SCELC Board of Directors

OCLC Data

AnalysisJohn McDonald

CIO, Claremont University Consortium

February 8, 2013

SCELC’s Need for DATA• Nascent resource sharing program (CAMINO)

What can I get out of this if I join?

• Interest in shared print preservation program

What will I be obligated to keep if I join?

• Some have interest in closer collaborative collection

development

What can I stop buying or what else can I buy?

OCLC Data Analysis

• SCELC officially requested provision of print book

holdings from OCLC for a portion of its members

• 56 SCELC schools requested (50% of membership)

• Simple Data provided:

By OCLC Number

Holding Libraries by Symbol

OCLC Data Analysis

• 2.2 Million Books (or 2,190,464 to be exact)

• 5.5 Million Holdings (or 5,558,921 to be exact)

Data looks a little like this…

0

100,000

200,000

300,000

400,000

500,000

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Total Books Held, by Library

So what? What will the data tell us…

Who makes a good resource sharing partner?

Who makes a good shared print partner?

What traits can influence a Library to join a

program or start a partnership?

Who do is best to collaborate with on

collections in the future?

0%

5%

10%

15%

20%

25%

30%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%

To

tal P

ort

ion o

f C

olle

ction

Unique across all Libraries

Fuller Theological Seminary, 100K

Caltech, 75K

Claremont, 180K

LMU, USF, Santa

Clara, 70-80K each

American Jewish

University, 50K

Occidental, 50K

Shared Print: Find Unique Holdings to Maximize Preservation

Shared Print: Find Overlap Holdings to Maximize Deselection

Bo

ok

s a

lso

held

by

Cla

rem

on

t

Shared Print: Find Overlap as a % of Collection

% o

f C

oll

ec

tio

n h

eld

by

Cla

rem

on

t

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

40% 50% 60% 70% 80% 90% 100%

LMU, USF, Santa

Clara, 200-250K each

To

tal P

ort

ion o

f C

olle

ction

Unique from Claremont

Fuller Theological

Seminary, 230K

Loma Linda, 120K

Biola, 135K

Caltech, 150K

Resource Sharing: Find Libraries Most Unlike Us

• Data has proven to be valuable in modeling collections

sharing, preserving, and collaboration potential

• Additional areas of analysis:

▫ Overlap and uniqueness by publication year and subject area (LC

Call Number)

▫ Paired and multiple modeled scenarios

• OCLC Data is just a snapshot in time (and already outdated)

• OCLC is hard to work with and can be expensive

Potential for this data

• Need data from members directly

▫ Simple data extraction should be easy and can be supplemented

by OCLC API

• Find appropriate permanent home for database

• Develop self-service tool with (close to) real time data

• Determine if new OCLC Collection Analysis tool will provide

the same or similar information

Next Steps