gathering decision making info elaina norlin and patricia morris university of arizona, usa
TRANSCRIPT
Gathering Decision Making Info
Elaina Norlin and Patricia Morris
University of Arizona, USA
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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!
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Actionable Data: The Presentation AGENDA
Quantitative and Qualitative Research
Pie in the Ski Thinking Case Studies
– IRC– SET– Access Plus– DLIG– Needs Assessment
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Actionable Data: The Presentation AGENDA
Quantitative and Qualitative Research
Pie in the Skyk Thinking Case Studies
– IRC– SET– Access Plus– DLIG– Needs Assessment
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Quantitative and Qualitative Research
Quantitative vs. Qualitative?
How Do They Differ?
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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.
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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)
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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.
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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!!!
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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.
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Actionable Data: The Presentation AGENDA
Pie in the Ski Thinking Case Studies
– IRC– SET– Access Plus– DLIG– Needs Assessment
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Pie in the Sky Thinking
We work hard!So we deserve pie!What would your pie like look
regarding data management in your library?
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Pie in the Sky Thinking
One big virtual Data pieAdding data would be easyIt would be well organized It would accessible 24/7It would be accessible from
anywhere
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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:
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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.”
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Pie in the Sky Thinking
In addition she notes other issues:– data migration– upgrade issues– database fields– metadata (data about data fields)– data consistency
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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!
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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.”
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Actionable Data: The Presentation AGENDA
Case Studies– IRC– SET– Access Plus– DLIG– Needs Assessment
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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.
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
CASE STUDIES: IRC
Sources of data for the matrix con’t:– World book publishing by LC class– Linkage tables connecting LC class and fund linesAll these elements are assigned a rank and through the magic of formulas produce whatis a starting place for a data informed decisionfor each fund line’s Fiscal Year budget.
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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.
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Actionable Data: The Presentation AGENDA
Case Studies– SET– Access Plus– DLIG– Needs Assessment
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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!
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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.
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Actionable Data: The Presentation AGENDA
Case Studies– Access Plus– DLIG– Needs Assessment
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Access Plus: Old Sabio
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Access Plus: Work in Progress
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Access Plus: Work in Progress
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Access Plus: Final Product
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Access Plus: Future Thinking
Usability Testing on the Inner Pages (indexes)
Electronic Journals --problematic
Multi-search Proposes to have a full
time “Access” person
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Actionable Data: The Presentation AGENDA
Case Studies– DLIG– Need Assessment
Conclusion
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
DLIG
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
DLIG
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Actionable Data: The Presentation AGENDA
Case Studies– Need Assessment
Conclusion
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Actionable Data: The Presentation AGENDA
– Conclusion
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
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
March 21, 2000Actionable Data: Elaina Norlin and Patricia Morris
Conclusion- What Does it all mean?
When you talk to people and something is obviously wrong.don’t 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..it’s a 9 months too long!