Data models can facilitate interaction among the designer, the applications programmer, and the end user. A well-developed data model can even foster improved understanding of the organization for which the database design is developed. In short, data models are a communication tool. Data Model gives us an idea that how the final system will look like after its complete implementation. It defines the data elements and the relationships between the data elements. Data Models are used to show how data is stored, connected, accessed and updated in the database management system. Here, we use a set of symbols and text to represent the information so that members of the organisation can communicate and understand it.
Data constitute the most basic information units employed by a system. Applications are created to manage data and to help transform data into information.
But data are viewed in different ways by different people. So that there is a huge importance of data modeling in DBMS.
The goal of the data model is to make sure that the all data objects required by the database are completely and accurately represented. Because the data model uses easily understood notations and natural language , it can be reviewed and verified as correct by the end-users. The data model is also detailed enough to be used by the database developers to use as a "blueprint" for building the physical database. The information contained in the data model will be used to define the relational tables, primary and foreign keys, stored procedures, and triggers. A poorly designed database will require more time in the long term. Without careful planning you may create a database that omits data required to create critical reports, produces results that are incorrect or inconsistent, and is unable to accommodate changes in the user's requirements.
1.) Higher quality: Just as architects consider blueprints before constructing a building, you should consider data before building an app. On average, about 70 percent of software development efforts fail, and a major source of failure is premature coding. A data model helps define the problem, enabling you to consider different approaches and choose the best one.
2.) Reduced cost: You can build applications at lower cost via data models. Data modeling typically consumes less than 10 percent of a project budget, and can reduce the 70 percent of budget that is typically devoted to programming. Data modeling catches errors and oversights early, when they are easy to fix. This is better than fixing errors once the software has been written or – worse yet – is in customer hands.
3.) Quicker time to market: You can also build software faster by catching errors early. In addition, a data model can automate some tasks – design tools can take a model as an input and generate the initial database structure, as well as some data access code.
4.) Clearer scope: A data model provides a focus for determining scope. It provides something tangible to help business sponsors and developers agree over precisely what is included with the software and what is omitted.
Business staff can see what the developers are building and compare it with their understanding. Models promote consensus among developers, customers and other stakeholders.
A data model also promotes agreement on vocabulary and jargon. The model highlights the chosen terms so that they can be driven forward into software artifacts. The resulting software becomes easier to maintain and extend.
5.) Faster performance: A sound model simplifies database tuning. A well-constructed database typically runs fast, often quicker than expected. To achieve optimal performance, the concepts in a data model must be crisp and coherent (see first bullet). Then the proper rules must be used for translating the model into a database design.
6.) Better documentation: Models document important concepts and jargon, proving a basis for long-term maintenance. The documentation will serve you well through staff turnover.
7.) Fewer application errors: A data model causes participants to crisply define concepts and resolve confusion. As a result, application development starts with a clear vision. Developers can still make detailed errors as they write application code, but they are less likely to make deep errors that are difficult to resolve.
8.) Fewer data errors: Data errors are worse than application errors. It is one thing to have an application crash, necessitating a restart. It is another thing to corrupt data in a large database. A data model not only improves the conceptual quality of an application, it also lets you leverage database features that improve data quality. Developers can weave constraints into the fabric of a model and the resulting database.
For example, every table should normally have a primary key. The database can enforce other unique combinations of fields. Referential integrity can ensure that foreign keys are bona fide and not dangling.
9.) Managed risk: You can use a data model to estimate the complexity of software, and gain insight into the level of development effort and project risk. You should consider the size of a model, as well as the intensity of inter-table connections.
10.) A good start for data mining: The documentation inherent in a model serves as a starting point for analytical data mining. You can take day-to-day business data and load it into a dedicated database, known as a “data warehouse.” Data warehouses are constructed specifically for the purpose of data analysis, leveraging that data from routine operations.
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