System Analysis Data Modeling has a significant role in the successful development of information systems. If the data modeling and the system analysis is effectively carried out, it provides a complete frame work on the business requirements of the end user application. In this research paper, the focus and emphasis is upon understanding the system analysis data modeling, importance of data modeling, how it can be resourceful and the process that has to be adapted for effective data modeling procedures are discussed and has been concluded that, if right kind of business requirements are gathered as inputs, and effectively collated, then the design and specification development can turnout effective with the implementation of physical data modeling.
Systems Analysis and Data modeling has a predominant role in the success of organizational information systems. Unless there is robust data modeling that has been designed for the system, the outcome from such information systems may not be giving right kind of inputs to the organization. Hence it is very essential that there is significant focus on the systems analysis for the data modeling that is required to provide the expected outcome. (A & Gondane Parikshit S., 2010)
Data model is pedagogy of how the data should be used in the system to meet the end user requirements or the client specifications. When there is an effective data modeling design that has been developed, it helps in understanding the requirements effectively. The data modeling for organizations keeps varying as per the requirements and the process functions of the organization and it is essential for the organization to focus upon having the requirements initially by analyzing and gathering data about the requirements. (Hay, 1997)
System Analysis is also an integral part of such development as it is imperative to understand the need of systems to implement the data modeling that is intended to be implemented in an organization. Unless the system applications in the organization support the requisite data modeling, the entire process might be in vain. Hence it is very essential for the organization to focus on building quality data modeling systems that could be resourceful to the organization in succeeding with the business attributes. (Wang, Henry B. Kon, & Stuart E. Madnick , 1993)
In this study, the focus is upon understanding the importance of data modeling and the process that has to be adapted to have successful system analysis and the data modeling which could support the organization in developing the requisite systems.
Data Modeling has significant role in the organizations systems development. For an effective system that caters to the expected outcome, it is very essential to understand the requirements properly and then design the systems with the requisite models, structures and the systems that could quickly provide the response to the queries and also effective data management. (Hay, 1997)
Unless there is an effective data modeling, the design and the development of the system may not be suiting to the functional requirements of the end application and the entire process may go in vain for the organization. In the other dimension, where the data flow process is well defined, and the need for integration and relativity of the data is ascertained for a system, it helps the team in identifying right kind of systems that are to be used, and also the process of designing and developing right kind of systems which could help the end user access a more suitable system. (Navathe, 1992)
The other important factor is when a detailed system analysis data modeling is carried out, involving the key stakeholders of the system, then there are potential chances for having right kind of inputs that could help choosing appropriate systems that could support the requirement. For instance, in the case of an organization, where there are multiple inputs that might reach the system, and also multiple queries that might hit the server, there is need for robust system that could with stand such load, and thus the implementation team could work on the effective systems development that could be right fit for the organizational requirements. (Navathe, 1992)
The process of data modeling starts with a requirement gathering process, and unless there is adequate interaction between the stakeholders of the project, the requirement collection may not be appropriate or substantial. It is the process of exploring the data oriented structures which can be used for various purposes, and critically one of the key functions of the data modeling are to evaluate and understand the information requirements of the organization. (Hay, 1997)
In precise it can be stated that when the team develops a process, there is need to understand, the key inputs to the system, the key output from the system, the placement of information and the accessibility of information to different users of the system. Developing such a relation and process flow between various sections of the data is the purpose of data modeling. (Wang, Henry B. Kon, & Stuart E. Madnick , 1993)
Users have to provide a well-defined objectives and the analysis have to develop framework accordingly, and when such data modeling is developed it makes the job of the developers and the end users much easier. Also when a defined and structured approach is adapted by the analysts for system analysis data modeling, it helps in developing an effective systems framework for the development of system. (A & Gondane Parikshit S., 2010)
Data modeling systems has significant role in the development of the systems and by having good data modeling systems implemented for analysis, the business requirements of the organization is effectively planned and detailed. (Hay, 1997)
When the data modeling systems are to be implemented by the analysts, it is essential to focus upon choosing right kind of data modeling systems. Basically the data models are classified in to four types as Conceptual data models, Enterprise Data models, Logical Data Modeling and Physical Data Modeling. Enterprise data model focus on unique requirements of a business function and is similar of the conceptual data modeling, whereas the Logical and Physical data modeling supports in developing an effective data modeling system. (A & Gondane Parikshit S., 2010)
Data models are used in both the functional assessments and also in technical assessments. In the case of functional teams, it consist both business analysts and end users and in the technical teams, it comprises the developers and programmers. In the case of systems design and data modeling, the involvement of the key stakeholders like the end users, business analysts, system architects and the developers, to ensure that everyone is aligned to one framework and for working towards developing quality systems. (Navathe, 1992)
Based on the requirements of the organization, and the functional process activities, and the business requirements inputs, the kind of data modeling system that could be adapted by the organization shall be chosen by the analysts. (Hay, 1997)
Managing large volumes of data which could be either structure or unstructured is the key function of the information systems of an organization. The effective usage of system analysis data modeling supports in building robust system that could handle the structured data storage and data management in the form or relational databases. Though in the early stages of SDLCs, the focus was more upon the conceptual model, in the micro outlook, the physical data model has significant importance in the system analysis and data modeling and if effectively managed could be very resourceful to the organizations in building robust information systems for their business process. (Hay, 1997)
A, H. B., & Gondane Parikshit S. (2010). System Analysis and Design Flexibility in the Approach Based on the Product Definition. International Journal of Computer Applications, 42-47.
Hay, D. C. (1997). The Zachman Framework: An Introduction. THE DATA ADMINISTRATION NEWSLETTER – TDAN.com.
Navathe, S. B. (1992). Evolution Of Data Modeling for Databases. Communications of ACM, 112-123.
Wang, R. Y., Henry B. Kon, & Stuart E. Madnick . (1993). Data Quality Requirements Analysis and Modeling. Ninth International Conference of Data Engineering. Vienna.