Data analysis involves classifying, structuring, and defining data in a model that can faithfully represent all the real world occurrences of interest to the business and the relationships between them.
Compare this with activity analysis, in which the type process is used to represent all possible occurrences (or executions) of the same activity.
The business model that is developed during analysis integrates three equally important aspects of business requirements:
This aspect serves to confirm, and if necessary modify, the analysis model. It also provides a detailed basis for system design.
Data modeling is performed early in analysis, to set, or clarify the development scope, and then revisited in more depth until definitions are agreed on for all the data that support the business requirements.
When analysis is complete, the resulting data elements of the model depict in detail what information is used in the business.
The modeling techniques in this chapter are associated with top-down data modeling. Some of these techniques can also be used in reverse engineering to help plan system re-engineering, interfacing between new and current systems, and evaluating and implementing packaged applications.
The reverse engineering use of data modeling techniques is described in the chapter Analyzing Current Systems.
Data and activities can be analyzed together.
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