- 1.
Identify data captured by a TPS at your university. Estimate how much data are generated in a year.
A TPS at a university may be the payroll system for the faculty and staff or the student grade system. For each system you would need to determine the number of entries per year, and the size of the fields contained in each entry. For instance, consider a simple payroll system where the data generated each pay period are just the social security number and the amount paid per employee. The social security number is a 9 digit field and the amount paid might be an 8 digit field. Given these dimensions and an employee count of 2,000, the amount of data generate each year would be :
- (9+8) * 2000 * 26 = 884,000
This is a very abbreviated look at a system, other fields would be included in most systems, and it is worth revisiting the data model on page 173 to get some ideas of these other fields.
- 3.
If your university were to implement a data warehouse, what examples of dirty data might you expect to find?
Dirty data that may be found in a university's database system include:
- Duplicate student records (one in the billing department, one in the grading system, and perhaps one in payroll);
- Duplicate faculty records (one in the payroll and one in the grading system);
- Multiple entries for the same student (for instance, if they changed their name or they took some time off and re-enrolled in the university);
- Misuse of data fields (a database may have been designed prior to the widespread use of e-mail and used a non-populated field such as fax number for the e-mail addresses).
- 5.
Write five data verification questions for a university data warehouse.
Examples of verification questions:
- What is the average GPA for MIS students?
- What is the average age of a university student?
- How many students have a 4.0 GPA?
- How many students have a home address from the same state in which the university resides?
- What is the average number of class hours a student registers for in a term?
- 7.
Imagine you work as an analyst for a major global auto manufacturer. What techniques would you use for the following questions?
- 7a.
How do sports car buyers differ from other customers?
A discovery question using data mining.
- 7c.
Where does our major competitor have its dealers? d. How much money is a dealer likely to make from servicing a customer who buys a luxury car?
Visualization. An SQL query will give a list of the locations, but a map (visualization) is likely to give you a better feel for the competitor's impact.
- 7e.
What do people who buy midsize sedans have in common?
Find classification characteristics of people who buy midsize sedans.
- 7g.
Who are the most likely prospects to buy a luxury car?
Classify customers into luxury and non-luxury car buyers, and then examine those attributes that discriminate between the two groups.
- 7i.
We know a great deal about the sort of car a customer will buy based on demographic data (e.g., age, number of children, and type of job). What is a simple visual aid we can provide to sales personnel to help them show customers the car they are most likely to buy?
Decision tree. By asking a series of questions based on the decision tree, a salesperson could identify the car a person is most likely to buy.
- 9.
A telephone company needs your advice on the data it should include in its multidimensional database. It has an extensive relational database that captures details (e.g., calling and called phone numbers, time of day, cost, length of call) of every call. As well, it has access to extensive demographic data so it can allocate customers to one of 50 lifestyle categories. What data would you load into the multidimensional database? What aggregations would you use? It might help to identify initially the identifier and variable dimensions (use Table 15-6 on page 450).
| Identifiers |
When |
Time of day, day of week, type of day (e.g., holiday) |
|
Where |
Location of calling and called phone |
|
What |
Regular call, emergency, toll-free |
|
How |
Cell or land line |
|
Who |
Residential, business, family, lifestyle category |
| Variables |
Outcomes |
Length
Cost
Revenue
|
- 11.
Download the file exped.xls from the book's Web site and open it with MS Excel. This file is a sample of 1,000 sales transactions for The Expeditioner. For each sale, there is a row recording when it was sold, where it was sold, what was sold, how it was sold, the quantity sold, and the sales revenue. Use the PivotTable Wizard (Data>PivotTable Report) to produce the following report:
| Sum of REVENUE |
HOW
|
|
|
|
| WHERE |
Catalog |
Store |
Web |
Grand Total |
London |
50,310 |
151,015 |
13,009 |
214,334 |
| New York |
8,712 |
28,060 |
2,351 |
39,123 |
| Paris |
32,166 |
104,083 |
7,054 |
143,303 |
| Sydney |
5,471 |
21,769 |
2,749 |
29,989 |
| Tokyo |
12,103 |
42,610 |
2,003 |
56,716 |
| Grand Total |
108,762 |
347,537 |
27,166 |
483,465 |
Continue to use the PivotTable Wizard to answer the following questions:
- 11a.
What was the value of catalog sales for London in the first quarter?
7,810 (assuming first quarter is Jan.- March)
- 11c.
What percent of Sydney's annual sales were catalog sales?
- 11e.
What was the value of Camel saddle sales for Paris in 2002 by quarter?
| Qtr1 |
13,156 |
| Qtr2 |
14,950 |
| Qtr3 |
24,518 |
| Qtr4 |
8,372 |
This page is part of the promotional and support material for Data Management (open edition) by Richard T. Watson
For questions and comments please contact the author |
Date revised:
17-Oct-2022
