This document discusses best practices and lessons learned for data wrangling projects. It emphasizes starting projects by defining goals and intended outcomes. Common challenges discussed include inconsistent data encoding, missing fields, and unexpected data issues. The document provides tips for investigating data exports, mapping fields, and using tools like MarcEdit to clean data when problems arise. The overall message is that real-world library data is messy and may vary across systems.

1. Data Wrangling: MSCS
View from the trenches
What we've learned
Where we failed
How we succeeded

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

6. 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!!

7. We Needed Data

8. 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

9. 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?

10. 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status: billed, missing, suppressed, withdrawn (!)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?

11. 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 ...)

12. 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?

13. So, you think you know your data...

14. 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

15. Your data may vary
35109002285482 3510900228549

16. Document!!! REALLY!!!

Export tables and field mappings

Locations

List creation criteria

Record ranges exported and dates

Files

17. … 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_

18. 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

19. When things go wrong
MarcEdit is your friend!

20. 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'

21. Data Wrangling: MSCS Side
Closing Haiku:
Data is messy
While it can be normalized
Nothing is perfect