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.
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Moneyball, Baseball, and Demand-Driven Collections Management
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
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)
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
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
15.
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?
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”.