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Data Wrangling: MSCS View from the trenches


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Presentation slides from MSCS Systems Librarian Sara Amato's ALA ALCTS Pre-Conference presentation in Chicago, IL on June 27, 2013.

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Data Wrangling: MSCS View from the trenches

  1. 1. Data Wrangling: MSCS View from the trenches What we've learned Where we failed How we succeeded
  2. 2. You do what?  Liaisoning between tech services, project team, and vendors on data manipulation and display  Skills: − Marc and ILS data migration/manipulation − Nitty Gritty details – hows and whys − Knowledge sharing between partners − Investigations and Implementations − Project management − Meeting management
  3. 3. Data driven? Start at the end!  What do you really want to know?  Do you have the data to answer that?  What are you going to do with the data  What is interesting vs. what is actionable  Test out your theories!!
  4. 4. We Needed Data
  5. 5. Data driven? Start at the end!  Comparisons across institutions – match points Started with an OCLC reclamation project Records Sent Returned Unresolved Updated OCLC # Ursus 2,100,299 13,232 171,474 Colby 474,438 373 26,334 Bowdoin 624,164 37,848 Bates 656,926 25,101 TOTALS 3,855,827 13,605 260,757
  6. 6. Start at the end...if your ordering out  Think about what you want to get back, make sure it goes out.  HOW will you deal with returned data?  Can all the partners do the same things in terms of processing?
  7. 7. Lists, lists, lists! What will you in/exclude if you are extracting: types: gov docs, serials, media, e-resources locations: ref, off-site, reserve, special collections status: billed, missing, suppressed, withdrawn (!) use: circ, internal use, reserves What constitutes a circulating copy? How are the above encoded? Can you get what you want?
  8. 8. Circ Data  How long has it been retained?  Any tech processing that included circing?  Has it ever been cleared?  (… and what does it really tell you ...)
  9. 9. Know your vendor / programmer  What exactly is going to happen to the data, and what will be in(ex)cluded?  Leader bib level m , s  Gov Doc? (008 / 28) ?  Printed material? Media?
  10. 10. So, you think you know your data...
  11. 11. Can you get it out? Export Tables  What exactly is exported  What do they do with weird data? (b b, b 930)  Do the add any data? v.v.29 , oclc prefix  Formats of dates
  12. 12. Your data may vary 35109002285482 3510900228549
  13. 13. Document!!! REALLY!!!  Export tables and field mappings  Locations  List creation criteria  Record ranges exported and dates  Files
  14. 14. … a few of the ugly things we saw...  Multiple fields used for internal use (INTL USE, COPY USE, and IUSE3)  Records with multiple 001s  Records with multiple barcodes, duplicate barcodes, bound with items  Barcodes in 949 not 'b'  Records with no 260  3 0000003 ocm3 3_
  15. 15. Your data through different lenses Points of departure: -Merged 001s -FRBR -Volume vs Title counts -Unique vs Holdings counts -Date of data used -Definition of public domain
  16. 16. When things go wrong MarcEdit is your friend!
  17. 17. One more reason to thank Terry Reese SELECT T0xx.field_data FROM T0xx, T9xx WHERE T9xx.field = '945' AND T9xx.subfield = "f" AND T9xx.field_data > 0 AND T0xx.cid = T9xx.cid AND T0xx.field = '001'
  18. 18. Data Wrangling: MSCS Side Closing Haiku: Data is messy While it can be normalized Nothing is perfect