gathering decision making info elaina norlin and patricia morris university of arizona, usa

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

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: The Presentation AGENDA

Quantitative and Qualitative Research

Pie in the Ski Thinking Case Studies

– IRC– SET– Access Plus– DLIG– Needs Assessment

Pie in the Skyk Thinking Case Studies

Quantitative vs. Qualitative?

How Do They Differ?

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.

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)

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.

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!!!

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.

Pie in the Ski Thinking Case Studies

Pie in the Sky Thinking

We work hard!So we deserve pie!What would your pie like look

regarding data management in your library?

One big virtual Data pieAdding data would be easyIt would be well organized It would accessible 24/7It would be accessible from

anywhere

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:

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.”

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

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!

“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.”

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

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.

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

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.

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.

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

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!

The complexities of managing scitech serials due: – to inflation, – the need to keep collections dynamic,

– the new “onslaught” of electronic packages

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

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

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

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

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

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

Case Studies– Access Plus– DLIG– Needs Assessment

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

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

Access Plus: Old Sabio

Access Plus: Work in Progress

Access Plus: Final Product

Access Plus: Future Thinking

Usability Testing on the Inner Pages (indexes)

Electronic Journals --problematic

Multi-search Proposes to have a full

time “Access” person

Case Studies– DLIG– Need Assessment

Conclusion

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

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

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

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

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

Case Studies– Need Assessment

Conclusion

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

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

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

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

– Conclusion

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

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!

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