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 (LSI, l.SA) of textual data and
3. Geographic Information Systems for spatial data.
Question: Compare and contrast these three approaches 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, at a minimum:
• Identify the problem the study was addressing.
• Describe the data that was analyzed.
• Describe the analytic technique and method of visualization that was used.
• Describe how the application contributed (or not) to BI, KMS and GDSS.
• Comment on the effectiveness of the approach.
• Determine whether the BI approach was effective and if so, how (what were the metrics by which you can claim that it worked/ failed).
Use these discussions to show the strengths weaknesses of each of the three analysis techniques above. It would be reasonable to show 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.
Typed, double-spaced, 12 pt font, max 10 pages