Looking Deeper and QuestioningAssumptionsIdentifying market inefficiencies.Apply and acculturate significant innovation.Question long-established wisdom.Test what is “known” with in-depth analysis, statistical modeling, and new approaches.Emphasize interpersonal skills in leveraging new knowledge and approaches.
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 a combination of speculative and package buying Use is secondary to size, dollars expended, and other input measures Credit to David Lewis (http://ulib.iupui.edu/users/dlewis)
Demand-Driven CollectionsMake information easily, widely, and cheaply availableCollections as drivers of research, teaching, and learningTo make special or unique collections held/managed by the library available to the user community and the world
Demand-Driven – Changing PracticeTension between time-honored role as custodians of scholarship versus enabling digital environment for scholarsNot just PDA – portfolio of approaches, but certainly more responsiveUtilize new tools and techniques to become advanced analystsTruly embrace evidence based decision making Look at how collections are actually used, not at expressed need
Demand-Driven – More AssumptionsLess tolerance for and less investment in lower use general collectionsResource management based increasingly on useModify collecting based on changes in the actual useRisks of doing nothing – newspapers
Demand-Driven – Assertions Rewards of adapting – more used and vital than ever 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
Why So Much Data? 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
Serials Review 2009 – Open, Data-Driven, and Real-Time Analysis Standardized usage data Usage ((07 usage+08 usage/2)+ (where available) (publications*10)+ (citations*5)+ Bibliometrics - publication data (Impact Factor) and citation patterns (e.g Community Feedback ((Weighted LJUR) Ranking x % Match) x Total # Impact factor and eigenfactor Rankings) + 0.1 x # of "1s“ User community feedback via Price/feedback value interactive, database-driven Price/use applications Merge results to filter out top 20% Weigh/calculate/quantify user feedback and bottom 20% Weigh price against multiple data points
Looking closer – Finding balanceAn example - a closer look at print item usageTraditional ILS reporting tools can make this difficultAdvanced analytical tools can helpWhat types of questions can we ask? Should Patron-Driven records not purchased be purged after 2 years? How does print item usage break down? Do print items even get used?
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…
If it’s not used after 2 years…Things begin tolook different
Looking even closer… How does print item use break down? Single circ usage is consistently ~14% Would this change in a PDA only world?
Measurable Uses of the Collection 2009/2010Measurable Uses of the Collection 2009/2010Full-text journal downloads* 3,672,600Database use 1,989,972Print book circulations/renewals 525,430Digital collections requests 471,403E-books 149,815Reserves** 327,267Total Uses 7,136,487* Includes use of NC LIVE full-text content** Includes textbook, print, and e-reserves usage
From Assumptions to Assertions toPractice 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”.