Counts for 20% of course grade
Written assignment to be completed individually.
Typed, double-spaced, 12 pt font, max 10pages
Please type your name in the header. Papers lacking a name will receive 0 marks.
ANY plagiarism will result in a mark =0
Papers that rely on extensive quotations or are predominantly cited from other sources will
receive an « average» mark
Due -12/09/10 (hard copy in class; also uploaded to iLearn by 6 pm 12/09/10).
Late papers will lose 10% per day (any portion of 24 hrs).
As we have seen, Business Intelligence is composed of a wide variety of techniques to
analyze data and present information to decision makers. BI approaches can be grouped
into broad classes of techniques:
1. Standard statistical methods for quantitative data (forecasting, predictive models,
decision trees, neural nets etc),
2. Semantic analysis methods (text mining, LSI, LSA) of textual data, and
3. Geographic Information Systems for spatial data.
Question: Compare and contrast these three broad classes of techniques as applied to
business intelligence by examining the basic assumptions of the techniques (what they are,
what they do) and how they are used. One good approach would be to-research and then
describe/ analyze three cases studies (total) which use one or more of each of the three
approaches (you can use a case study which combines two techniques). These may be case
studies from any industry – I would recommend you pick an area that you are interested in!
In your description/ analysis you should consider the following:
identify the problem the study was addressing
describe the data that was analyzed, quality, governance
describe the analytic techniques and methods of visualization that were used
describe how the application contributed (or not) to BI,KMSand GDSS
comment on the effectiveness of the implementation approach
determine whether the BI analysis/presentation was effective and if so, how (what
were the metrics by which you can claim that it worked/ failed)
what you learned from analyzing these casesw
Use these discussions to show the strengths and weaknesses of each of the three analysis
techniques above. It would be reasonable to discuss how integrating them will lead to better
information (in other words, how do the three approaches complement each other.)
This is to be your own work – be sure to use quotes if you are taking material verbatim from
sources and to properly cite sources. There is a 11 zero tolerance” plagiarism policy.
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