Moneyball, Libraries, and more - Ithaka collections presentation

  • 553 views
Uploaded on

 

More in: Technology , Education
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
553
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
12
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide
  • Moneyball, the Extra 2%, and What Baseball Management Can Teach Us About Fostering Innovation in Managing Collections I have been thinking about the intersection of modern baseball management with innovation in managing collections quite a bit and have been trying to have a bit of fun with it. I was not sure the Moneyball analogy completely worked, but then Anne Kenney brought it up independently in a conversation about libraries and Brad Pitt made it into a movie – so I pressed-on assuming if it is good enough for Anne and Brad - then it is a good enough comparison for me. You might conclude that this is a forced analogy that means I spend too much time thinking about baseball and my wife would whole-heartedly agree, but I do think the comparisons about innovation developing and managing baseball teams and research library collections work well. There is much we can learn. Advance slide.
  • This is the title my boss recommended. Advance slide.
  • Briefly - Moneyball is the story of the success of the Oakland A’s in the late 1990’s and early 2000’s and The Extra 2% is the less popular, but just as important, story of the success of the Tampa Bay Rays from 2007 to today. Both teams had to struggle against inferior revenue streams, dysfunctional markets, and competition with significantly better financing and resource basis – so the New York Yankees and Boston Red Sox for example. Both had to find new approaches to building and running a successful baseball team in order to compete, maintain vitality, and be successful in serving their primary users – fans. I would argue that today many libraries find themselves in a very similar position.
  • My wife asked me if this guy was supposed to be a metaphor for looking deeper and analytical thinking and I can tell you no. In baseball looking deeper and questioning assumptions means questioning reliance on old fashioned numbers and axioms that do not tell you much about past, current, or likely future success - using statistical analysis to identify market inefficiencies. It also means not relying on grizzled hardened scouts – that is what this guy is – who think can predict performance based on what they see and rely on paradigms that dominated the game until the late 1990's rather than what they can test, question, analyze, and project based on good information - and then breeding that into every element of the organizational culture. I suppose this guy could easily be a grizzled hardened bibliographer as well – maybe he works in one of your libraries, but I doubt it – most of us are well past the grizzled back-room selector models, but we still hold onto many of those old practices as we turn our big ships to fully embrace the digital environment. Many of us are changing practice and working to fully embrace digital content as the primary means for discovering and disseminating information, but we are not doing it fast enough and would benefit from looking outside of libraries for ideas to expedite the pace of innovation in collections. Here are some specific ideas I think research libraries can increasingly employ.
  • Peak of supply-side collections occurred in 1990's. 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).
  • The shift from supply-side development of collections (print-based, large inventories, unpredictable supply and demand periods, and judging collections largely on size) to more directly demand-driven approach has been underway for quite a while, but the pace is definitely accelerating for many reasons, including: Technology and increasing amount of content on open networks Changes in publishing Supply-chain capabilities and print-on-demand Increased accountability for libraries and higher education, but One reason trumps all others – economics The combination of dysfunctional markets for scholarly information and significant economic downturn have brought many of us to the level of the Oakland A’s and Tampa Bay Rays – inexorable economic factors driving us to increasingly innovate in our management of collections, question established norms, and develop new models for managing collections.
  • Collections for the sake of collections is dead, but much of the mission for academic and research library remains... 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
  • Tension between time-honored role as custodians of scholarship versus enabling digital environment for scholars. Not just PDA – portfolio of approaches, but certainly more responsive and more analytical. Utilize new tools and techniques to become advanced analysts. Truly embrace evidence based decision making. Look at how collections are actually used, not at expressed need.
  • More assumptions Less tolerance for and less investment in lower use general collections Resource management based increasingly on use Modify collecting based on changes in the actual use Risks of doing nothing – newspapers “ I don’t know. Nobody knows. We’re collectively living through 1500, when it’s easier to see what’s broken than what will replace it. The internet turns 40 this fall. Access by the general public is less than half that age. Web use, as a normal part of life for a majority of the developed world, is less than half that age. We just got here. Even the revolutionaries can’t predict what will happen.” – Clay Shirky If we can not predict - then how can we at least better understand what is going on with the changing nature of scholarship.
  • Anybody know who these guys are - good - random fantasy football draft picture I pulled off of Google. Tell fantasy football story. 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 - Ithaka studies are interesting - libraries need healthy fear they provide - but best do not need to worry - gateway role can decline if demonstrate use
  • 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 It is also something that not everyone was doing - so you can reap some competitive advantage.
  • We developed a database to solicit community input on journal titles, weight community input, and pair it with eigenfactor, snip, usage data, publication rates, and citation rates among our community to develop a weighted valuation for each journal title in our collection. Ivy and colleagues at CDL have done much of this work as well. Piggybacked on work at University of Washington and CDL to roll the data up to analyze, manage, and negotiate packages with publishers
  • 13,000+ points of data from 700 users – how do you at least run an initial filter through that data? Relationships between usage data and community feedback data. Way more open and data-driven process than ever before where capturing data and feedback and analyzing it in real-time. We developed our ERM system to ingest statistics related to every continuing resource we own and enables a myriad of reports based on usage rates and expenditures - and here is the key - we use it consistently, not just when we have cuts or journal reviews and annually review the bottom 20% of our continuing resources based on overall performance and make decisions accordingly to enable investment in emerging areas.
  • We put our entire book collection and circulation activity into SAS. We ran a myriad of statistical analyses that have informed everything from cutting approval plan expenditures down by 45%, scoping our demand-driven e-book collections program, and heavily influencing and reducing our item-by-item book selection. Saved hundreds of thousands annually on our various book collecting efforts, improved our usage rates, broadened our scope in targeted subjects, and reinvested in new areas.
  • We developed a database where we actively map collections expenditures by fund/subject to university data points such as number of phd’s, faculty members, enrollment, grant dollars, and can manipulate those at the research center, departmental, and college level. We can use that real-time information to mine for overvalued areas, under-invested subjects, test ourselves against university expenditures and trends, and advocate for the preservation and addition of library resources. It has been very effective in balancing our collections portfolio and helping to advocate for more funds.
  • Moving quickly from changing assumptions to assertions and changing practice. Do whatever you have to do to grow, develop, or hire statistical analysts to be part of your collections team. Change your staff and your staffing models with at least one new hire. People are the most important tool for innovation - that is nothing new - but it requires real change with staff to truly change culture and breed innovation. Statistical tools - build, adopt, adapt, and start using them broadly across the collections program. Some brief examples. Partnering with digital library colleagues. Digital library innovation has not been applied enough to the business of building and managing collections. We need to do more to foster collaboration among collections and digital library colleagues. A couple of quick examples. These efforts have been imperfect and we can get better - but we are making progress and taking Steve Jobs advice and stealing ideas from other libraries whenever we can. Develop positive arbitrage Positive arbitrage is a concept at the heart of The Extra 2%. the practice of taking advantage of a price difference between two or more markets or a transaction that involves no negative cash flow at any probabilistic or temporal state and a positive cash flow in at least one state. It's use in the book and my use here is kind of a bastardization of the term, but the key concept is finding and generating value in everything you do with collections. Library collections work has excelled at creating negative arbitrage and value – we exchanged dollars for books that did not circulate, purchased journals we did not want, and often signed contracts that favored the publishers and vendors we work with. Sometimes there are good reasons for doing this, but often it was due to basing decisions on past practice and not having the type of data and analysis that would help us find the best value in expending collections resources – help us find the extra 2% or 5% or 10% to maximize the impact of collections. Free collections funds to invest in new and emerging areas. Free funds to invest in digital media, collaborative efforts, and the kinds of great digital library projects we have heard about here that are non-traditional, but critical areas of value for scholars and libraries. If you budget for experimentation and reward innovation (even innovative failures) - then you can reinforce your efforts to change the staff, change the culture to consistently question assumptions, experiment with new approaches, generate innovation, and bring more content and value to users. Both of the teams referenced today had people with dynamic interpersonal skills to carry forward the combination of analytical people and analytical tools to drive collections work. They met resistance as some constituencies dug in, but persevered by demonstrating impact and working the necessary political channels to create buy-in. Libraries tend to be very good at generating positive political arbitrage with faculty and other users. That needs to be maintained as we work on new models. For analytical approaches to be successfully applied in local environments - the user community and especially faculty - have to accept them in time. Analysis does not obviate the political and interpersonal work of managing collections, but I am certain that if we use analytical tools to increase access to the collections users want in the format in which they want to consume them, then just like winning in baseball, your fan-base will buy-in.

Transcript

  • 1. Moneyball, the Extra 2%, and What BaseballManagement Can Teach Us About Fostering Innovation in Managing Collections Greg Raschke North Carolina State University Ithaka March 8, 2012
  • 2. Moneyball and More...
  • 3. Baseball to Collections Context
  • 4. Looking Deeper and QuestioningAssumptionsIdentifying 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.
  • 5. 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)
  • 6. Supply-Side to Demand-Driven
  • 7. Demand-Driven Collections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
  • 8. Demand-Driven – Changing PracticeTension between time-honored role as custodians of scholarship versus enabling digital environment for scholarsNot just PDA – portfolio of approaches, but certainly more responsiveUtilize new tools and techniques to become advanced analystsTruly embrace evidence based decision making  Look at how collections are actually used, not at expressed need
  • 9. Demand-Driven – More AssumptionsLess tolerance for and less investment in lower use general collectionsResource management based increasingly on useModify collecting based on changes in the actual useRisks of doing nothing – newspapers
  • 10. 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
  • 11. 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
  • 12. 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
  • 13. Looking closer – Finding balanceAn 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 2 years? How does print item usage break down? Do print items even get used?
  • 14. 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…
  • 15. If it’s not used after 2 years…Things begin tolook different
  • 16. Looking even closer… How does print item use break down? Single circ usage is consistently ~14% Would this change in a PDA only world?
  • 17. Expenditures to University Data
  • 18. Expenditures to University Data
  • 19. Expenditures to University Data
  • 20. Expenditures to University Data
  • 21. 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
  • 22. 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”.