Pie in the Sky Thinking. Now how much time do you have t

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Gathering Decision Making Info
Actionable Data

• Elaina Norlin and Patricia Morris
• University of Arizona, USA

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Real Life Problem Dont Let This Happen To Your
Project

• University of Arizonas Staff Development Funding
  Committee
• Base Budget remains constant every year
• Collected generic quantitative numbers..but did
  not do much needs assessment or future thinking
• Result During the fiscal year the committee ran
  out of money and now need to take time to get
  customer feedback

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What Is Actionable Data??

• Actionable Data is the process of using
  qualitative and /or quantitative data effectively
  and efficiently to make decisions and to be ready
  when top administrators want to take a look at .
• OUTCOMES!

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Actionable Data The Presentation AGENDA

• Quantitative and Qualitative Research
• Pie in the Ski Thinking
• Case Studies
• IRC
• SET
• Access Plus
• DLIG
• Needs Assessment

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Actionable Data The Presentation AGENDA

• Quantitative and Qualitative Research
• Pie in the Skyk Thinking
• Case Studies
• IRC
• SET
• Access Plus
• DLIG
• Needs Assessment

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Quantitative and Qualitative Research

• Quantitative vs. Qualitative?
• How Do They Differ?

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Quantitative and Qualitative Research

• Definitions- How Do They Differ?
• Quantitative data methodologies usually provide
  the terminology or numerical scales within which
  respondents have to restrict or limit their
  answers.

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Quantitative and Qualitative Research

• Qualitative research begins by accepting that
  there is a range of different ways of making
  sense of the world and is concerned with
  discovering the meanings seen by those who are
  being researched and with understanding their
  view rather than that of the researchers.
  (Jones 19952)

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Quantitative and Qualitative Research

• This method allows for greater respondent
  influence in the interpretative stage of data
  analysis. (Qualitative)
• In practice, however, there are still several
  issues to consider when using either method as
  there always exists an opportunity for
  misinterpretation by the researcher.

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Quantitative and Qualitative Research

• Both quantitative and qualitative methods seek
  reliable and valid results and should be used as
  complementary data sources.
• Quantitative vs. Qualitative-gtNOT
• We need them Both!!!

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Quantitative and Qualitative Research

• Qualitative Research focuses on descriptive words
  and symbols and usually involves observing
  consumers in a marketing setting or questioning
  them about their product or service consumption
  experiences..Qualitative research is most
  effective when combined with quantitative and
  target marketing.
• A.C. Nielsen Co.

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Actionable Data The Presentation AGENDA

• Pie in the Ski Thinking
• Case Studies
• IRC
• SET
• Access Plus
• DLIG
• Needs Assessment

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Pie in the Sky Thinking

• We work hard!
• So we deserve pie!
• What would your pie like look regarding data
  management in your library?

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Pie in the Sky Thinking

• One big virtual Data pie
• Adding data would be easy
• It would be well organized
• It would accessible 24/7
• It would be accessible from anywhere

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Pie in the Sky Thinking

• Now how much time do you have to analyze and
  interpret all that data?
• Well you could build a thinkbot, or use a report
  generator or how about using data mining
  techniques?
• Well hold on there because there are a multitude
  of issues to contend with first

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Pie in the Sky Thinking

• None of these techniques are plug and play
• According to Sandy Schulman
• Making the transition from older systems
• to accommodate these fascinating new
  possibilities is not just a matter of porting an
  existing database.

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Pie in the Sky Thinking

• In addition she notes other issues
• data migration
• upgrade issues
• database fields
• metadata (data about data fields)
• data consistency

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Pie in the Sky Thinking

• So much to contend with, why bother ?!!
• Read this comment from Ms. Schulman and see if
  you agree why we, who have to be accountable,
  have no choice!

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Pie in the Sky Thinking

• As our databases grow and constantly change, it
  becomes almost impossible to spot trends and
  changing patterns manually, not to mention
  quickly enough to make a difference in optimizing
  collection development, or providing
  up-to-the-minute or new information services.

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Actionable Data The Presentation AGENDA

• Case Studies
• IRC
• SET
• Access Plus
• DLIG
• Needs Assessment

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CASE STUDIES Information Resources Council (IRC)

• UA Libraries info materials oversight grp.
• PURPOSE to support
• the needs of the Library's internal and external
  customers
• by providing leadership, vision, and strategic
  directions
• for information resources development, creation,
  management, and preservation.

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CASE STUDIES IRC

• This group uses a data matrix to inform budget
  allocation decisions
• Sources of data for this matrix include
• The UAs Decision and Planning Support
• system which contains quantitative data on
  faculty and students by department, college,
  degrees granted, etc
• Circulation data by Library of Congress class

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CASE STUDIES IRC

• Sources of data for the matrix cont
• World book publishing by LC class
• Linkage tables connecting LC class
• and fund lines
• All these elements are assigned a rank and
• through the magic of formulas produce what
• is a starting place for a data informed decision
• for each fund lines Fiscal Year budget.

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CASE STUDIES IRC

• Is it perfect? No.
• Are we still working to improve it? YES
• Are we ready when a faculty member or other
  stakeholder asks questions about the budget? YES.
  
• We have more than anecdotal data when asked to be
  accountable for our decisions.

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Actionable Data The Presentation AGENDA

• Case Studies
• SET
• Access Plus
• DLIG
• Needs Assessment

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CASE STUDIES Sci.-Eng. Team Serial Review
Database

• The Science Engineering Team at the UA Libraries
  began building a serial review database in the
  1980s
• It was to provide a one stop location of the
  historical data collected during a journal
  cancellation project.
• Well we realized it was needed for more than that
  function. So it was revived!

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CASE STUDIES Sci.-Eng. Team Serial Review
Database

• The complexities of managing scitech serials due
  
• to inflation,
• the need to keep collections dynamic,
• the new onslaught of electronic packages

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CASE STUDIES Sci.-Eng. Team Serial Review
Database

• all of these issues made it obvious that we
  needed a handy centralized tool to provide data
  to support our journal collection decisions.

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CASE STUDIES Sci.-Eng. Team Serial Review
Database

• The serials review database, began as a data
  dump from the acquisition part of our OPAC into
  a spreadsheet then into Excel and is now an
  Access db which contains
• gt8 Mb, 5129 titles, and 60 data fields

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CASE STUDIES Sci.-Eng. Team Serial Review
Database

• One serials identification table links six
  evaluative and or analytical data tables
• The evaluative data is more qualitative in nature
• The analytical data is quantitative

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CASE STUDIES Sci.-Eng. Team Serial Review
Database

• The 6 data tables are
• Local ISI citation data (LJUR)
• Journal Citation Reports data
• historical cost data
• Top Ten Survey results
• ILL data (InterLibrary Loan)
• current periodical room usage

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CASE STUDIES Sci.-Eng. Team Serial Review
Database

• Is this our pie in the sky?
• NOT YET
• Updating not yet automated
• Not 24/7 availability
• Not yet easy to manipulate
• Data integrity issues

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CASE STUDIES Sci.-Eng. Team Serial Review
Database

• Is it a tool that assists us in being
  accountable?
• YES
• It is a centralized source of organized data
• It provides quantitative data
• It provides qualitative data
• It provides trend data

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Actionable Data The Presentation AGENDA

• Case Studies
• Access Plus
• DLIG
• Needs Assessment

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Access Plus

• Objective Access Plus, originally Access 2000
  was charged with redesigning the library
  interface and incorporating Site Search
• Dilemma Site Search or Multi-search had several
  problems and the library interface needed work

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Access Plus

• Solution Access Plus decided to customize their
  own usability testing to make changes on the
  website and figure out how to integrate Site
  Search
• After several rounds of usability tests, they
  completely changed the library website
• Getting customer feedback made it easier to
  justify making changes and moving forward

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Access Plus Old Sabio
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Access Plus Work in Progress
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Access Plus Work in Progress
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Access Plus Final Product
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Access Plus Future Thinking

• Usability Testing on the Inner Pages (indexes)
• Electronic Journals --problematic
• Multi-search
• Proposes to have a full time Access person

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Actionable Data The Presentation AGENDA

• Case Studies
• DLIG
• Need Assessment
• Conclusion

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DLIG

• Objective The purpose of the Digital Library
  Initiative is to build on the existing base of
  digitization projects, and to develop new
  projects that move the library forward
  strategically.
• These projects will embed the knowledge
  management function within the U of A Digital
  Library positioning us as a leader in technology
• DLIG also include electronic reserves

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DLIG

• Dilemma Initially, Electronic Reserves and DLIG
  took on any projects that came around and now are
  overwhelmed with the growing demand of their
  services and the complexity of the problem
• Electronic Reserves is a popular point for
  library services

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DLIG

• Currently they have a gut level strategy on who
  they will accept projects or not
• Right now they are working on a vision statement
  which clarifies the mission
• Politically the dean accepts the project and uses
  the success of the electronic reserves project
  and technology to request more funds

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DLIG
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DLIG
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DLIG Future Issues

• Electronic Reserves Electronic database which
  allows users to find out status of request and
  average turnaround time accessible on the web
• More staffing but need data to support this
  function and additional funding
• Expects that the demand will increase but not
  ready
• Individual professors are also expecting more
  with the technology

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DLIG Future Issues

• DLIG Need more buy in from the library- library
  education and support, currently too busy to
  really reach out
• Manpower The demand and complexity of the
  projects will require people with more expertise
  to get things done
• Outcomes Has the Dean approval but how much can
  be spent out without knowing its potential cost
  recovery or what the library has to give up

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Actionable Data The Presentation AGENDA

• Case Studies
• Need Assessment
• Conclusion

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Case Study Needs Assessment

• Objective To strengthen the ability of teams
  and the library to identify critical data and to
  collect, manage, share and use data to make
  decisions to meet the needs of their customers
• Outcome Provide the library with a coordinated
  system wide data management system by June 30,
  2000
• Problem You have to hunt around literally to
  find relevant data

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Case Study Needs Assessment

• Needs Assessment Tool Combination of
  qualitative and quantitative research
• Interviewed every functional and cross functional
  teams
• Reviewed and analyzed statistics ARL,
  reference, circulation, ILL, faculty surveys
• Communicated with the dean to find out how she
  receives crucial information

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Case Study Needs Assessment

• Dilemma Not many academic libraries are
  initiating a data management system--not many
  librarians to compare notes
• The types of data we collect--from reference
  statistics to faculty surveys--we cannot find a
  compatible database system that tailors to an
  academic environment

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Case Study Needs Assessment

• Pie in the Sky Goal-seamless data gathering/data
  entry which could generate a report on demand-
  assessable everywhere including intranet-internet
• Solution The U of A is going with Oracle and we
  are going to use Oracle for some components and
  try to design the other parts in house --an
  expensive and time consuming option but the only
  feasible one at the moment

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Actionable Data The Presentation AGENDA

• Conclusion

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Conclusion- What Does it all mean?

• Stop taking needs assessment so seriously that
  you miss the big picture
• Work with other libraries on data management
  systems-take control of your data
• Trust your own instincts then get a second opinion

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Conclusion- What Does it all mean?

• When you talk to people and something is
  obviously wrong.dont spend a year getting
  endless numbers .FIX IT!
• If your spending a year designing a survey,
  finding a hotshot to analyze it and figuring out
  the results..its a 9 months too long!