Tennessee Higher Education and
the Use of Decision Support
Systems in Strategic Planning
and Decision Making
Jeff Hinds
PR...
Abstract

Business Intelligence products and processes have helped public
and private organization to identify opportuniti...
 Why this topic?
 Professional background
 The M.P.S. program and its influence

 What resources were used for this to...
Purpose of the study

Introduction

 Determine if decision support systems are being used by higher
education institution...
Higher education share a number of commonalities with business organizations.
Public and Private Sector
businesses
Product...
Types of sources reviewed
 Peer reviewed journal articles
 Non-Peered reviewed articles

 Industry periodical publicati...
 Type of sources that were used

Review of
Literature








Journal articles from multiple industries and discipl...
Examples of source used:
 Callery, C. A. (2012). DATA-DRIVEN DECISION MAKING IN
COMMUNITY COLLEGES: AN INTEGRATIVE MODEL ...
Examples of source not used:
 Briggs, L. L. (2006). Data-Driven Decision-Making >> Data Pioneers.
Retrieved from Campus T...
 BI - Business Intelligence.

Definitions of
Variables

 DSS - Decision Support Systems.
 ETL - extract, translate, and...
 Decision Support Systems including






Definitions of
Variables

Data modeling
business intelligence/analytics
da...
 U.S. Department of Education

 Professional webinars and seminars

Other
Information
Sources

 Discussions with Data W...
Planning a survey
 What questions to ask (purpose of this survey)

 Who should be included

Method of
Investigation

 H...
The survey - What information to ask?
 Does the institutions use any form of Decision support system in
for strategic pla...
The survey - Who should be included?
 Time constraints
 Is there any evidence that decisions support systems are being
u...
Method of
Investigation

Usage of IPEDS data
U. S. Department of Education
Method of
Investigation
IPEDS data from the U. S.
Department of Education
Method of
Investigation
IPEDS data from the U. S.
Department of Education
Method of
Investigation
IPEDS data from the U. S.
Department of Education
Method of
Investigation

 Data from College Navigator
Method of
Investigation
IPEDS data from the U. S.
Department of Education
Method of
Investigation
IPEDS data from the U. S.
Department of Education
Method of
Investigation
IPEDS data from the U. S.
Department of Education
Method of
Investigation
IPEDS data from the U. S.
Department of Education
Possible participants
 All institutions?
 Public Institutions
 Private institutions

 4-Year or 2 Year institutions

M...
National Center for Education Statistics (NCES), there
are 190 Higher Education institutions in the State of
Tennessee
 4...
Selection of an intended population
 Time constraints
 Current scope

Method of
Investigation

Target organizations
 Al...
This would limit the target population to 9 organizations:
 Austin Peay State University
 East Tennessee State Universit...
Communications with Institutions
 Email
 Follow up communications

Method of
Investigation
Survey delivery method
 Paper
 Electronic email
 Web site

Method of
Investigation
Method of
Investigation
Survey collection method
Method of
Investigation
Survey collection method
Method of
Investigation
Survey collection method
Method of
Investigation
Survey collection method
Survey Process
 Allow the survey to be open for a defined period of time
 Invite the target population to participate
 ...
Possible Results
 institutions that respond included complete information on the
survey
 institutions that respond with ...
Results at the end of survey period
 No responses where submitted.

Method of
Investigation
The survey is completely volu...
After the survey was closed
 Communications from one of the institutions about participating.
 Zero participation in the...
Second review of responses
 A total of 4 responses submitted
 Preferred number of submitted responses for the review

 ...
Basic observations about the survey
 the time that the survey is available for institutions to participate
should be long...
 Population size

(9 public 4-year institutions)

 Survey Return rate 44.4%

(4 returns / 9 members in the population)

...
How the information is used:

 “[c]ritical for strategic planning and institutional effectiveness”,
 “… important, becau...
 There is a total of 78 four-year higher education institutions in the
state of Tennessee that report a combined enrollme...
Questions
Acknowledgements
Without the support of my wife, family, and friends, this
program and project would not have been possibl...
Thank you

To my review committee, instructors, and advisors, thank you for
your time, effort and advice at the beginning ...
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Tennessee Higher Education and the Use of Decision Support Systems in Strategic Planning and Decision Making

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Business Intelligence products and processes have helped public and private organization to identify opportunities and trends that are both internal and external. For years the concepts of Business Intelligence, data mining, “Big” data, have become an important part of strategic planning and decision making within many successful organizations. Higher education has been expressing the importance of research, statistical analysis, data modeling, and business decisions based on good information. In fact, higher education institutions have created entire academic programs around these topics. While this information is written and reported within professional periodicals and events, the question stands about how higher education is taking advantage of business intelligence as part of their strategic planning and decision making processes.

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Transcript of "Tennessee Higher Education and the Use of Decision Support Systems in Strategic Planning and Decision Making"

  1. 1. Tennessee Higher Education and the Use of Decision Support Systems in Strategic Planning and Decision Making Jeff Hinds PRST6998 Professional Project December 11, 2013
  2. 2. Abstract Business Intelligence products and processes have helped public and private organization to identify opportunities and trends that are both internal and external. For years the concepts of Business Intelligence, data mining, “Big” data, have become an important part of strategic planning and decision making within many successful organizations. Higher education has been expressing the importance of research, statistical analysis, data modeling, and business decisions based on good information. In fact, higher education institutions have created entire academic programs around these topics. While this information is written and reported within professional periodicals and events, the question stands about how higher education is taking advantage of business intelligence as part of their strategic planning and decision making processes.
  3. 3.  Why this topic?  Professional background  The M.P.S. program and its influence  What resources were used for this topic? Introduction  Academic  Professional  Personal  What was my purpose for this study?  Professional background  Personal interest
  4. 4. Purpose of the study Introduction  Determine if decision support systems are being used by higher education institutions in the state of Tennessee  How Decisions Support Systems (DSS) can benefit higher education institutions within the state of Tennessee
  5. 5. Higher education share a number of commonalities with business organizations. Public and Private Sector businesses Product or service development Student recruitment Talent management Recruitment of qualified instructors Operating procedures Why Decision Support Systems? Program and degree offerings Product sales and advertisements Introduction Higher Education Process management Payment from customers for products or services Payments for tuition, fees, and services Payments to vendors for products and services (electric, water, materials, equipment, etc.) Payments to vendors for products and services (electric, water, equipment, communications, etc.) Stake holders Board of Trustees Regulatory requirements (taxes, auditor, etc.) Regulatory requirements (taxes, auditor, etc.) Competition Competition
  6. 6. Types of sources reviewed  Peer reviewed journal articles  Non-Peered reviewed articles  Industry periodical publications  Professional lectures and symposiums presentations Review of Literature  Online webinars and seminar presentations  Course offering from many higher education institutions  Commercial and non-commercial web sites  Doctoral Dissertations  “Think Tank” white papers and articles
  7. 7.  Type of sources that were used Review of Literature       Journal articles from multiple industries and disciplines Books References from Newspapers Commercial and Non-Commercial web sites Doctoral Dissertations “Think Tank” white papers and articles  Some of the sources that ware not used       Interviews and discussions Books from previous courses Online webinars and seminar presentations Professional lectures and symposiums presentations Professional consultants specializing in leadership Course offerings from many higher education institutions
  8. 8. Examples of source used:  Callery, C. A. (2012). DATA-DRIVEN DECISION MAKING IN COMMUNITY COLLEGES: AN INTEGRATIVE MODEL FOR INSTITUTIONAL EFFECTIVENESS. Dissertations. Review of Literature  Mayer-Schonberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Boston New York: Houghton Mifflin Harcourt.  Power, D. J. (1995-2013). A Brief History of Decision Support Systems. Retrieved Sept. 5, 2012, from Decision Support Systems Resources: http://dssresources.com/history/dsshistoryv28.html  Rowley, J. (2000). Is higher education ready for knowledge management? International Journal of Educational Management, Vol. 14 Iss: 7, 325 - 333.
  9. 9. Examples of source not used:  Briggs, L. L. (2006). Data-Driven Decision-Making >> Data Pioneers. Retrieved from Campus Technology: http://campustechnology.com/articles/2006/10/datadriven decisionmaking--data-pioneers.aspx Review of Literature  Data-Driven Decision Making: A Powerful Tool for School Improvement. (2004). Minneapolis, Minnesota: Sagebrush Corporation.  Petersen, N. J., Davies, R. S., & Spitzer, B. (2006). Improving Data Driven Decision Making through Assessment Literacy for Respondents. Educational Research and Review Vol. 1 (8), 267-271.
  10. 10.  BI - Business Intelligence. Definitions of Variables  DSS - Decision Support Systems.  ETL - extract, translate, and load.  IPEDS - Integrated Postsecondary Education Data System.
  11. 11.  Decision Support Systems including      Definitions of Variables Data modeling business intelligence/analytics data interpretation services Data Mining Predictive analysis  Example Technologies include     Databases Data Warehouses and ETL Data Cubes and Pivot Tables Commercial and open source analytical tools      SAS SPSS DataHero BigML MADlib
  12. 12.  U.S. Department of Education  Professional webinars and seminars Other Information Sources  Discussions with Data Warehousing and Business Intelligent professions  Discussions with Higher Education Information Technology leadership
  13. 13. Planning a survey  What questions to ask (purpose of this survey)  Who should be included Method of Investigation  How long should the survey be available  How to conduct the survey or collect the information
  14. 14. The survey - What information to ask?  Does the institutions use any form of Decision support system in for strategic planning and operations?  How important would you rate the information in the decision making process? Method of Investigation  Is the information used for strategic planning and measuring?  What type of information is being used or defined?
  15. 15. The survey - Who should be included?  Time constraints  Is there any evidence that decisions support systems are being used in higher education institutions in the state of Tennessee  Possible participants Method of Investigation  Selection of a intended population
  16. 16. Method of Investigation Usage of IPEDS data U. S. Department of Education
  17. 17. Method of Investigation IPEDS data from the U. S. Department of Education
  18. 18. Method of Investigation IPEDS data from the U. S. Department of Education
  19. 19. Method of Investigation IPEDS data from the U. S. Department of Education
  20. 20. Method of Investigation  Data from College Navigator
  21. 21. Method of Investigation IPEDS data from the U. S. Department of Education
  22. 22. Method of Investigation IPEDS data from the U. S. Department of Education
  23. 23. Method of Investigation IPEDS data from the U. S. Department of Education
  24. 24. Method of Investigation IPEDS data from the U. S. Department of Education
  25. 25. Possible participants  All institutions?  Public Institutions  Private institutions  4-Year or 2 Year institutions Method of Investigation
  26. 26. National Center for Education Statistics (NCES), there are 190 Higher Education institutions in the State of Tennessee  48 public institutions (25.26% of total higher education institutions) Method of Investigation  9 are 4 year institutions (18.75% of public institutions)  39 are 2 year institutions (81.25% of public institutions) (Community Colleges and Tennessee Technology Centers)  142 private institutions (74.74% of total higher education institutions)  90 private for-profit (63.38% of private institutions)  52 private non-profit (36.62% of private institutions)  Note: Some institutions reported by NCES are duplicated as one institution may offer 4-year, 2-year, and < 2-year programs and are counted in each classification.
  27. 27. Selection of an intended population  Time constraints  Current scope Method of Investigation Target organizations  All public 4-year institutions in the state of Tennessee
  28. 28. This would limit the target population to 9 organizations:  Austin Peay State University  East Tennessee State University Method of Investigation  Middle Tennessee State University  Tennessee State University  Tennessee Technological University  The University of Tennessee  The University of Tennessee at Chattanooga  The University of Tennessee-Martin  University of Memphis
  29. 29. Communications with Institutions  Email  Follow up communications Method of Investigation
  30. 30. Survey delivery method  Paper  Electronic email  Web site Method of Investigation
  31. 31. Method of Investigation Survey collection method
  32. 32. Method of Investigation Survey collection method
  33. 33. Method of Investigation Survey collection method
  34. 34. Method of Investigation Survey collection method
  35. 35. Survey Process  Allow the survey to be open for a defined period of time  Invite the target population to participate  Scheduled follow up communications  Close the survey and review the results Method of Investigation
  36. 36. Possible Results  institutions that respond included complete information on the survey  institutions that respond with only some or partial information Method of Investigation  the response data will consist of both complete and partial information  all institutions choose not to participate in the survey.
  37. 37. Results at the end of survey period  No responses where submitted. Method of Investigation The survey is completely voluntary. This is a valid possible result to the survey.
  38. 38. After the survey was closed  Communications from one of the institutions about participating.  Zero participation in the survey was determined to be incomplete for the study.  Survey was reopened Method of Investigation  Addition email communications to invite organizations to participate in the survey.  Phone communications to follow up the email communications
  39. 39. Second review of responses  A total of 4 responses submitted  Preferred number of submitted responses for the review  Number of responses that used validation code Method of Investigation  How responses were verified
  40. 40. Basic observations about the survey  the time that the survey is available for institutions to participate should be longer.  information about the survey window should be included and stated clearly in the invitation communication. Findings  the invitation communication to the target population may receive a better response if the communication was communicated with someone other than the senior decision maker, president/chancellor, of the organization.  while attempting to keep the initial communications with the target population short, more information about the study and its purpose should be included.  because of the limited responses that have been submitted, the use of the verification code was note needed
  41. 41.  Population size (9 public 4-year institutions)  Survey Return rate 44.4% (4 returns / 9 members in the population)  Margin of error 33.3% (1 / SqRoot of members in the population ) Findings  75% of those that responded to the survey are utilizing some form of decision support system within their organization for strategic planning and operations.  50% of the respondents indicated they are neutral to the importance of the information from the decision support system in the decision making process.  there is evidence that would support that some of the four-year public higher education institutions in the state of Tennessee is using some form of decision support system in their strategic planning and operational processes.
  42. 42. How the information is used:  “[c]ritical for strategic planning and institutional effectiveness”,  “… important, because we use it to monitor implementation of our plan and to assess the results…”, Findings  “… used mainly to show process toward strategic objectives.”
  43. 43.  There is a total of 78 four-year higher education institutions in the state of Tennessee that report a combined enrollment greater than 240,000 students.  This study is not sufficient to draw any conclusions about the use of decision support systems for strategic planning in higher education within the state of Tennessee. Conclusions  This study does indicate that further studies would be justified to determine how decision support systems are being used in the strategic planning and decision making processes
  44. 44. Questions
  45. 45. Acknowledgements Without the support of my wife, family, and friends, this program and project would not have been possible. This has been a difficult journey trying to balance family, work, and school. Many times it seems there was no balance. Either work or school would take priority leaving very little time for anything else. Many times my friends and family would provide support with listening and ideas when I was looking for additional information or places to find information. My wife has been a rock that has kept me grounded in my studies throughout this program. She has helped me keep on track when I had doubts about myself or what I was attempting to accomplish when I enrolled in the program. My son and daughter have been very patient and supportive with a quick joke for me to go and do my homework. Sounds funny coming from a high school kid directed to their “Padre”.
  46. 46. Thank you To my review committee, instructors, and advisors, thank you for your time, effort and advice at the beginning and throughout the entire program. Your assistances has been invaluable.
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