1. Division of Institutional Research,
Planning, Effectiveness, and Analytics
Student Enrollment Monitoring
Process Automation
Dhanashree Arole
Lead SAS Developer
SAS Global Forum April 2015
1

2. Abstract
At Broward College, the Division of Institutional Research,
Planning, Effectiveness, and Analytics had a manual process for
tracking and reporting daily to Senior Management the student
enrollment at all college campuses. It included numerous views of the
data related to multiple sessions within the term, methods of
instruction, and campus centers. The data is monitored on a daily
basis for each active term from the start of registration through the end
of the term. The overall process consists of a nightly scheduled ETL
job that mines source data to create permanent datasets. Various SAS
models were developed in Enterprise Guide to pull and organize the
data so that it can be accumulated and mined for generating multiple
reports in Microsoft Excel. The reports compare quantitative data for
the current term with the corresponding data for the prior year’s term to
derive meaningful insights. Traditionally, both SAS Enterprise Guide
and Microsoft Excel were used to achieve this goal and the reports
were generated manually by copying and pasting the data from SAS
Enterprise Guide into Microsoft Excel worksheets. To eliminate the
daily manual effort as well as possible human error, it was proposed to
create permanent datasets representing each of the Excel worksheets
and create automated SAS Data Integration Studio jobs to generate
both these new datasets as well as the PDF reports on a daily basis.
The new scheduled process has resulted in over 90%
savings of the time required to create these reports and more
importantly this innovation minimizes the probability of human errors
generated due to manual copy-paste of data. It is a more streamlined
automated process for creating and storing data and creating reports
for Senior Management.
Student Enrollment Monitoring
Process Automation 2
Division
of
IRPEA

3. Executive Summary
• Traditional Student Enrollment Monitoring
Process Background
• Challenges Involved in the Traditional Process
• Details about the New Automated Solution
• Overall Savings and Long Term Wins
Student Enrollment Monitoring
Process Automation 3
Division
of
IRPEA

4. BACKGROUND
• Two separate SAS Enterprise Guide projects
were run manually by an Analyst each weekday to
produce the raw data
• Each project required about two to three minutes
to produce the data
• The bulk of the effort was in the manual copying
and pasting of cells from SAS Enterprise Guide
into the appropriate Excel worksheet
• Five PDF reports were manually generated on a
daily basis
Student Enrollment Monitoring
Process Automation 4
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of
IRPEA

5. CHALLENGES
• Minimize the risk of human error from the copy-
paste process of over hundred cells from five
temporary output data tables
• Time taken to finish daily operational task of
validation and creating reports is between 45 to
75 minutes depending on the experience of the
Report Analyst
• The process must be completed by 9 AM daily for
Senior Management to review, including holidays
and other college closures
Student Enrollment Monitoring
Process Automation 5
Division
of
IRPEA

6. Automated Solution
• Created multiple SAS Data
Integration Studio Jobs and
scheduled a process flow:
– Established storage of the raw data
in permanent SAS tables
– Modified existing model to calculate
totals and organize the data in a
streamlined manner and address
scenario of unavailable data due to
term date
– Programmatically created PDF
Reports
– Scheduled the Data Integration
Studio jobs to mine the data and
create PDF reports for Senior
Management
Student Enrollment Monitoring
Process Automation 6
Division
of
IRPEA

7. Task Time Taken
By
Traditional
Process
(minutes)
Time Taken
By
Automated
Process
(minutes)
Percent
Savings in
Time
(%)
Raw Data
Mining
2 to 3 2 0 to 33
Campus 9 to 15 1 88 to 93
Campus
Center
9 to 15 0.5 94 to 96
Campus
Sessions
9 to 15 0.5 94 to 96
FTIC 9 to 15 0.5 94 to 96
Instruction
Method
9 to 15 0.5 94 to 96
Total 45 to 75 5 88 to 93
Daily Time Savings Summary
Division
of
IRPEA
7
Student Enrollment Monitoring
Process Automation

8. Long Term Wins
• Minimization of human error due to copy paste of
data
• Reduction in time required to create the report
and minimization of cross-training staff
• Elimination of the one-time preparation work
required to start monitoring
Student Enrollment Monitoring
Process Automation 8
Division
of
IRPEA