OLAP Cubes
Today we learned about using OLAP cubes in SPSS. OLAP is an acronym for Online Analytical Processing. It is a computer based technique for analyzing business related data. An OLAP cube is a specially designed database that is optimized for reporting.
Features of OLAP Cubes
While most databases designed for online transaction processing such as those used in claims processing are designed for efficiency in data storage, OLAP cubes are designed for efficiency in data retrieval. This means that the data is stored in such a way as to make it easy and efficient for reporting. Regular relational databases treat all data into the database similarly but OLAP cubes categorize the data into ‘dimensions’ and ‘measures’. Measures represent items that are counted, summarized or aggregated, such as cost or period of time. Dimensions are variables by which measures are summarized, such as Gender, Educational Background, Nationality etc. This organization of data greatly facilitates the ability to formulate data requests based on real-life situations. In addition, many of queries that could be posed to the data are pre-aggregated in the database such that the answers have already been pre-calculated and can be reported without delay. OLAP cubes can have many more dimensions than 3, but the term continues to apply.The size of an OLAP cube depends on the number of measures and dimensions it contains. It may have no relationship to the size of the initial data set. Therefore, a claims data set having millions of members can be consolidated into a relatively small OLAP cube that can return data almost instantaneously. Thus analytical products based on OLAP data sources are fast and user friendly.. A unique feature that is part of the OLAP database structure is the ability of OLAP cube to "drill down" into the data. When designing an OLAP database, dimensions are structured into "hierarchies". For example, service dates can be arranged in a hierarchy of days, months, quarters, and years. Similarly, diagnoses can be arranged by major and minor categories, drilling down to the individual diagnosis code. The OLAP cube is familiar the hierarchy and hence if the analyst issues the command to "drill down", the cube knows the next level of data to be presented. With the above mentioned and a few more powerful features, thus OLAP cube is a great tool for reporting of any type of data.
Mobile phone service subscribers’ details
We opened the file Cell_inter.sav in SPSS which contained data like demographics and mobile phone service subscription details of many people. We generated an OLAP cube taking Level of education, Name of current service provider, Mode of payment, Gender of respondent and Games as ‘Dimensions’ while Age of respondent, Usage period in months, monthly expenditure on phone, fixed component of bill, voice calls bill, sms bill and other charges were taken as ‘Measures’. The following OLAP cube was generated.
OLAP Cubes
Level of education: Total
Name of current service provider: Total
Mode of payment: Total
Gender of respondent: Total
Games: Total
Sum
|
N
|
Mean
|
Std. Deviation
|
% of Total Sum
|
% of Total N
| |
Age in years of respondent
|
3487
|
206
|
16.93
|
1.288
|
100.0%
|
100.0%
|
Usage period In Months
|
2569
|
206
|
12.47
|
9.084
|
100.0%
|
100.0%
|
Monthly expenditure on phone
|
72633.00
|
206
|
352.5874
|
184.64170
|
100.0%
|
100.0%
|
Fixed component of bill
|
9914.00
|
206
|
48.1262
|
19.59825
|
100.0%
|
100.0%
|
Voice calls bill
|
9985.00
|
206
|
48.4709
|
28.83031
|
100.0%
|
100.0%
|
SMS bill
|
5519.00
|
206
|
26.7913
|
17.64308
|
100.0%
|
100.0%
|
Other charges
|
1147.00
|
206
|
5.5680
|
11.18940
|
100.0%
|
100.0%
|
Filtering can be done on the various dimensions in order to further analyse the data. For example, the following OLAP cube shows only the details of subscribers of Hutch who use Games.
OLAP Cubes
Level of education: Total
Name of current service provider: Hutch
Mode of payment: Total
Gender of respondent: Total
Games: Yes
Sum
|
N
|
Mean
|
Std. Deviation
|
% of Total Sum
|
% of Total N
| |
Age in years of respondent
|
1178
|
69
|
17.07
|
.975
|
33.8%
|
33.5%
|
Usage period In Months
|
890
|
69
|
12.90
|
9.955
|
34.6%
|
33.5%
|
Monthly expenditure on phone
|
23820.00
|
69
|
345.2174
|
122.62219
|
32.8%
|
33.5%
|
Fixed component of bill
|
3228.00
|
69
|
46.7826
|
19.26966
|
32.6%
|
33.5%
|
Voice calls bill
|
3395.00
|
69
|
49.2029
|
24.01934
|
34.0%
|
33.5%
|
SMS bill
|
1736.00
|
69
|
25.1594
|
16.89381
|
31.5%
|
33.5%
|
Other charges
|
251.00
|
69
|
3.6377
|
8.71437
|
21.9%
|
33.5%
|
This shows that a significant part of Hutch subscribers use Games and thus we can recommend them to concentrate more on providing even better games in order to generate more revenues.
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