Systems analysis data modeling

Introduction

A data model represents the information requirements for an organization. A data model acts as an essential communication tool for discussion with system users apart from serving as a blueprint for the database system (Blaha, 2007). It thus acts as a bridge between the real work information and the database storing the data content. Creation of blueprints takes place at various levels of detail, that is, the high-level requirements, basic architectural layout, and the detailed plumbing design. Data modeling in systems analysis differs depending on the type of business, as businesses processes are different, and the identification of the system occurs at the modeling stage. When developing a suitable data model, it is vital to liaise with stakeholders so as to understand the requirements. The function of data modeling in system analysis is to help in comprehending the system requirements. It makes the life of end users and developers easier.

Origins of data modeling

During the early days of data processing system development focused on the automation of the tedious manual business processes. The key river was the elimination of the slow manual process.  In the process of system development, the primary objective was the production of a set of programs that helped to automate a business process (Kabeli & Peretz, 2003). The business environment has been changing in complexity due to the development of technology, thus making the old ways of performing system analysis hard to meet current and future needs.  Nowadays, the process of system analysis comprises of two main tasks that are data modeling and functional modeling.  Traditional development methodologies emphasize on functional modeling using data flow diagrams while object-oriented methodologies put emphasis on data modeling using class diagrams.

The role of system analysis data modeling

The goal of data modeling is to ensure that complete and accurate representation of all data objects required by the database (Valacich et al., 2007). Data modeling for system analysis uses makes use of easily understood natural language and notations, thus, it is easy for the end-users to review and verify them. Data modeling is also indispensable to database developers that use it as a blueprint for building the physical database. The developers utilize the information of the data model to define the relational tables, stored procedures, triggers, primary keys, and secondary keys. A poorly-designed database will consume more time in the long run. If the developers do not carefully analyze the system, they may create data models that leave out data needed to create critical reports (Valacich et al., 2007).  It may also produce results that are inconsistent or incorrect, and the one that cannot accommodate changes in the users’ requirements. If the data model is wrong, the application will do what the users want.

What makes system analysis data modeling important?

The main characteristics that make system analysis data modeling vital are communication and precision (Blaha, 2007). Data model is a representation of the organizational information requirements and so it ought to truly reflect data requirements of a business.  The data model must clearly indicate every aspect of the data of a company’s operations. That is because data models serve as communication tools with users of domain experts. That is why the analysis of the system must focus on those two aspects.  One of the major requirements in system analysis is quality. The value of data modeling is to establish the single version of truth for the enterprise or to have a single view of a business. For companies to maintain a competitive edge in the marketplace, they ought to make decisions based on accurate data of the enterprise. Systems analysis data modeling helps the decision makers to make better evaluations and enhancements of efficiency or their organizations or companies.  One of the approaches to improving the quality of data in systems is to model the data that improves the procedures for decision-making.

Data modeling at different information levels

External data model

The external data model or very high-level data model is a display of the database system as viewed by the different user groups. An external view (or model) looks at the world from a specific perspective and for a specific purpose. The external models are the appropriate places to hold data needs for a specific business context and the rules that apply to it.  Data models of most systems nowadays take a view of the world from the perspective of that system, and so external models (Ponniah, 2007).

Conceptual data model (high-level data model)

Conceptual models are useful in clarifying the vital information, how concepts have a definition, and how the concepts relate to one another (Valacich et al., 2007).  High-level data models help in collecting the business requirements as well as clarifying understanding of the basic concepts. The purpose of the conceptual model is for communicating with the user community.  It is also a diagram that identifies the business concepts and the relationship between those concepts for the purpose of gaining, reflecting and document understanding of an enterprise’s business. The model is essential and has wide usage in the early stage of requirements analysis for systems. Conceptual data model construction is a common practice in the early stage of system development in industries.  It takes the form of an entity-relationship diagram, and some scholars consider it as a picture on the puzzle box offering the vision of the result of the information puzzle (Valacich et al., 2007).

Logical data model

It entails more detailed levels of a diagram showing data layout, size and type of data, and the relationships between various objects (Hoberman et al., 2009). The logical level data model shows more details including business rules and business logic.  The mandate of creating a logical data model lies on an architect of a data modeler, but a business person is in the requirement to ensure proper application and definition of business rules.

Physical level data model

Physical level data models for system analysis indicate technical details for implementing data structures or databases (Blaha, 2007). A physical model represents a physical store of data. Physical models must support the conceptual model, and there are several physical models for a conceptual model.

Conclusion

System analysis data modeling take place at the organization level, and it acts as a vital communication tool useful to the developers and the end-users.  It helps the users and the developers to understand better the system because data models represent the visual image of the system (Ponniah, 2007). The analysis process of a system entails data modeling and functional modeling as the main activities. The traditional data modeling methodologies emphasize on functional modeling of systems using data flow diagrams while new ones, which are objects oriented emphasize on data modeling using class diagrams. There are data models at different levels of information systems including conceptual, logical, external and physical models. The type of model depends on the purpose of that model and the resources available.

References

Hay, David C.  Saddle River: Prentice Hall, 2002.

Ponniah, Paulraj: “Data Modeling Fundamentals”, Hoboken, New Jersey: A John Wiley &Sons, INC., 2007 USA.

Steve Hoberman, Donna Burbank, and Chris Bradley. 2009. “Data Modeling for the Business: A Handbook for Aligning the Business with it Using High-Level Data Models.” Technics Publications, LLC,, USA.

Valacich, A. Hoffer, F. & George, S. (2007). Analyzing System Data Requirements: Saddle River: Prentice Hall, Pp 295 - 296.

Sherry Roberts is the author of this paper. A senior editor at MeldaResearch.Com in custom essay paper writing if you need a similar paper you can place your order from custom research paper services.