This document discusses how libraries can transition from traditional supply-driven collection models to more demand-driven, data-driven models that are more sustainable. It argues that usage data and analytics should be used more in collection decisions to lower costs and better meet user needs. Specific strategies mentioned include analyzing print and e-book usage patterns, using data to inform space planning, and collaborating through resource sharing networks. Challenges discussed include resistance to change and accounting for niche areas. The document advocates growing analytical skills, experimenting, and using data to make collections more vital to researchers.

1. Moneyball, the Extra 2%, and What Baseball
Management, Fantasy Football, and Newspapers
Can Teach Us About Fostering Innovation in
Managing Collections
Greg Raschke
North Carolina State University
University of Oklahoma
March, 2014

2. Baseball to Collections Context

3. Supply-Side Collections
 Print-based, unpredictable
demand, and legitimate need for
just in case collections
 Lead to judging quality by size (as
in the ARL rankings) and libraries
were then held captive to this
standard
 Contributed to inelastic demand
for journals and combinations of
speculative buying
 Use is secondary to size, dollars
expended, and other input
measures
 Credit to David Lewis
(http://ulib.iupui.edu/users/dlewis)

4. Supply-Side to Will Not Continue
$-
$2,000,000
$4,000,000
$6,000,000
$8,000,000
$10,000,000
$12,000,000
$14,000,000
$16,000,000
$18,000,000
$20,000,000
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Total Library Materials Expenditures
1983-2013
ARL 1
ARL 2
ARL 3

5. Assumptions
 Economics are not sustainable
 Collections budgets will not grow at rate of past 30 years
 Unit growth and growth in cost per unit are not sustainable
 Need to lower costs of overall system
 Lower unit costs
 Use data and users to be more precise
 Tipping point for ability and expectations to deliver content at point
of need
 Therefore collection practices and strategies must change
 This is difficult – much reason for optimism

6. Demand-Driven Collections – Core
Roles
 Make information
easily, widely, and cheaply
available
 Collections as drivers of
research, teaching, and
learning
 To make special or unique
collections held/managed by
the library available to the user
community and the world

7. Demand-Driven – More Assumptions
 Less tolerance for and less
investment in lower use
general collections
 Resource management based
increasingly on use
 Embrace expansion of
available content and sense-
making role
 Risks of not evolving and
failing to innovate –
newspapers

8. Demand-Driven – Assertions
 Tension between time-honored
role as custodians of
scholarship versus enabling
digital environment for
scholars
 Must work on:
 Lowering unit costs of
scholarly materials OR
 Lowering number of
publishable units
 Must free funds for investing in
“new” arenas such as digital
curation, digital scholarship,
DDA, and collaboration

9. Demand-Driven – Assertions
 Use based and user driven
collecting models will take
growing share of budget
 Bet on numbers
 Bet on good and quick
 Put resources into enabling
digital environment for
scholars and custodian role
will come out of that strategy
 Rewards of adapting – more
used and vital than ever

10. Demand-Driven – Changing Practice
 Access won – management and coherence are keys
 Not just PDA – portfolio of approaches - more responsive and
expansive
 Utilize new tools and techniques to become advanced analysts and
deliver content at point of need
 Truly embrace evidence-based decision making and ability to deliver
content on demand
 Challenges:
 Resource sharing
 Existing practices and organizational models

11. Competency Trap

12. Competency Trap

13. Looking Deeper and Questioning
Existing Practices
 Identifying market inefficiencies.
 Apply and accelerate significant
creativity.
 Question long-established
wisdom.
 Test what is “known” with in-
depth analysis, statistical
modeling, and new approaches.
 Value in stopping making stupid
decisions
 Emphasize interpersonal skills in
leveraging new knowledge and
approaches.

14. Reducing Unit Costs – Data Analysis
 Collections work less about selection and more about
analyzing use and incorporating content w/technology
 Data analysis is a key component in solving/managing:
 Increasing pressure for accountability
 Increasing capability to gather and analyze data
 Increasing precision in the way we build collections
and expend resources
 Advocacy
 Changing practice and data analysis at NCSU

16. Serials Review 2009 – Open, Data-Driven,
and Real-Time Analysis
 Standardized usage data
(where available)
 Bibliometrics - publication
data and citation patterns
(e.g LJUR)
 Impact factor and eigenfactor
 User community feedback via
interactive, database-driven
applications
 Weigh/calculate/quantify user
feedback
 Weigh price against multiple
data points
 Usage ((07 usage+08
usage/2)+(publications*10)+
(citations*5)+(Impact Factor)
 Community Feedback
((Weighted Ranking x % Match)
x Total # Rankings) + 0.1 x # of
"1s“
 Price/feedback value
 Price/use
 Merge results to filter out top
20% and bottom 20%

17. Looking closer – Book Collections
An example - a closer look at print item usage
 Traditional ILS reporting tools can make this difficult
 Advanced analytical tools can help
 What types of questions can we ask?
 Should Patron-Driven records not purchased be purged after 1 year?
 How does print item usage break down?
 Which categories of print items net the best value?

18. If it’s not used after 2 years…
Should PDA records
be purged?
Maybe…
We haven’t even hit
50% usage
But what if we take
a longer view…

19. If it’s not used after 2 years…
 Things begin to
look different

20. Looking even closer…
 How does
print item use
break down?
 Single circ
usage is
consistently
~14%
 Would this
change in a
PDA only
world?

21. Expenditures to University Data

22. Expenditures to University Data

23. Expenditures to University Data

24. 0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
Reserves
E-books
Digital collections
requests
Print book
circulations/renewals
Database use
Full-text journal
downloads
Measurable Uses of the Collection 2009/2010
Full-text journal downloads* 3,672,600
Database use 1,989,972
Print book circulations/renewals 525,430
Digital collections requests 471,403
E-books 149,815
Reserves** 327,267
Total Uses 7,136,487
* Includes use of NC LIVE full-text content
** Includes textbook, print, and e-reserves usage
Measurable Uses of the Collection 2009/2010

25. Collections Data in Re-Conceptualizing
Library Space
 Sell new collections
layouts and inventory.
 Decision-making.

26. Collections Data in Re-Conceptualizing
Library Space

27. Collections Data in Re-Conceptualizing
Library Space

28. Collaborative Imperative
 Print curation
 Digital curation
 Digital collections
 Regional networks
 Mega-consortia and collective bargaining
 Reframe notion of collections budget

29. Challenges
 Have ability to be more
precise, more used, and
more relevant than ever –
need to make the
necessary changes
 CAVE people and Zealots
 Data and user-driven
approaches can punish
niche areas, disciplinary
variation, and resources
without data
 New value, new skills

30. Challenges, cont.
 Contradiction of personal
apps/devices and open
resources
 Open resources impact
ability to control and
command discovery
environments, content
delivery, and data
analysis

31. From Assumptions to Assertions to
Practice
 Grow/develop/hire analysts.
 Adapt statistical tools such as SAS software.
 Partner with digital library/technologists.
 Develop positive arbitrage.
 Put resources into enabling digital environment for scholars.
 Experiment – budget for it, reward it.
 Work hard to get the faculty to buy into new approaches.
 Combine analytical approaches with the people skills .
“…there was a bias toward what people saw with their own eyes,
or thought they had seen. The human mind played tricks on itself
when it relied exclusively on what it saw, and every trick it played
was a financial opportunity for someone who saw through the
illusion to the reality”.