Collection Intelligence: Using data driven
decision-making in collection management
Annette Day
Hilary Davis
North Carolin...
Today’s Presentation
 Using data to inform and articulate collections decisions
 NCSU Libraries’ projects
 Journal canc...
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 an...
NCSU Context
 ~31,000 students
 ~8,000 faculty
 $10 million collection budget
 4 million volumes
 1 main library
 4 ...
Collections Review 2009/2010
Collections Review Project 2009/2010
 15% cut in collections budget = $1.5 million
 Significant journal cuts
 1,112 jou...
Gathering Campus Feedback
 Low barrier to entry to encourage feedback
 Created informational website
 Authenticated Web...
Feedback Received
 1,365 users  700 submitted feedback
 12,710 title rankings
 Lots of data; how to make sense of it a...
Processing the Feedback
 Weighted Ranking – college affiliation and journal subject
 Favored rankings most closely align...
Ranking Patron's Department Patron's College % Match
Weighted
Ranking
Weighted
Ranking x
% Match
(Weighted
Ranking x
% Mat...
Processing the Feedback – Other metrics
 Cost per use
 Other data points
 Use data
 Impact factor
 Publication and ci...
Journal Title Price 2007 Use
2008
Use
Impact
Factor
LJUR
Pubs
LJUR
Citations
Data
Metric
Cost per
Use
Weighted
Ranking
Env...
Issues/Challenges
 What difficulties did we encounter?
 List of what we subscribe to and costs
 All data not available ...
Collection Views Database
Collection Views Database Project
 We needed to answer the
following questions:
 How do the NCSU Libraries‘
expenditures...
Data Types
 Library data
 Expenditure data
 Monographs (Quantity & Cost)
 Firm Order
 Approval Plan
 Serials (Cost)
...
Data Types
 Academic Department Data
 NCSU Office of University Planning and Analysis
 Faculty Headcount
 Enrolled Stu...
Connecting the Data
 Map subject fund codes to departments
 Connect library expenditures and department demographics (e....
Collection Views Database
 An SQL database was created to store the data and the
mappings
 Only have to add new data – n...
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?
...
Issues/Challenges
 All depends on the mapping
 Considering adding weighted mappings
 Timely gathering of data
 Campus ...
Journal Backfiles ROI
Journal Backfiles ROI Project
 Investment in online journal backfiles over many years
 Demonstrate value and impact of t...
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: ...
How we calculated the metrics
 Data Sources
 Full text article downloads
 Cost data
 Every backfile purchased since 20...
One
Time
Price
2003
Use
Data
2003
Annual
Fee
2004
Use
Data
2004
Annual
Fee
2005
Use
Data
2005
Annual
Fee
2006
Use
Data
200...
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: Cu...
Issues/Challenges
 Non-traditional ROI metric
 May need clarification
 Use data not always available from year of purch...
Final Thoughts
 Data is a powerful tool, but not the end-all, be-all!
 Moving Forward…..
 Continued use of data
 Build...
THANK YOU!
ANNETTE_DAY@NCSU.EDU
HILARY_DAVIS@NCSU.EDU
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
Collection Intelligence: Using data driven decision making in collection management
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Collection Intelligence: Using data driven decision making in collection management

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Annette Day and Hilary Davis; NCSU Libraries; Charleston Conference 2010

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  • 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 NCSU’s Sponsored Programs office.
  • 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!
  • 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
  • Collection Intelligence: Using data driven decision making in collection management

    1. 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. 2. Today’s 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. 3. Maintaining a balance  Articulate and explain our decisions  Show our collection intelligence Flickr: RayBanBro66
    4. 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. 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
    6. 6. Collections Review 2009/2010
    7. 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. 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
    9. 9. 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
    10. 10. Processing the Feedback  Weighted Ranking – college affiliation and journal subject  Favored rankings most closely aligned with a user’s 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
    11. 11. 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
    12. 12. 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 journal’s relevance
    13. 13. 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
    14. 14. 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
    15. 15. Collection Views Database
    16. 16. 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
    17. 17. 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
    18. 18. 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
    19. 19. 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
    20. 20. 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
    21. 21. Data Portal
    22. 22. Outputs
    23. 23. Outputs
    24. 24. Outputs
    25. 25. Outputs
    26. 26. Quick Comparison Tool
    27. 27. 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
    28. 28. 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
    29. 29. Journal Backfiles ROI
    30. 30. 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
    31. 31. 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
    32. 32. 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
    33. 33. 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
    34. 34. 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
    35. 35. 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
    36. 36. 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
    37. 37. THANK YOU! ANNETTE_DAY@NCSU.EDU HILARY_DAVIS@NCSU.EDU

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