Post by account_disabled on Feb 20, 2024 4:30:34 GMT -5
There are various tips on data modeling techniques that can help in developing a useful and accurate data model for operational and business intelligence applications. Overcoming the obstacles that appear in the process is the way to fully enjoy all the advantages that data modeling brings to the organization. Data model: for today and tomorrow A data model is a communication tool that allows confirming knowledge about areas of interest, while ensuring that people visualize reality as a global whole. It is about connecting through a single vision or, at least, in a similar way, from an understanding of the differences that may exist. The use given to a data model can vary depending on the objective to be achieved. So: It can describe a new panorama based on the available information: to support risk mitigation and improve the capacity for innovation and adaptation to a future scenario. It can describe a scenario that exists in reality based on data: improving the quality of knowledge transfer and contributing to a better understanding of the business. There are countless questions that arise during the data modeling process. Asking and getting answers is the core dynamic of data modeling, which starts from the data requirements documentation phase.
Overcoming challenges on the path to building the data model The biggest challenge is correctly capturing the requirements on the data model . Often, when the project starts, there are only vague, barely outlined requirements (if they exist at all). This is a serious setback, since the data model must represent these requirements completely and accurately, so its prior existence is absolutely necessary. Therefore, it is a very difficult task (although quite common) to have to go from ambiguity or vagueness to precision. A large number of questions need to be asked, the answers to which must be documented in the model. This process involves a USA Student Phone Number List significant consumption of time, although even more significant is the urgency of talent, because it is clear that knowledge of what questions to ask is essential and, unfortunately, projects often lack the deadlines and talent necessary to correctly ask and answer these questions. Data modeling is the process of learning about the business, it is a long and far from easy journey, which looks better when it is taken with the help of experts. Those who know how to effectively design a data model know that, for example, to achieve the desired precision when there is conflict over definitions and specifications, different techniques can be used, such as: A/That the developer of the data model is in charge of preparing the definitions related to it, despite being aware that they will be incorrect definitions; and then transmit them to business users whose sole purpose will be to correct them. A mission with a high probability of success, since it is easier for a business user to tweak and adapt an existing definition than to create one from scratch.
Another tactic that ensures good results on the path to the data model is to ask the development team for the definition of the terms before proceeding to their appointment. In order to name a concept you have to know what it is, what it is about and what it consists of. So, by making the effort to define it first, you simplify the task of naming it later and ensure consistency. But requirements and specifications are not the only complications that can arise in the data model creation process. One of the most notable is deciding which of the various shapes available is the most appropriate for the final model. It is necessary to find the data model that best works for the purpose for which it was designed. Steve Hoberman , in one of his books, explains that there are three keys to overcoming this challenge: The person in charge of designing the data model has to be flexible regarding the visual format of the model. Efforts should be made to avoid standard modeling using traditional data modeling nomenclature. Typically, it is important to first assess the appropriateness of introducing traditional data modeling nomenclature. Sometimes it will be the case that business users already know it, but on other occasions it will be better to avoid this notation, using instead a nomenclature that they understand, something more accessible that may consist of the use of spreadsheets, but which can also be based on the introduction of representative images of the concepts, if it is considered most appropriate. Despite all the challenges, taking the time to create a data model is a business experience that comes with many benefits, the main one of which is turning the data into an understandable reality, from which people can learn. From there, the advantages are many, such as: Savings in operations. Creation of higher quality systems.