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Gathering Decision Making Info

Actionable Data. Gathering Decision Making Info. Elaina Norlin and Patricia Morris University of Arizona, USA. Real Life Problem: Don’t Let This Happen To Your Project. University of Arizona’s Staff Development Funding Committee Base Budget remains constant every year

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Gathering Decision Making Info

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  1. Actionable Data Gathering Decision Making Info Elaina Norlin and Patricia Morris University of Arizona, USA

  2. Real Life Problem: Don’t Let This Happen To Your Project • University of Arizona’s 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 Actionable Data: Elaina Norlin and Patricia Morris

  3. 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! Actionable Data: Elaina Norlin and Patricia Morris

  4. Actionable Data: The Presentation AGENDA • Quantitative and Qualitative Research • Pie in the Ski Thinking • Case Studies • IRC • SET • Access Plus • DLIG • Needs Assessment Actionable Data: Elaina Norlin and Patricia Morris

  5. Actionable Data: The Presentation AGENDA • Quantitative and Qualitative Research • Pie in the Skyk Thinking • Case Studies • IRC • SET • Access Plus • DLIG • Needs Assessment Actionable Data: Elaina Norlin and Patricia Morris

  6. Quantitative and Qualitative Research • Quantitative vs. Qualitative? How Do They Differ? Actionable Data: Elaina Norlin and Patricia Morris

  7. 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. Actionable Data: Elaina Norlin and Patricia Morris

  8. 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 1995:2) Actionable Data: Elaina Norlin and Patricia Morris

  9. 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. Actionable Data: Elaina Norlin and Patricia Morris

  10. 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->NOT We need them Both!!! Actionable Data: Elaina Norlin and Patricia Morris

  11. 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. Actionable Data: Elaina Norlin and Patricia Morris

  12. Actionable Data: The Presentation AGENDA • Pie in the Ski Thinking • Case Studies • IRC • SET • Access Plus • DLIG • Needs Assessment Actionable Data: Elaina Norlin and Patricia Morris

  13. Pie in the Sky Thinking • We work hard! • So we deserve pie! • What would your pie like look regarding data management in your library? Actionable Data: Elaina Norlin and Patricia Morris

  14. 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 Actionable Data: Elaina Norlin and Patricia Morris

  15. 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: Actionable Data: Elaina Norlin and Patricia Morris

  16. 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.” Actionable Data: Elaina Norlin and Patricia Morris

  17. Pie in the Sky Thinking • In addition she notes other issues: • data migration • upgrade issues • database fields • metadata (data about data fields) • data consistency Actionable Data: Elaina Norlin and Patricia Morris

  18. 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! Actionable Data: Elaina Norlin and Patricia Morris

  19. 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.” Actionable Data: Elaina Norlin and Patricia Morris

  20. Actionable Data: The Presentation AGENDA • Case Studies • IRC • SET • Access Plus • DLIG • Needs Assessment Actionable Data: Elaina Norlin and Patricia Morris

  21. 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. Actionable Data: Elaina Norlin and Patricia Morris

  22. CASE STUDIES: IRC • This group uses a data matrix to inform budget allocation decisions • Sources of data for this matrix include: • The UA’s 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 Actionable Data: Elaina Norlin and Patricia Morris

  23. CASE STUDIES: IRC • Sources of data for the matrix con’t: • 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 line’s Fiscal Year budget. Actionable Data: Elaina Norlin and Patricia Morris

  24. 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. Actionable Data: Elaina Norlin and Patricia Morris

  25. Actionable Data: The Presentation AGENDA • Case Studies • SET • Access Plus • DLIG • Needs Assessment Actionable Data: Elaina Norlin and Patricia Morris

  26. CASE STUDIES: Sci.-Eng. Team Serial Review Database • The Science Engineering Team at the UA Libraries began building a serial review database in the 1980’s • 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! Actionable Data: Elaina Norlin and Patricia Morris

  27. 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 Actionable Data: Elaina Norlin and Patricia Morris

  28. 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. Actionable Data: Elaina Norlin and Patricia Morris

  29. 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: >8 Mb, 5129 titles, and 60+ data fields Actionable Data: Elaina Norlin and Patricia Morris

  30. CASE STUDIES: Sci.-Eng. Team Serial Review Database • One serial’s identification table links six evaluative and or analytical data tables • The evaluative data is more qualitative in nature • The analytical data is quantitative Actionable Data: Elaina Norlin and Patricia Morris

  31. 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 Actionable Data: Elaina Norlin and Patricia Morris

  32. 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 Actionable Data: Elaina Norlin and Patricia Morris

  33. 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 Actionable Data: Elaina Norlin and Patricia Morris

  34. Actionable Data: The Presentation AGENDA • Case Studies • Access Plus • DLIG • Needs Assessment Actionable Data: Elaina Norlin and Patricia Morris

  35. 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 Actionable Data: Elaina Norlin and Patricia Morris

  36. 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 Actionable Data: Elaina Norlin and Patricia Morris

  37. Access Plus: Old Sabio Actionable Data: Elaina Norlin and Patricia Morris

  38. Access Plus: Work in Progress Actionable Data: Elaina Norlin and Patricia Morris

  39. Access Plus: Work in Progress Actionable Data: Elaina Norlin and Patricia Morris

  40. Access Plus: Final Product Actionable Data: Elaina Norlin and Patricia Morris

  41. Access Plus: Future Thinking • Usability Testing on the Inner Pages (indexes) • Electronic Journals --problematic • Multi-search • Proposes to have a full time “Access” person Actionable Data: Elaina Norlin and Patricia Morris

  42. Actionable Data: The Presentation AGENDA • Case Studies • DLIG • Need Assessment • Conclusion Actionable Data: Elaina Norlin and Patricia Morris

  43. 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 Actionable Data: Elaina Norlin and Patricia Morris

  44. 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 Actionable Data: Elaina Norlin and Patricia Morris

  45. 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 Actionable Data: Elaina Norlin and Patricia Morris

  46. DLIG Actionable Data: Elaina Norlin and Patricia Morris

  47. DLIG Actionable Data: Elaina Norlin and Patricia Morris

  48. 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 Actionable Data: Elaina Norlin and Patricia Morris

  49. 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 Actionable Data: Elaina Norlin and Patricia Morris

  50. Actionable Data: The Presentation AGENDA • Case Studies • Need Assessment • Conclusion Actionable Data: Elaina Norlin and Patricia Morris

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