A data model gives the description of how data would be used to meet end-user requirements. The modeling process allows the creator to understand information requirements. Models will normally vary between different businesses because of the peculiarities that each business has in different sectors. This paper will discuss the major milestones in data modeling, including data and system requirements.
Any proper data modeling activity will begin with the proper requirement gathering. Communicating with the stakeholders on the needs of the data model required is vital. The chief function of a data model is to ensure that it captures the information needs put forward. As such, consultations with stakeholders on requirement needs are key in providing the right background to the data model. The result is that both developer and end-users enjoy the user experience on the model.
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Figure 1 : Summary of Data Modeling Process (Mallikaarachchi, 2010).
The above model shows the graphical presentation of the data modeling process. Customer needs and output requirements inform the technology base that developers will use. Requirements analysis, functional analysis and system synthesis correlate so that the system can produce required information needs. Functional analysis allows the developer to determine system needs, including the decomposition of the system needs, allocating performance specifications and refine interfaces (Sturm & Shehory, 2002). The synthesis allows system specifications to be turned to physical performances of the developed interfaces. Preferences are also set here. Once the system is completed, a system analysis can be conducted to determine the effectiveness of the system in reaching information needs set out by the end-users. Configuration, data management, risk management and overall effectiveness are some factors considered at this point (Jensen, Kligys, Pedersen, & Timko, 2004).
The levels of data modeling are used as well in conducting the entire exercise. The following pictorial describes the levels of data modeling:
Figure 2 : Levels of data models (Mallikaarachchi, 2010).
Either three of the major data models (conceptual, physical or logical design) can be used to achieve the objectives of the data modeling process. The functional team and technical team must come together to achieve these objectives. The functional team will consist of the analysts in the business arena as well as end-users who will use the end-product. The technical team on the other hand, consists of programmers and developers. A section of data modelers design the data model that meets the functional team’s expectations and provides the necessary system specifications for the technical team (Ballard, Herreman, Schau, & Bell, 1998).
References
Ballard, C., Herreman, D., Schau, D., & Bell, R. (1998). Data Modeling Techniques for Data Warehousing. International Technical Support Organization , 8 -158.
Jensen, C. S., Kligys, A., Pedersen, T. B., & Timko, I. (2004). Multidimensional data modeling for location-based services. , . The VLDB Journal—The International Journal on Very Large Data Bases, 13(1) , 1-21.
Mallikaarachchi, V. (2010). Data Modeling for System Analysis . Retrieved from University of Missouri - St. Louis: http://www.umsl.edu/~sauterv/analysis/Fall2010Papers/varuni/ .
Sturm, A., & Shehory, O. (2002). Towards industrially applicable modeling technique for agent-based systems. Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1 (pp. 39-40). Washington D.C.: ACM.
