Organising A Data Model Development Effort
Introduction
Experts have established that it is very important to come up with a data model development effort for a variety of reasons. One of the main reasons of developing a data model development effort is because of its ability to fast track an organizations acquisition of a common view of one of its most vital resources, that is, information. In this text, I will explain how I would go about organizing a data model development effort.
Organizing a data model development effort
As stated in the introductory part, it is prudent for an organization or enterprise for that matter to come up with a data model development effort for purposes of acquiring a common view of information, which is considered one of the most important resources a business has. The acquisition of this vital asset has to be informed in one way or the other by the enterprise conceptual data model development.
This goes a long way in the identification of data entities that are essential and how they relate to business objects (Strathman 2008). Here, a framework is formulated for purposes of coming up with as well as developing application data models, systems of information etc.
When it comes to organizing a data model development effort with regard to enterprise conceptual data model development, sponsorship becomes a vital requirement. This is primarily because the enterprise conceptual data model will be anchored on visibility that is enterprise based. The sponsor in this case depends on the composition of the policy and decision making group of function overseeing it.
For example, an organization involved with data governance that has a high level of decision makers involved in the whole oversight would end up having or gathering support of all the departments and business units in the organization.
Without the involvement of top decision makers, and hence sponsorship from the executive, it may be difficult to attain involvement of the various businesses units. Hay (2006) notes that prior to commencing the organization of a data model development effort, some vital questions should be asked.
As I seek to organize a data model development effort, the first thing I would look into include whether the various conceptual data modeling standards have been met. If these standards have not been addressed, first identify them before proceeding on anything else. Some of the standards to be looked into include but are not limited to naming, notation, more metadata etc.
Secondly, I would identify the level of support I am to seek from the organization or enterprise. With regard to the various business units, I would first identify such things as the number of interviews I will be taking when it comes to the users in the various business units. I would also seek to review the information I will be seeking fro the users and whether there is need to seek more information. On this point, I would also consider whether the various users need to take part in model reviews. Strathman (2008) notes that the importance of user participation when it comes to model reviews cannot be underestimated.
Third, I would seek to update myself on the enterprise conceptual data model usage level. This may be enhanced by the identification of cases or situations of a given level of usage. Hay (2006) notes that while it is very possible to come up with applications as well as databases while virtually avoiding the utilization of a data model, it may end up behind counterproductive in the long run.
The above scenario can be equated to coming up with an elaborate building without any blueprint. However, many organizations have gone ahead to develop databases without logical data modeling. Many experts attribute this to resource constraints as well as human resource constraints. The development of a conceptual data model is important if firms are to discover various vital relationships including but not limited to order and client which may in turn be utilized in quite a number of applications.
Lastly, I would do a cost benefit analysis of sorts to ascertain the benefits of the data model development effort. According to Allen et al. (2005), the real benefit accrues upon the organization rather than on the model. Hence I would review the benefits in terms of what will accrue organization wide rather than on the model.
Conclusion
It is important to note that while coming up with a data model development effort, one should consider the benefits that are likely to accrue in the light of the prevailing conditions. This together with the commitment required from the various organizational units goes a long way towards enhancing the organization of the data model development effort.
References
Allen, S. & Terry, E. (2005). Beginning relational data modeling. Apress
Hay, D.C. (2006). Data model patterns: a metadata map. Morgan Kaufmann
Strathman, J.G. (2008). Leveraging ITS data for transit market research: a practitioner’s guidebook. Transportation Research Board
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