1. Examples of Analytics in Higher Education
Karl Konsdorf
Manager, Research, Analytics and Reporting
Office:
Jared

2. Agenda Analytics: A Quick Primer
Three Example of Analytics in Higher Education
Building the Analytical Organization
Karl Start

3. Water water everywhere, but not a drop to drink!

4. Data Data everywhere, but no one to help me think!

5. Data – Data - Data? We are drowning in data but starving for knowledge
~50 years to realize that Data is a valuable asset
Data volumes are growing exponentially
161 exabytes (161 billion gigabytes) of digital information were created in 2006 – 3 million times the information in all of the books ever written (IDC, 2006)
US produces ~40% of these new stored data worldwide
Why worry about data?
[slide info]
A few more examples:
Walmart stores 100 millions transactions per day.
Google has indexed more than 4 billion web pages – each containing lots of data. 5

6. Data – Data - Data? Other Data volume examples…..
U.S. Internal Revenue Service loads 1.7 Terabytes of new individual and business tax information into its system each year
Computerworld Magazine (10 September 2007)
Amount of stored data worldwide will approach 1 Zettabyte by 2010
Disk Storage Costs (per gigabyte)
$202
$28 $5 $1
Why worry about this data explosion in higher education?
Because the future technologies are data-intensive! 6

7. Information overload?
61% of managers believe that information overload is present in their workplaces
80% believe the situation will get worse
Over 50% of the managers ignore data in current decision-making processes because of information overload
84% of managers store information for the future but never use it for current analysis
60% believe that the cost of gathering information outweighs its value
The only real solution should be to replace classical data analysis and interpretation methodologies (both manual and computer-based) with a new technology applicable to large data sets.
What are the solutions managers propose in the situation of a data overload? Work harder. Yes, but how long can you keep up, because the limits are very close. Employ an assistant. Maybe, if you can afford it. Ignore the data. But then you are not competitive in the market. The only real solution should be to replace classical data analysis and interpretation methodologies (both manual and computer-based) with a new technology applicable to large data sets.
Data From: Data Mining: Concepts, Models, Methods, and Algorithms 

8. “This telephone has too many shortcomings to be seriously considered as a means of communication. The device is inherently of no value to us.”
Western Union internal memo

9. “Who wants to hear actors talk?”
H. M. Warner, Co-Founder and President
of Warner Brothers

10. “I think there is a world market for maybe five computers.”
Thomas J. Watson, President of IBM

11. “I have traveled the length and breadth of this country and talked with the best people, and I can assure you that data processing is a fad that won’t last out the year.”
Editor in charge of business books for Prentice Hall

12. “There is no reason why anyone would want a computer in the home.”
Ken Olson, President, Chairman and Founder
Digital Equipment Corporation

13. What is Business Analytics/Data Mining?
Analyzing data from different perspectives
Categorizing and summarizing the data into useful information - Identifying correlations or patterns among attributes in large data repositories
Developing knowledge by interpreting the information
Applying the knowledge to predict behaviors and future trends

14. Examples of Analytics in Higher Education
Enrollment Management
Instructional Support
Information Technology

15. Applicant Analysis – Who’s eating our lunch?
Jared Start

16. Applicant Analysis – Who’s eating our lunch?

17. Applicant Analysis – Who’s eating our lunch?

18. Applicant Analysis – Who’s eating our lunch?

19. Applicant Analysis – Who’s eating our lunch?
So what does it all mean?
Placement tests are the most influential factor
Applicants who haven’t taken a placement test represent a large opportunity for servicing the community
Financial aid is an important factor
Applicants who place into remedial math represent another at-risk segment
We are not losing applicants to the competition, but it isn’t likely to remain that way.

20. Enrollment Management Analysis
Analysis of payments
Deregistration cycle
Scored potential students for deregistration
Reduced purge activity from 11% to 6% in Fall ’08
Continued saving of approximately $75,000 per quarter

21. Analytics in Instructional Support Predicting Success in Courses with High Failure RatesFY
Success
Non-success
ACC-121
53.9%
46.1%
BIO-141
50.9%
49.1%
DEV-063
49.4%
50.6%
DEV-084
48.0%
52.0%
GEO-101
49.6%
50.4%
MAT-101
48.7%
51.3%
MAT-102
48.2%
51.8%
MAT-191
53.7%
46.3%
ALH-219
47.8%
52.2%
BIS-M75
54.0%
46.0%

22. Predicting Success in Courses with High Failure Rates
Research, Analytics, and Reporting sought to identify variables, which could predict success in courses with high non-success rates as a means of identifying students at risk for not passing these courses
Predicted
Fail
Pass
Actual
6,606 (78.84%)
1,773 (21.16%)
2,037 (27.94%)
5,254 (72.06%)

23. Analytics in Information Technology
The purpose of the exercise is to identify the relationship between the Angel Application usage and database performance to predict the performance of the angel database during high usage times thus avoiding database application failure in future enrollment terms

24. Online Enrollment Trend Report 1st day of class enrollment
CollegeWide
Mixed
Online
Total Online
Term to Term % Chg
Season to Season % Chg
% of Total Enrollment
Term
Head Count
Seat Count
03/FA
23,414
64,417
1,746
2,431
607
1,046
2,353
3,477
10.05%
5.40%
04/WI
21,885
60,490
1,796
2,509
579
1,109
2,375
3,618
0.93%
4.06%
10.85%
5.98%
04/SP
21,441
58,426
1,881
2,650
689
1,223
2,570
3,873
8.21%
7.05%
11.99%
6.63%
04/SU
9,902
20,257
791
1,110
520
822
1,311
1,932
-48.99%
-50.12%
13.24%
9.54%
04/FA
23,029
64,037
1,726
2,384
627
1,058
3,442
79.48%
78.16%
0.00%
-1.01%
10.22%
5.38%
05/WI
22,301
61,831
1,770
2,493
645
1,207
2,415
3,700
2.63%
7.50%
1.68%
2.27%
10.83%
05/SP
21,242
58,616
1,846
2,580
721
1,336
2,567
3,916
6.29%
5.84%
-0.12%
1.11%
12.08%
6.68%
05/SU
9,680
19,612
766
1,112
577
954
1,343
2,066
-47.68%
-47.24%
2.44%
6.94%
13.87%
10.53%
05/FA
22,361
62,278
1,709
2,412
679
1,227
2,388
3,639
77.81%
76.14%
1.49%
5.72%
10.68%
06/WI
21,629
59,744
1,950
2,772
763
1,451
2,713
4,223
13.61%
16.05%
12.34%
14.14%
12.54%
7.07%
06/SP
20,233
55,319
2,029
2,843
921
1,674
2,950
4,517
8.74%
6.96%
14.92%
15.35%
14.58%
8.17%
06/SU
8,876
17,635
809
1,183
857
1,357
1,666
2,540
-43.53%
-43.77%
24.05%
22.94%
18.77%
14.40%
06/FA
22,156
61,854
2,104
2,980
994
1,921
3,098
4,901
85.95%
92.95%
29.73%
34.68%
13.98%
7.92%
07/WI
21,789
59,634
2,370
3,366
1,051
2,120
3,421
5,486
10.43%
11.94%
26.10%
29.91%
15.70%
9.20%
07/SP
20,238
54,920
2,345
3,254
1,162
2,318
3,507
5,572
2.51%
1.57%
18.88%
23.36%
17.33%
10.15%
07/SU
8,955
17,814
978
1,404
1,185
1,999
2,163
3,403
-38.32%
-38.93%
29.83%
33.98%
24.15%
19.10%
07/FA
21,907
60,396
2,596
3,839
2,702
3,953
6,541
82.76%
92.21%
27.60%
33.46%
18.04%
08/WI
22,252
61,218
3,016
4,535
1,665
3,533
4,681
8,068
18.42%
23.35%
36.83%
47.07%
21.04%
13.18%
08/SP
20,073
55,761
2,891
4,318
1,650
3,398
4,541
7,716
-2.99%
-4.36%
29.48%
38.48%
22.62%
13.84%
08/SU
9,432
20,110
1,290
2,009
1,688
3,125
2,978
5,134
-34.42%
-33.46%
37.68%
50.87%
31.57%
25.53%
08/FA
23,356
65,746
3,153
4,630
1,963
4,240
5,116
8,870
71.79%
72.77%
29.42%
35.61%
21.90%
13.49%
09/WI
23,179
65,718
3,463
5,213
2,069
4,741
5,532
9,954
8.13%
12.22%
18.18%
23.38%
23.87%
15.15%
09/SP
22,323
64,119
3,567
5,501
2,087
4,720
5,654
10,221
2.21%
2.68%
24.51%
32.47%
25.33%
15.94%
09/SU
11,528
25,989
937
1,252
2,947
5,423
3,884
6,675
-31.31%
-34.69%
30.42%
30.02%
33.69%
25.68%
09/FA
25,112
74,511
3,730
5,795
2,333
5,502
6,063
11,297
56.10%
69.24%
18.51%
27.36%
24.14%
15.16%

25. Analytics in Information Technology

26. Analytics in Information Technology
Our current hardware would not support predicted online enrollment in Winter 2010.
The analytics enable the purchase and installations of new hardware for online learning in the Summer of 2009.
Fall 2009 Online Learning had very few technical issues.

27. Enabling the Analytical Organization
People
Executive Support from the President to the Registrar
Processes
Any capital decision requires supportive analytics
Technology
Mature Data Warehouse and Analytical Platform

29. Questions?
Thank You!