It is paramount to choose the right user requirement approach that might be suitable to organization
environment. Recognizing the strengths and weaknesses of each approach could assist the process of selecting
suitable user requirement analysis that might contribute to successful data warehouse project. There are few
advantages in applying data driven approach. First and foremost it could simplify ETL design [13], [14], [18]. Since
the requirements selection are based on data available in data source, it is easy to construct data warehouse schema
[8] and at the same time ensure data availability. Data source schema is normally stable and does not change
repeatedly. This will establish high stability in multidimensional schema [18] compare with requirements based on
end user needs. However there are few weaknesses of data driven approach. This approach is high risk of wasting
resources to handle unneeded information structure [4],[8]. With abundant of information from data source, it is
almost impossible to extract relevant requirements [10].This will diminish end user motivation to participate in
design process especially when they need to work with large data model[4]. In addition, it is possible that the result
of multidimensional schema did not fit user requirements [18] due to lack of end user involvement
It is paramount to choose the right user requirement approach that might be suitable to organization
environment. Recognizing the strengths and weaknesses of each approach could assist the process of selecting
suitable user requirement analysis that might contribute to successful data warehouse project. There are few
advantages in applying data driven approach. First and foremost it could simplify ETL design [13], [14], [18]. Since
the requirements selection are based on data available in data source, it is easy to construct data warehouse schema
[8] and at the same time ensure data availability. Data source schema is normally stable and does not change
repeatedly. This will establish high stability in multidimensional schema [18] compare with requirements based on
end user needs. However there are few weaknesses of data driven approach. This approach is high risk of wasting
resources to handle unneeded information structure [4],[8]. With abundant of information from data source, it is
almost impossible to extract relevant requirements [10].This will diminish end user motivation to participate in
design process especially when they need to work with large data model[4]. In addition, it is possible that the result
of multidimensional schema did not fit user requirements [18] due to lack of end user involvement
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