global Replace

  19. Regular Expressions ^ = Start of the line $ = End of the line

  20. Support We are here to help, email us: support@man.kesoftware.com

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