IntelliSolve Group, Inc.St. Mary’s UniversityKennesaw State University San Antonio, TXSan Antonio,...

IntelliSolve Group, Inc. St. Mary’s University Kennesaw State UniversitySan Antonio, TX San Antonio, TX Kennesaw, GAwww.intellisolve.com www.stmarytx.edu www.kennesaw.eduContents copyright 2001 IntelliSolve Group Inc.

Panel PresentationAcademic Reporting and Data Analysis

Gerry DizinnoSt. Mary’s University

Debbie HeadKennesaw State University

Susan NiesIntelliSolve Group, Inc.

Shaun WilliamsIntelliSolve Group, Inc.


Agenda

• Overview of Data Warehousing concepts and terminology

• Transitioning from Concept to Reality

• Issues now that the data warehouse is in production

• Some latest tools available and the old standards


Differences Between a Data Warehouse and Simple Extractions

•More extensive data•Controlled environment for data validation•Pre-determined timing for the extracts•Software to view/manipulate data


Current Environment Facts

• There is a wealth of information (data) in the transactional system

• Users are knowledgeable about the data• The challenge is that the users cannot

get access to the data!• The missing piece is the technology to

leverage the information and user knowledge


User Expectations

• Access and Tools to analyze and report. Easy access to both current and historical information (for comparison purposes)

• Data should be easily accessible and easily navigable through simple software tools

• Results of queries and reports should be easily understandable

• Data should be available regularly and reliably


Institution’s Expectations

• Continue to improve the institution, for example through better student retention and student success

• Respond to requests for information from inside and outside the institution


The Benefits of a Data Warehouse

• Faster response to requests for information

• Consistent result to queries• Minimal data processing skills required• Maximizes your ability to understand

your institution• Ability to Access Historical Data

regardless of time period changes


The IntelliSolve Data Warehouse Model

• Limit scope to target deliverable in 90 - 120 days– Keeps momentum and interest high– Results can be put into action quickly

• Design for long term solution– Implementation plan should include

incremental deliverables• Use off-the-shelf tools wherever practical

– Don’t reinvent the wheel


Data Base Design for a Transaction System


Data Warehouse Design

TIME_KEYSTUDENT_KEYFACULTY_KEYCOURSE_KEYFINANCIAL_AID_KEYTERM_KEYLOCATION_KEY

FTE_COUNTHEAD_COUNTETC.

TIME_KEYDAY_OF_WKWK_OF_MONTHMONT_OF_YRDAYMONTHYEARETC.

TERM_KEY TERM_IDTERM_DESCTERM_NOTEETC.

COURSE_KEYCOURSE_IDCOURSE_DESCCOURSE_GROUPETC.

STUDENT_KEYSTUDENT_IDSTUDENT_SSNSTUDENT_CASSSTUDENT_STATUSSTUDENT_ETHNICITYETC.

FACULTY_KEY FACULTY_IDFACULTY_NAMEFACULTY_HIRE_DATEFACULTY_COURSE_IDFACULTY_GROUPETC.

LOCATION_KEY LOC_BLD_IDLOC_ROOM_IDLOC_CAPACITYETC.


Warehouse KENNESAW

Where we are and how we got there…


The KSU Story In Brief

• Student System was the data jail and dictator

• Created stressful times in the functional offices

• Could not get (reliable) data during the first few terms


Short History at KSU

3-years to realize what we needed

New Relational Student SystemUnexpected Politics OR

What’s in it for ME?

New HR Relational

System

Legacy Financia

ls

Untold Number of Shadow Systems


The Politics or “What’s in for me?” syndrome

New CIO Bought into the idea in theory

De facto small Data Warehousing Team EstablishedIntelliSolve interviewed 50+

usersResistance encountered

with ownership/protectiveness

issuesExcitement generated

but now cooled


Politics or “What’s in it for me?” (continued)

Overcoming (or at least dealing) with the politics: Link the project to institutional and

administrators’ priorities Presenting something to the one VP who needs

something and can’t get it! (In process) Promising portal to VPs (CIO’s request)

Addressing some attitudes that all data are dirty so no warehouse will ever clean it up


Thinking about the DW

Benefits Consistency Standardized data

sources Reliable numbers

from numerous sources

Enable users to get their own answers

Transactional System protected from queries

Costs Length of time to see

results of any kind Intensity of

personnel to understand, clean and manage the data

Education of the users

Actual dollars in tight financial times


Where We Started or Wrong Assumptions

Assumed key administrators would be supportive of effort since it meant quicker responses

Thought the technical staff would appreciate the concept of static data

Did not want to assume responsibility for data extraction

Did not have a thorough understanding of the new relational table structure or fields


Lessons Learned?

Illustrate key questions (priority of the moment) that had to go unanswered for a time

Educate technical staff on the importance of consistent data…census date

Change our attitude (earlier) about client/server environment

Use incremental solutions - little steps at a time


St. Mary’s UniversityData Warehouse Project


What They Don't Tell You (at least very loudly) in "Data Warehouse School"

• Maintenance Needs• Consistency Needs• Training Needs


Data Warehouse Lifespan

• Portability– Source data can change or additional

sources– Target database can change– Information access tools can change

• Scalability– Start small, but plan for orders of magnitude

more data and users• Reliability

– Ensure repeatable processes


Maintenance and Consistency Needs

• Prior to each extract: accuracy checks

• After each extract: checks for consistency, calculations

• Modifications


The Cycle

1. People

2. Process

3. Technology


Retention Cube Built from a Data Warehouse

• Enables cohort analysis• Easily allows looking at the cohort

by different demographic and other characteristics


Migration and Retention:School of Humanities & Social Sciences Fall 1997 Cohort

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Semester (Beginning)

Perc

ent o

f Orig

inal

Coh

ort

Business & Administration 0 0.037433155 0.048128342 0.07486631 0.080213904 0.085561497 0.058823529

Humanities & Social Sciences 1 0.909090909 0.732620321 0.668449198 0.561497326 0.550802139 0.534759358

Science, Engineering, & Technology 0 0.010695187 0.032085561 0.021390374 0.037433155 0.032085561 0.032085561

Undeclared 0 0 0 0 0 0.005347594 0.005347594

Not enrolled 0 0.042780749 0.187165775 0.235294118 0.320855615 0.326203209 0.368983957

Fall 1997 Spring 1998 Fall 1998 Spring 1999 Fall 1999 Spring 2000 Fall 2000

N = 187


Migration and Retention: School of Science, Engineering, & Technology Fall 1997 Cohort

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Semester (Beginning)

Perc

ent o

f Org

inal

Coh

ort


Humanities & Social Sciences 0 0.044444444 0.097777778 0.093333333 0.115555556 0.12 0.124444444

Science, Engineering, & Technology 1 0.871111111 0.693333333 0.595555556 0.48 0.44 0.426666667

Undeclared 0 0 0 0 0 0 0

Not enrolled 0 0.053333333 0.168888889 0.257777778 0.324444444 0.368888889 0.373333333


N = 225


Migration and Retention: School of Business & Administration Fall 1997 Cohort

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Semester(Beginning)

Perc

ent o

f Orig

inal

Coh

ort


Humanities & Social Sciences 0 0 0 0.03 0.04 0.04 0.05

Science, Engineering, & Technology 0 0 0 0.01 0 0 0

Undeclared 0 0 0 0 0 0.01 0

Not enrolled 0 0.06 0.15 0.17 0.2 0.22 0.21


N = 100


The Solution

• OLAP Cubes• Reporting Tools• Data Mining tools


Sources of Information

• Adamson, Christopher and Venerable, Michael, Data Warehouse Design Solutions, 1998

• Brobst, Klauer, and, Poe, Building A Data Warehouse for Decision Support, 1998

• Kimball, Ralph, The Data Warehouse Toolkit, 1996

• www.IntelliSolve.com

IntelliSolve Group, Inc.St. Mary’s UniversityKennesaw State University San Antonio, TXSan Antonio,...

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