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Collection Intelligence: Using data driven
decision-making in collection management
Annette Day
Hilary Davis
North Carolina State University Libraries
Charleston Conference
November 6, 2010
Todays Presentation
 Using data to inform and articulate collections decisions
 NCSU Libraries projects
 Journal cancellation project
 Collections Views tool
 Return on investment for
journal backfiles
Maintaining a balance
 Articulate and explain our decisions
 Show our collection intelligence
Flickr: RayBanBro66
Data can help
 Data-informed collection management
 Types of Data
 Cost
 Use
 Formats
 Owned or Leased
 Citation and publication patterns
 Impact Factors
 Regional holdings
 Editorial activity
 Ways to use Data
 Show value/ROI
 Use is high and increasing
 Test assumptions about the collections
 Fit and alignment with campus
Flickr: quinn.anya
NCSU Context
 ~31,000 students
 ~8,000 faculty
 $10 million collection budget
 4 million volumes
 1 main library
 4 branch libraries
 Campus Strength Areas
 Engineering, Architecture, Agriculture, Science, Technology,
Veterinary Medicine
Flickr: rshannonsmith, ncsunewsdept, Angela De Marco
Collections Review 2009/2010
Collections Review Project 2009/2010
 15% cut in collections budget = $1.5 million
 Significant journal cuts
 1,112 journals proposed for cancelation
 Cost for each title
 Package bundle or piecemeal?
 Usage statistics
 Impact factor (where available)
 Publication and citation data
 Alternative access points
Gathering Campus Feedback
 Low barrier to entry to encourage feedback
 Created informational website
 Authenticated Webform
 Captured departmental affiliation and rank
 Sortable
 Saveable
 Downloadable
 Admin features
Collection Intelligence: Using data driven decision making in collection management
Collection Intelligence: Using data driven decision making in collection management
Collection Intelligence: Using data driven decision making in collection management
Feedback Received
 1,365 users  700 submitted feedback
 12,710 title rankings
 Lots of data; how to make sense of it all?!
 Weighted approach
 Minimize impact of ranking journals outside discipline/research
 Cost per use
 Additional data metrics
Processing the Feedback
 Weighted Ranking  college affiliation and journal subject
 Favored rankings most closely aligned with a users
research/teaching (weight of 1.0)
 Minimized tangential/unrelated rankings (weight of 0.1)
 Priority to Must keep rank (10 points)
 Multiplied ranking points by the association weight and the
total number of rankings, then summed
 Higher the number, the more campus wants to keep it
Ranking Patron's Department Patron's College % Match
Weighted
Ranking
Weighted
Ranking x
% Match
(Weighted
Ranking x
% Match)
x Total #
Rankings
Sum
((Weighted
Ranking x %
Match) x Total
# Rankings)
Can Cancel Entomology Agriculture and Life Sciences 0.5 1 0.5 7.5 621
Can Cancel Agricultural & Life Sciences Agriculture and Life Sciences 0.5 1 0.5 7.5 621
Can Cancel Agricultural & Life Sciences Agriculture and Life Sciences 0.5 1 0.5 7.5 621
Can Cancel Poultry Science Agriculture and Life Sciences 0.5 1 0.5 7.5 621
Can Cancel Engineering Engineering 0.8 1 0.8 12 621
Can Cancel
Humanities & Social
Sciences Humanities & Social Sciences 0.1 1 0.1 1.5 621
Can Cancel
Physical & Mathematical
Sciences Physical & Mathematical Sciences 1 1 1 15 621
Can Cancel Chemistry
Physical and Mathematical
Sciences 1 1 1 15 621
Can Cancel Chemistry
Physical and Mathematical
Sciences 1 1 1 15 621
Can Cancel Mathematics
Physical and Mathematical
Sciences 1 1 1 15 621
keep if
possible Engineering Engineering 0.8 5 4 60 621
keep if
possible Veterinary Medicine Veterinary Medicine 0.5 5 2.5 37.5 621
Must keep Electrical Engineering Engineering 0.8 10 8 120 621
Must keep Physics Physical & Mathematical Sciences 1 10 10 150 621
Must keep Physics Physical & Mathematical Sciences 1 10 10 150 621
Example: Astronomy Letters
Processing the Feedback  Other metrics
 Cost per use
 Other data points
 Use data
 Impact factor
 Publication and citation data
 Resulting Formula
 Sum of the following:
 Average of 2 most recent years of use data
 Number of cites
 (2 x Number of publications) x (impact factor +1)
 More weight to data points we valued highly and reflected journals
relevance
Journal Title Price 2007 Use
2008
Use
Impact
Factor
LJUR
Pubs
LJUR
Citations
Data
Metric
Cost per
Use
Weighted
Ranking
Environmental Progress $486.00 64 67 1 0 11 24.62 $7 165.2
Robotics and autonomous systems $1,841.00 107 200 0.633 3 12 34.41 $12 536
Computational intelligence $858.00 23 76 1.972 2 4 26.72 $17 536
Sensor Review $2,972.00 156 84 2.40 $25 109.9
Journal of environmental science and
health - part A $3,886.00 99 164 0.967 1 36 79.92 $30 625.3
Information Processing Letters $2,238.00 42 83 0.66 2 10 25.32 $36 378.9
Materials Science and Technology $2,180.00 57 55 0.713 0 0 1.92 $39 1086.4
Separation science and technology $8,678.00 56 172 1.048 0 28 62.01 $76 284.9
Circuits, Systems, and Signal
Processing $1,407.00 12 18 0.456 0 2 3.35 $94 369.9
Distributed and Parallel Databases $927.00 6 11 0.771 0 1 2.07 $109 71.4
Applied Artificial Intelligence $1,485.00 15 12 0.753 1 8 18.00 $110 347.4
Plastics, rubber and composites $1,489.00 11 10 0.431 0.30 $142 80.4
Acta Informatica $1,219.00 4 7 0.8 1 7 16.40 $222 1413.3
Cybernetics and Systems Analysis $3,368.00 8 16 0.24 $281 50.5
International Journal of Satellite
Communications and Networking $412.00 0 2 0.284 0.03 $412 254.8
Chemical Engineering Research and
Design $1,692.00 0 2 0.837 2 22 47.80 $1,692 151.2
Issues/Challenges
 What difficulties did we encounter?
 List of what we subscribe to and costs
 All data not available for every title
 Usage statistics
 Impact factor and publication/citation data
 Processing the data
 Tune out irrelevant rankings
 Imprecise weighting
 Data is instructive but not the final decision point
 Technical skills needed to create webform
Collection Views Database
Collection Views Database Project
 We needed to answer the
following questions:
 How do the NCSU Libraries
expenditures on resources support
the research and teaching needs of
diverse colleges and departments
at NCSU?
 What data exist that might help us
understand how our resource
expenditures look in terms of the
departments we serve?
Flickr: ncsunewsdept, egnowit
Data Types
 Library data
 Expenditure data
 Monographs (Quantity & Cost)
 Firm Order
 Approval Plan
 Serials (Cost)
 Databases (Cost)
 Subject Fund Codes
Examples:
 ENTO  Entomology
 GTEC  General Technology
 NATM  Atmospheric Sciences NRL*
 TDES  Textiles Design
Flickr: hemingway gyro
Data Types
 Academic Department Data
 NCSU Office of University Planning and Analysis
 Faculty Headcount
 Enrolled Student Headcount
 Graduate Students
 Undergraduate Students
 NCSU's Sponsored Programs & Regulatory
Compliance Services
 PhD Degrees Awarded
 Research Grant Income
Flickr: ncsunewsdept
Connecting the Data
 Map subject fund codes to departments
 Connect library expenditures and department demographics (e.g., $x
supports the Physics Dept)
 Present expenditure data and department data side-by-side
 No right way to map codes to departments
 A code could be applied to more than one department
 Expenditures associated with a code applies to departments in full
(no weighting/no splitting)
 Broad and narrow mappings
Collection Intelligence: Using data driven decision making in collection management
Collection Views Database
 An SQL database was created to store the data and the
mappings
 Only have to add new data  not rebuild relationships and
other data
 Flexible output options
 Web
 Custom queries
 Canned queries
 Data Portal
Data Portal
Outputs
Outputs
Outputs
Outputs
Quick Comparison Tool
Uses of Collection Views
 Distribution of collections budget/expenditures across
subject areas
 Is it what we expected?
 Is it in line with our knowledge of how specific
departments/disciplines use library resources?
 Cumulative impacts of collecting decisions over time
 Facilitates discussion on budget allocation
 Graphs and charts provide illustrations of impact
Issues/Challenges
 All depends on the mapping
 Considering adding weighted mappings
 Timely gathering of data
 Campus data not readily available
 SQL database programming skills
 Digital Library Initiatives
Journal Backfiles ROI
Journal Backfiles ROI Project
 Investment in online journal backfiles over many years
 Demonstrate value and impact of these purchases
 Usage statistics
 Fiscal effectiveness
 Non-traditional ROI approach
 Cumulative cost of backfiles compared to cumulative use
 Lower cost/use over time
Flickr: cambodia4kidsorg
Results!
$0.00
$1.00
$2.00
$3.00
$4.00
$5.00
$6.00
$7.00
2004 2005 2006 2007 2008 2009
cost/use
year
ROI - All backfiles: Cumulative cost over cumulative use
(first year of use includes one time price)
 Over 100 downloads daily (2008 and 2009 use data)
 Historical ROI is $1.07
How we calculated the metrics
 Data Sources
 Full text article downloads
 Cost data
 Every backfile purchased since 2003
 Initial purchase cost and annual fees
 Calculations
 Initial cost and annual fees carried over through years
 Cost divided by cumulative usage
One
Time
Price
2003
Use
Data
2003
Annual
Fee
2004
Use
Data
2004
Annual
Fee
2005
Use
Data
2005
Annual
Fee
2006
Use
Data
2006
Annual
Fee
2007
Use
Data
2007
Annual
Fee
2008
Use
Data
2008
Annual
Fee
2009
Use
Data
2009
$40,800 $800 $800 3145 $800 3253 $800 3918 $1,095 4697 $1,095 2871
Example
 RSC Archive
 Calculations
 ROI 2005
 Cumulative Cost/ Cumulative Use
 (One Time Price + Annual Fee 2004 + Annual Fee 2005)/Use data
2005
 no use available prior to 2005
 = $42,400/3145
 $13.48
Example
$0.00
$2.00
$4.00
$6.00
$8.00
$10.00
$12.00
$14.00
$16.00
2005 2006 2007 2008 2009
cost/use
ROI - RSC backfile: Cumulative cost over cumulative use
(first year of use includes one time price)
2005 = $13.48
2006 = $6.75
2007 = $4.27
2008 = $3.00
2009 = $2.58
Issues/Challenges
 Non-traditional ROI metric
 May need clarification
 Use data not always available from year of purchase
 Backfile use data is not always separate from current
journals
Final Thoughts
 Data is a powerful tool, but not the end-all, be-all!
 Moving Forward..
 Continued use of data
 Build data skills competencies
 Tools
 Data manipulation and interpretation
 Data dashboard
 Expanded/Improved Tools
 Visualization
THANK YOU!
ANNETTE_DAY@NCSU.EDU
HILARY_DAVIS@NCSU.EDU

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Collection Intelligence: Using data driven decision making in collection management

  • 1. Collection Intelligence: Using data driven decision-making in collection management Annette Day Hilary Davis North Carolina State University Libraries Charleston Conference November 6, 2010
  • 2. Todays Presentation Using data to inform and articulate collections decisions NCSU Libraries projects Journal cancellation project Collections Views tool Return on investment for journal backfiles
  • 3. Maintaining a balance Articulate and explain our decisions Show our collection intelligence Flickr: RayBanBro66
  • 4. Data can help Data-informed collection management Types of Data Cost Use Formats Owned or Leased Citation and publication patterns Impact Factors Regional holdings Editorial activity Ways to use Data Show value/ROI Use is high and increasing Test assumptions about the collections Fit and alignment with campus Flickr: quinn.anya
  • 5. NCSU Context ~31,000 students ~8,000 faculty $10 million collection budget 4 million volumes 1 main library 4 branch libraries Campus Strength Areas Engineering, Architecture, Agriculture, Science, Technology, Veterinary Medicine Flickr: rshannonsmith, ncsunewsdept, Angela De Marco
  • 7. Collections Review Project 2009/2010 15% cut in collections budget = $1.5 million Significant journal cuts 1,112 journals proposed for cancelation Cost for each title Package bundle or piecemeal? Usage statistics Impact factor (where available) Publication and citation data Alternative access points
  • 8. Gathering Campus Feedback Low barrier to entry to encourage feedback Created informational website Authenticated Webform Captured departmental affiliation and rank Sortable Saveable Downloadable Admin features
  • 12. Feedback Received 1,365 users 700 submitted feedback 12,710 title rankings Lots of data; how to make sense of it all?! Weighted approach Minimize impact of ranking journals outside discipline/research Cost per use Additional data metrics
  • 13. Processing the Feedback Weighted Ranking college affiliation and journal subject Favored rankings most closely aligned with a users research/teaching (weight of 1.0) Minimized tangential/unrelated rankings (weight of 0.1) Priority to Must keep rank (10 points) Multiplied ranking points by the association weight and the total number of rankings, then summed Higher the number, the more campus wants to keep it
  • 14. Ranking Patron's Department Patron's College % Match Weighted Ranking Weighted Ranking x % Match (Weighted Ranking x % Match) x Total # Rankings Sum ((Weighted Ranking x % Match) x Total # Rankings) Can Cancel Entomology Agriculture and Life Sciences 0.5 1 0.5 7.5 621 Can Cancel Agricultural & Life Sciences Agriculture and Life Sciences 0.5 1 0.5 7.5 621 Can Cancel Agricultural & Life Sciences Agriculture and Life Sciences 0.5 1 0.5 7.5 621 Can Cancel Poultry Science Agriculture and Life Sciences 0.5 1 0.5 7.5 621 Can Cancel Engineering Engineering 0.8 1 0.8 12 621 Can Cancel Humanities & Social Sciences Humanities & Social Sciences 0.1 1 0.1 1.5 621 Can Cancel Physical & Mathematical Sciences Physical & Mathematical Sciences 1 1 1 15 621 Can Cancel Chemistry Physical and Mathematical Sciences 1 1 1 15 621 Can Cancel Chemistry Physical and Mathematical Sciences 1 1 1 15 621 Can Cancel Mathematics Physical and Mathematical Sciences 1 1 1 15 621 keep if possible Engineering Engineering 0.8 5 4 60 621 keep if possible Veterinary Medicine Veterinary Medicine 0.5 5 2.5 37.5 621 Must keep Electrical Engineering Engineering 0.8 10 8 120 621 Must keep Physics Physical & Mathematical Sciences 1 10 10 150 621 Must keep Physics Physical & Mathematical Sciences 1 10 10 150 621 Example: Astronomy Letters
  • 15. Processing the Feedback Other metrics Cost per use Other data points Use data Impact factor Publication and citation data Resulting Formula Sum of the following: Average of 2 most recent years of use data Number of cites (2 x Number of publications) x (impact factor +1) More weight to data points we valued highly and reflected journals relevance
  • 16. Journal Title Price 2007 Use 2008 Use Impact Factor LJUR Pubs LJUR Citations Data Metric Cost per Use Weighted Ranking Environmental Progress $486.00 64 67 1 0 11 24.62 $7 165.2 Robotics and autonomous systems $1,841.00 107 200 0.633 3 12 34.41 $12 536 Computational intelligence $858.00 23 76 1.972 2 4 26.72 $17 536 Sensor Review $2,972.00 156 84 2.40 $25 109.9 Journal of environmental science and health - part A $3,886.00 99 164 0.967 1 36 79.92 $30 625.3 Information Processing Letters $2,238.00 42 83 0.66 2 10 25.32 $36 378.9 Materials Science and Technology $2,180.00 57 55 0.713 0 0 1.92 $39 1086.4 Separation science and technology $8,678.00 56 172 1.048 0 28 62.01 $76 284.9 Circuits, Systems, and Signal Processing $1,407.00 12 18 0.456 0 2 3.35 $94 369.9 Distributed and Parallel Databases $927.00 6 11 0.771 0 1 2.07 $109 71.4 Applied Artificial Intelligence $1,485.00 15 12 0.753 1 8 18.00 $110 347.4 Plastics, rubber and composites $1,489.00 11 10 0.431 0.30 $142 80.4 Acta Informatica $1,219.00 4 7 0.8 1 7 16.40 $222 1413.3 Cybernetics and Systems Analysis $3,368.00 8 16 0.24 $281 50.5 International Journal of Satellite Communications and Networking $412.00 0 2 0.284 0.03 $412 254.8 Chemical Engineering Research and Design $1,692.00 0 2 0.837 2 22 47.80 $1,692 151.2
  • 17. Issues/Challenges What difficulties did we encounter? List of what we subscribe to and costs All data not available for every title Usage statistics Impact factor and publication/citation data Processing the data Tune out irrelevant rankings Imprecise weighting Data is instructive but not the final decision point Technical skills needed to create webform
  • 19. Collection Views Database Project We needed to answer the following questions: How do the NCSU Libraries expenditures on resources support the research and teaching needs of diverse colleges and departments at NCSU? What data exist that might help us understand how our resource expenditures look in terms of the departments we serve? Flickr: ncsunewsdept, egnowit
  • 20. Data Types Library data Expenditure data Monographs (Quantity & Cost) Firm Order Approval Plan Serials (Cost) Databases (Cost) Subject Fund Codes Examples: ENTO Entomology GTEC General Technology NATM Atmospheric Sciences NRL* TDES Textiles Design Flickr: hemingway gyro
  • 21. Data Types Academic Department Data NCSU Office of University Planning and Analysis Faculty Headcount Enrolled Student Headcount Graduate Students Undergraduate Students NCSU's Sponsored Programs & Regulatory Compliance Services PhD Degrees Awarded Research Grant Income Flickr: ncsunewsdept
  • 22. Connecting the Data Map subject fund codes to departments Connect library expenditures and department demographics (e.g., $x supports the Physics Dept) Present expenditure data and department data side-by-side No right way to map codes to departments A code could be applied to more than one department Expenditures associated with a code applies to departments in full (no weighting/no splitting) Broad and narrow mappings
  • 24. Collection Views Database An SQL database was created to store the data and the mappings Only have to add new data not rebuild relationships and other data Flexible output options Web Custom queries Canned queries Data Portal
  • 31. Uses of Collection Views Distribution of collections budget/expenditures across subject areas Is it what we expected? Is it in line with our knowledge of how specific departments/disciplines use library resources? Cumulative impacts of collecting decisions over time Facilitates discussion on budget allocation Graphs and charts provide illustrations of impact
  • 32. Issues/Challenges All depends on the mapping Considering adding weighted mappings Timely gathering of data Campus data not readily available SQL database programming skills Digital Library Initiatives
  • 34. Journal Backfiles ROI Project Investment in online journal backfiles over many years Demonstrate value and impact of these purchases Usage statistics Fiscal effectiveness Non-traditional ROI approach Cumulative cost of backfiles compared to cumulative use Lower cost/use over time Flickr: cambodia4kidsorg
  • 35. Results! $0.00 $1.00 $2.00 $3.00 $4.00 $5.00 $6.00 $7.00 2004 2005 2006 2007 2008 2009 cost/use year ROI - All backfiles: Cumulative cost over cumulative use (first year of use includes one time price) Over 100 downloads daily (2008 and 2009 use data) Historical ROI is $1.07
  • 36. How we calculated the metrics Data Sources Full text article downloads Cost data Every backfile purchased since 2003 Initial purchase cost and annual fees Calculations Initial cost and annual fees carried over through years Cost divided by cumulative usage
  • 37. One Time Price 2003 Use Data 2003 Annual Fee 2004 Use Data 2004 Annual Fee 2005 Use Data 2005 Annual Fee 2006 Use Data 2006 Annual Fee 2007 Use Data 2007 Annual Fee 2008 Use Data 2008 Annual Fee 2009 Use Data 2009 $40,800 $800 $800 3145 $800 3253 $800 3918 $1,095 4697 $1,095 2871 Example RSC Archive Calculations ROI 2005 Cumulative Cost/ Cumulative Use (One Time Price + Annual Fee 2004 + Annual Fee 2005)/Use data 2005 no use available prior to 2005 = $42,400/3145 $13.48
  • 38. Example $0.00 $2.00 $4.00 $6.00 $8.00 $10.00 $12.00 $14.00 $16.00 2005 2006 2007 2008 2009 cost/use ROI - RSC backfile: Cumulative cost over cumulative use (first year of use includes one time price) 2005 = $13.48 2006 = $6.75 2007 = $4.27 2008 = $3.00 2009 = $2.58
  • 39. Issues/Challenges Non-traditional ROI metric May need clarification Use data not always available from year of purchase Backfile use data is not always separate from current journals
  • 40. Final Thoughts Data is a powerful tool, but not the end-all, be-all! Moving Forward.. Continued use of data Build data skills competencies Tools Data manipulation and interpretation Data dashboard Expanded/Improved Tools Visualization

Editor's Notes

  • #22: For the NCSU demographic data, we worked with the NCSU Office of University Planning and Analysis to get data on number of faculty, students and staff in each department on campus. We also collected data on grant dollars acquired by each department from another database maintained by NCSUs Sponsored Programs office.
  • #23: To connect library expenditures and data about departments Map subject fund codes to departments Make connections between library expenditures and department demographics View expenditure data and department data next to each other Mapping was totally subjective no right way A Subject Identifier could be applied to more than one department. The expenditure amount associated with the Subject Identifier applies to departments in full (no weighting). Broad and narrow mappings control scope of how codes are mapped to departments make it a more broad mapping by including the general fund codes or make it more narrow by limiting to only the more specific fund codes. Example to make all this clear!
  • #35: Investment in online journal backfiles over many years Approximately 90 backfile packages How to demonstrate value and impact of these purchases Usage Fiscal effectiveness i.e. were these good investments for campus Non traditional ROI approach Cumulative cost of archives compared to cumulative use Lower cost per use over year Investment in backfiles pays for itself over time