AIM: Data-driven collection management

Data-Driven Collection Management
Fernan R. Dizon
Associate Director
AIM-Knowledge Resource Center
2016 PAARL Summer Conference
Library Analytics: Data-Driven Library Management
April 20-22, 2016
Pearl Manila Hotel

Outline
• Definition of terms
• Collection management (evolution, collection
development vs. collection management, collection
management activities)
• Data-driven collection management
• Imperatives for data-driven collection management
• Cases
• Conclusion

Definitions
• Analytics: The scientific process of
transforming data into insight for making
better decisions.
(INFORMS, 2016)

Analytics
• Analytics is now a major activity, as transaction or behavioral data is
aggregated and mined for insight. We have become used to
recommendations based on buying or navigation patterns. As more
material is digital, as more business processes are automated, and as
more activities shed usage data, organizations are manipulating larger
amounts of relatively unstructured data and extracting value from it.
Within the library field, patterns of download, holdings, and query
resolutions are being mined to improve services. This trend has major
implications for discovery, selection, acquisition, and management of
collections.
(Dempsey, Malpas, and Lavoie, 2014, p.11-12)

Definitions
• Collection management: In a more general
sense, the activity of planning and supervising
the growth and preservation of a library's
collections based on an assessment of existing
strengths and weaknesses and an estimate of
future needs.
(ODLIS, 2016)

Definitions
• Collection management is defined as a process of information
gathering, communication, coordination, policy formulation,
evaluation, and planning. These processes, in turn, influence
decisions about the acquisition, retention, and provision of
access to information sources in support of the intellectual
needs of a given library community. Collection development is
the part of collection management that primarily deals with
decisions about the acquisition of materials.
(Osburn, in Johnson, 2009, p. 2)

Definitions
• Dashboard: is a data visualization tool that displays the
current status of metrics and key performance indicators
(KPIs) for an enterprise. Dashboards consolidate and arrange
numbers, metrics and sometimes performance scorecards on
a single screen. They may be tailored for a specific role and
display metrics targeted for a single point of view or
department. The essential features of a BI dashboard product
include a customizable interface and the ability to pull real-
time data from multiple sources.
(Search Business Analytics, 2016)

AIM: Data-driven collection management

Dashboards
• The dashboard allows librarians and management to monitor
key library operations from a single, convenient page, with an
emphasis on long-term trends rather than day-to-day
fluctuations in use.
(Morton-Owens and Hanson, 2012, p.50)

Definitions
• Patron-driven acquisition (aka demand-driven acquisitions) is
a purchasing model in which patrons select e-books by
choosing them from the OPAC and the library and vendor set
the number and kind of uses that trigger a purchase. Selecting
a portion of your funding and experimenting with patron-
driven acquisition will ensure that you are purchasing some e-
books that are being used and collecting some statistics on
the desires of your patrons.
(Richard, 2012, p.63)

Collection management:
evolution;
collection development vs.
collection management;
collection management
activities

Collection management evolution
1. How the process takes place and the tasks involved (From
selection to collection management/ simple to complex/ one
man to participatory decision making.
2. What the underlying goal of collection content should be
(gatekeeping to open-access).
(Evans and Saponaro, 2012, p. 19)

Collection development vs. Collection
management
• Collection development (building)
• Collection management (maintaining)
(Chapman, 2012)

Collection management activities
Core-collection management encompasses a wide range of activities:
• selection and acquisition
• budget allocation and management
• serials and electronic resource management and access control
• stock evaluation
• weeding
• storage and preservation
• liaison with users, managers, suppliers and publishers
• collaboration with other institutions
(Fieldhouse, 2012)

Collection management activities
Activities associated with collection management:
• Promotion and marketing activities
• Activities associated with the quality of both bibliographic and
electronic records and the use of appropriate metadata to
describe resources accurately to facilitate access.
(Fieldhouse, 2012)

Collection Management
Process (Evans and
Saponaro, 2012, p.23)

Data-driven collection management

Challenges librarians face (Day and Davis, 2010, p.81):
• buying power diminished by the woes of shrinking, or if we’re
lucky, static budgets and inflation increases;
• gate counts are rising;
• patrons expect the library to be open 24 hours;
• researchers are demanding access to the new journals and
digital products that are launched each year;
• difficult balancing act of deciding what we can afford and
what we can live without, while still striving to provide quality
support for learning, research, and teaching.

What does data-driven means?
Using data proactively to address emerging trends and
challenges is “what it really means to be a data-driven…”
- Sarah Tudesco, Assessment Librarian, Yale University (in Enis,
2013)

Data that can be used to support decisions:
• cost of books/journals/databases/videos, etc. include;
• usage (full-text downloads, circulations, ILL transactions);
• rights (owned/leased);
• citation and publication patterns by your campus researchers (i.e., which
journals are they citing and which journals are they publishing in);
• journal impact factors (as flawed as they are, they are still seen as a
benchmark among many researchers and libraries);
• regional holdings, and;
• journal editorial activity of your campus researchers.
(Day and Davis, 2010)

Imperatives for data-driven
collection management

Imperatives for data-driven collection
management
Collections are changing in a network environment (Dempsey,
Malpas, and Lavoie, 2014, p.6-7):
1. The network context:
• Unbundling and rebundling: transaction costs and system-
wide reorganization.
• A data driven environment: activities are becoming
“informationalized,” where more operations are automated
and data drives decisions.
• Research and learning behaviors are changing: libraries serve
a constituency whose needs are also changing.

management
2. The evolving scholarly record: Libraries acquire, organize, and
provide stewardship of the scholarly record. Ongoing
redefinition of the scholarly record will drive changes in library
and publishing practice. (Accompanying materials, i.e. videos,
raw data, blogs, discussions, etc.)

management
3. The collections grid: Libraries engage with different types of
collections, which have different dynamics associated with them.
Understanding the shift in the patterns of operational support
for different types of resources is important to library planning
and investment, both for individual libraries and for the
networks of which they are a part.

management
4. The inside-out collection: The dominant library model has been
outside-in, where materials are purchased or licensed from external
sources and made available to a local audience. The inside-out
model, where institutional materials (digitized special collections,
research and learning materials, researcher expertise profiles, etc.)
are shared with an external audience requires new ways of thinking.
5. Managing shared print: The print collection has been central to
the identity of the library but is now on the threshold of major
network reorganization. The emergence of cooperative
infrastructure, facilitated by the network, has enabled a transition
from institutionally-organized stewardship toward group-scaled
solutions.

management
6. Sourcing and scaling: Collections will be managed at
several levels, above the institution as well as within it.
Choices about the optimum level (institutional,
consortial/group, regional, global) for management are
becoming more common, as are decisions about how
to source activities (collaborative, buy from third party,
etc.).

Case 1: North Carolina
State University

Case 1: North Carolina State University
North Carolina State University (Day and Davis, 2010)
Journal review and cancellation
• Methodology: Online Form
• Respondents: University Library Committee and programming
and faculty, graduate library representatives, other
stakeholders.
• Objectives: To enable the NCSU Library address the collections
budget but of 15% or $1.5 million dollars by reviewing all
journal subscriptions and putting together a list of lower use
(in terms of full-text downloads), less relevant, titles and
presenting to campus constituents and requesting for their
feedback. (This list consisted of 1,112 titles).

Data elements used at the NCSU
Libraries for their serials review
exercise

2. Collection view tools
Uses of the Collection Views Tool
• “The broad purpose of this project was to help collection
management librarians evaluate and think about how library
purchasing power is distributed across different subject areas.
The data allows us to examine the relationship between our
spending on resources and the departments the library
serves, and can help us test our assumptions about the
strengths of the university and what we know about the work
of colleges and departments on campus.”

3. Journal backfiles return on investment (ROI) - analysis of the cost/use for
journal archives
Methodology:
• Data was derived from a combination of sources(publisher/provider
websites, local ILS and files where cost data were maintained, usage
statistics from publishers and providers, and collated cost data for each of
the backfile collections purchased since 2003, including both one-time
payments and annual maintenance fees).
• Using Microsoft Excel, they plotted cost/use for each year where the data
was available. In instances when they could not acquire usage data back to
the year of initial purchase, they carried the one-time cost through each
year of ownership and divided that by cumulative use. Where onetime
payments existed, they applied the one-time payment to the first year of
reported use.

Objectives:
• To justify the spending of valuable state resources and
illustrate the value of the resources the NCSU Libraries
purchased and supported.
• To describe how online journal archives saves the Libraries
valuable space for seating and innovative work/study/play
space, while also enhancing ease of access from virtually any
location, noting that portability and discoverability were
important needs for NCSU faculty, staff and students. The
acquisition of journal archives ensures that the journal
collections are comprehensive, spanning access from volume
1 through to present…

Impact of the data:
• The data was provided to library administration and used in
communications with various administrative and oversight
groups across campus.
• In terms of internal use, this kind of analysis supported their
ability to monitor performance and guide their future
approaches in dealing with this kind of content.
• Determining which online journal archive collections have
provided the best initial ROI and what is the impact of those
that are drawing on their limited budgets year after year with
their annual maintenance fees is key to these approaches.

Case 2: AIM-Knowledge Resource
Center
EBSCO Ebook Survey
Conducted last May 2014
• Objectives: to determine the level of awareness of
KRC users about ebooks and to determine major
considerations in the selection of an ebook platform.
• Methodology: Online survey (via Google
Docs/Forms)
• Respondents: 32 faculty, students, staff, alumni

Results
Major considerations in the selection of a potential
ebook platform:
• Remote (off-campus) access;
• Downloading of the entire ebook (offline reading);
• Copying/pasting, printing, downloading or saving
capabilities;
• User friendly;
• Powerful searching capability (searching within the
texts/books)

Case 2: AIM-Knowledge Resource
Center
Online Customer Satisfaction Survey
Conducted last October 2015.
• Objectives: To determine the level of satisfaction of
the various stakeholders on the KRC’s collections,
services, etc.
• Methodology: Online Customer Satisfaction Survey
(via Sharepoint)
• Respondents: 142 faculty, students, staff, alumni

Other data collected
• Monthly library statistics (circulation statistics,
online database usage, ILL/DDS, etc.)
• Suggestion box
• Facebook posts/comments/reviews
• Benchmark data, etc.

Results
Demand for:
• more fiction books
• books on specific topics (national
security and economics)
• up-to-date/diverse reading materials
• more magazines

Data-informed vs. Data-driven?
“Being data-informed is about striking a balance
in which your expertise and understanding of
information plays as great a role in your
decisions as the information itself… you can
apply your own experience to that information
and choose accordingly — even if that means
overriding what the system recommends.”
(Moycotte, 2015)

Conclusion
• Data is a valuable tool. It is has been extremely
helpful and instructive for us to have data on hand to
aid our collections decisions and allow us to
articulate clearly some of the decisions we have
made. Data is a powerful collection management
tool when used in an informed way, but it should not
be the only factor in your decision making.
(Day and Davis, 2010, p.97)

Conclusion
• We (librarians) must keep on enhancing our data skill sets both
in terms of tools (i.e. common desktop applications) and also
competencies and comfort levels in manipulating and
interpreting the data.
• We have to work collaboratively with our colleagues in
improving the skill sets of people not directly involved in
collection management. These collaborations result in valuable
tools to aid collection decision-making. Such collaboration is also
helpful in presenting the complexities of collections issues to
colleagues across the institution.
(Day and Davis, 2010, p.97)

References
“Analytics” (2016). INFORMS. Retrieved from https://www.informs.org/About-
INFORMS/What-is-Analytics
“Collection management” (2016). ODLIS. Retrieved from http://www.abc-
clio.com/ODLIS/odlis_c.aspx
Day, Annette and Davis, Hilary (2010). Collection Intelligence: Using Data Driven
Decision Making in Collection Management. Proceedings of the
Charleston Library Conference. Retrieved from
http://dx.doi.org/10.5703/1288284314822
Dempsey, Lorcan, Malpas, Constance, and Lavoie, Brian (2014). Collection
Directions: Some Reflections on the Future of Library Collections and
Collecting. Portal: Libraries and the Academy, 14(3).
Enis, Matt (2013). Using Data to Shape a Library’s Direction : Data-Driven
Academic Libraries. Retrieved from
http://www.thedigitalshift.com/2013/12/staffing/using-data-shape-
librarys-direction-data-driven-academic-libraries/

References
Evans, G. Edward and Saponaro, Margaret (2012). Collection management basics.
6th ed. Santa Barbara, CA : Libraries Unlimited
Fieldhouse, Maggie and Marshall, Audrey, Eds. (2012). Collection development in
the digital age. UK : Facet Publishing
Johnson, Peggy (2009). Fundamentals of collection development and
management. 2nd ed. USA : ALA
Kaplan, Richard (2012). Building and Managing E-book Collections : A How-to-do-
it Manual for Librarians. Chicago, IL: American Library Association.
Maycotte, H.O. (2015). Be Data-Informed, Not Data-Driven, For Now. Forbes.
Retrieved from
http://www.forbes.com/sites/homaycotte/2015/01/13/data-informed-
not-data-driven-for-now/#6e6f66ab6ff9
Morton-Owens, Emily and Hanson, Karen L. (2012). Trends at a Glance: A
Management Dashboard of Library Statistics. Information Technology
and Libraries. pp: 36-51.

AIM: Data-driven collection management

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