Role of Data Modeling within Enterprise Management
When it comes to the development, maintaining, augmentation, and integration of enterprise systems, data modeling is key. Over 90% of enterprise systems’ functionality is based on the creation, manipulation, and querying of data. When managing a major enterprise project, it is thus necessary to depend on data modeling in the successful execution of projects.
Data modeling can be used to deliver dependable, cost effective systems. Project managers are responsible for numerous tasks, including planning, estimating, evaluating risks, managing resources, monitoring, managing deliveries, and more. Almost every one of those activities depends on the data model’s evolution.
In recent years, data modeling has been the subject of fierce criticism. It is said that data modeling has been poor to adapt to changes forced by the rapid changes of globalization and decentralized structures. A lot of this criticism is rooted in negative experiences with data modeling. But data modeling should not be eliminated altogether. Rather, the goal should be to use it for its advantages, and get it right.
But for those not keyed in to the pros and cons, let’s start by looking at some of the arguments against data modeling. Then we’ll use these negatives to show why data modeling can, in fact, be an effective enterprising strategy.
First, it is argued that data modeling slows down the development of systems in an era when speed is the key in technological development. It is also claimed that in the process of developing software, enterprise data models add unnecessary complexity. Prefabricated data models are said to be of no good use, and are thus not good investments. Data modeling is said to not be able to keep up with the changes in the information processing systems belonging to financial institutions. It is also claimed that data modeling cannot provide banks with information systems that are adaptable to the necessary speed of innovation.
Above all, data modeling is seen as a brake, rather than an accelerator. In the realm of systems development, change is a constant. So, the reasoning goes, by using data modeling, you are promoting the idea that structure shouldn’t change.
Yet despite the rampant criticism that enterprise data modeling often becomes the target of, the fact remains that some basic requirements are needed when developing software. No financial institution can survive without using integrated systems. They manage complexities and handle interdependencies. Integrating new and old systems is, in fact, the norm in the realm of systems development. The usage of consistent terms in an enterprise’s single systems will result in the consistent processing of data over the course of several different systems.
The fact is, vast majority of the problems identified above can be repaired by a quality data modeling system. By using these arguments against, we will demonstrate how data modeling can actually be used for the benefit of an enterprise.
In regards to the argument that data modeling is unable to keep up with rapid change, the obvious answer is that rapidly changing variants should not be processed in a data model. It is the core, steadfast aspects of a business that are the subject of data modeling – not short-term organizational schemes. External partners or bank account info, for example, can be readily modeled in data model schemes, as they are not prone to rapid changes. These areas should remain the focus of data modeling. This is the objective of reference models.
In fact, when used correctly, data modeling does work as an accelerator, rather than a brake. It should work as a service function for various projects. By visiting the company’s data model group, you can learn about other efforts occurring within the enterprise, about other entities, and how similar problems were solved by others using a similar reference model. If your data administration department only does reviews, then yes, it will slow down the progress of your projects. Instead, the data administration department should aid in the development of projects and thus help them pick up speed.
What is needed in beneficial data modeling is just the right amount of detail. A top level data model is a viable framework, although it is not essential to work from the top downwards. Never assume that it is necessary to expand your data model to the fourth level of detail. This kind of detailing should never be a part of the enterprise data model. Rather it should be kept as part of the individual project data model.
The question that critics of data modeling fail to answer is what should be done as an alternative to data modeling. We don’t want to go back to the old days of data processing, do we? In fact, it will take up even more time and resources to revert back to these stone-age techniques. With a little time and smart planning, data modeling can be an effective tool in an enterprise.
