System Design and Modelling

System Design

Introduction

• System Design is the specification or construction of a technical, computer-based solution for the business requirements identified in the system analysis phase.
• During design, system analysts convert the description of the recommended alternative solution into logical and then physical system specifications. S/he must design all aspects of the system from input and output screens to reports, databases, and computer processes.
• Databases are designed with the help of data modeling tools (for example, E-R diagrams) and computer processes are designed with the help of Structured English notation, Decision Trees, and Decision Tables.
• The final output of the design phase is the system specifications in a form ready to be turned over to programmers and other system builders for construction (coding).

Logical Design

Definition

• • Logical Design is the phase of the system development life cycle in which the system analyst and the user develop a concrete understanding of the operation of the system.

Features of Logical Design

• • This phase comes after the system analysis phase.
  • Logical design is not tied to any specific hardware and software platform.
  • Logical design concentrates on the processing of business aspects of the system.

Physical Design

Introduction of Physical Design

• • In physical design, logical design is converted to physical or technical specifications.
  • During physical design, the analyst’s team makes decisions regarding the programming language, database management system, hardware platform, operating system, and networking environment to be used. At this stage, any new hardware or software can also be purchased.

System Modeling

• System modeling is a vital practice in designing, analyzing, and improving complex systems.
• System modeling is the process of creating abstract models of a system, capturing its structure, behavior, and interactions with its environment.
• These models help in understanding, analyzing, designing, and verifying complex systems across various domains such as software engineering, systems engineering, and business processes.
• By providing clear and detailed representations of systems, it enhances understanding, communication, and decision-making among stakeholders.
• Utilizing various modeling techniques and tools, system modeling helps ensure that systems are well-structured, efficient, and aligned with business goals.

Types of System Modeling

• There are the following types of System Modelling –
  • Process Modeling
  • Data Modeling

(A) Process Modeling

• • Process modeling is a vital technique for understanding, analyzing, and improving business processes.
  • Process modeling is a method used to visually represent the flow of activities, tasks, and processes within an organization or system.
  • By using process modeling, organizations can enhance communication, identify inefficiencies, and streamline their operations.
  • By using process modeling, stakeholders can gain insights into how processes function, identify bottlenecks, inefficiencies, and areas for improvement.
  • Examples of Process Modelling are –
    • Flowcharts: Basic diagrams that show the steps in a process using symbols like arrows, rectangles, and diamonds.
    • Data Flow Diagrams (DFDs): It Illustrates the flow of data between processes, data stores, and external entities.
    • Business Process Model and Notation (BPMN): A standardized method for creating detailed and complex process diagrams that can be understood by all stakeholders.
    • Unified Modeling Language (UML): Used in software engineering to represent the design and structure of systems.
    • Gantt Charts: Visual representations of project schedules showing the start and end dates of tasks.

  • Benefits of Process Modeling

    • Visual models make it easier to understand how processes work and interact.
    • It provides a common language for stakeholders, facilitating better communication and collaboration.
    • It helps in identifying bottlenecks, redundancies, and inefficiencies in processes.
    • It aids in designing more efficient and effective processes i.e. process improvement.
    • It serves as formal documentation of processes, useful for training and compliance purposes.

  • Tools for Process Modeling

    • Microsoft Visio: A versatile diagramming tool for creating flowcharts, DFDs, and other process models.
    • Lucidchart: An online diagramming application that supports a variety of process modeling techniques.
    • Bizagi: A BPMN tool for business process modeling and automation.
    • ARIS: A comprehensive tool for enterprise process modeling and analysis.
    • Draw.io: A free online tool for creating diagrams and flowcharts.

(B) Data Modeling

• • Data modeling is a critical step in the design and implementation of databases. 
  • Data modeling is the process of creating a visual representation of a complex system’s data structure, often referred to as a data model.
  • Data modeling defines how data is connected, stored, and accessed, providing a blueprint for constructing databases that meet business requirements and ensure data consistency and integrity.
  • Data modeling includes the concept of entities, attributes, relationships, primary keys, foreign keys, etc.
  • Data modeling ensures that the data architecture aligns with business requirements and facilitates efficient data management and usage.
  • Data modeling helps in achieving data integrity, consistency, and accessibility across the organization by providing a clear framework for data structure and relationships.
  • Examples of Data Modeling are – E-R Diagrams.

  • Types of Data Models

    1. Conceptual Data Model

       • Purpose: High-level representation of organizational data.
       • Focus: This model is Business-oriented and focuses on entities and relationships without worrying about technical details.
       • Audience: It includes business stakeholders and data architects.
       • Tools: Entity-Relationship Diagrams (ERDs).

    2. Logical Data Model

       • Purpose: It is the detailed version of the conceptual model, specifying attributes, primary keys, and relationships.
       • Focus: More detail-oriented and defines how data elements are structured.
       • Audience: It includes data architects and database designers.
       • Tools: ERDs, data dictionaries.

    3. Physical Data Model

       • Purpose: Implementation-specific model detailing that defines how data will be stored in the database.
       • Focus: Database performance, indexing, and storage details.
       • Audience: Database administrators and developers.
       • Tools: Database schema diagrams, SQL scripts.

  • Benefits of Data Modeling

    • Data modeling provides a common framework for discussing and understanding data requirements among stakeholders that finally enhances communication between the system or bodies.
    • Data modeling ensures consistent data definitions and structures across the organization.
    • Data modeling helps in designing databases that are well-structured and optimized for performance.
    • Data modeling facilitates data integrity and reduces redundancy i.e. it improves data quality.
    • Data modeling serves as comprehensive documentation of the data architecture.

    Tools for Data Modeling

    • ERwin Data Modeler: A popular tool for creating conceptual, logical, and physical data models.
    • IBM InfoSphere Data Architect: It helps design and manage data models.
    • Microsoft Visio: It is useful for drawing ER diagrams and other data models.
    • Oracle SQL Developer Data Modeler: This tool supports logical and physical data modeling.
    • Lucidchart: An online tool for creating ER diagrams and other data models.

Process Specification Tools

For example – Decision Trees, Decision Tables, Data Dictionary etc