Data Models and Schemas Quiz

Data Models

• Logical View: How users perceive and understand data organization.
• Physical View: How data is physically stored on hardware.
• These views ensure program-data independence, meaning the user interface can change without affecting the underlying data storage.

Data Schema

• Conceptual-level schema: Organization-wide view of the entire database, mainly for management.
• External-level schema: Individual user's view of specific parts of the database (a subschema).
• Internal-level schema: Detailed view of how data is stored, including data types, attribute sizes, and specific attributes in tables.

Data Dictionary

• Contains information about the database structure.
• Each data element has a corresponding record describing it.
• Access is typically restricted to database administrators.
• Key controls such as primary and foreign keys are added and managed here.
• Validation rules, data format restrictions, and security permissions are also defined in the data dictionary.

Structured Query Language (SQL)

• A programming language used to create, manipulate, and extract information from databases.
• SQL code can be used for structural changes (creating tables), maintenance operations, and querying data.
• Application programs like SQL server and Microsoft Access provide user-friendly interfaces for working with SQL.

Semantic Modelling

• Uses models that inherently have meaning.
• Facilitates efficient database design for transaction processing.
• Creates graphical models that represent business processes and policies, improving communication with users.

Data Modelling

• The process of defining a database to accurately represent all aspects of an organization, including external interactions.
• Uses include:
  • Designing databases.
  • Graphically documenting data.
  • Understanding existing data within an organization.
  • Showing what information should be stored and the relationships between entities.

Entity Relationship Diagrams (ERDs)

• A well-known data model used in AIS design.
• Represents entities and the relationships between them.
• Helps visualize how data is structured and connected.

REA Model

• An extension of ERD with three types of entities:
  • Resources: Assets with economic value to the organization.
  • Events: Business activities for which management wants to collect information.
  • Agents: People or organizations participating in events.

Rules for Connecting Entities

• Each event affects at least one resource.

Automation Impact on Accounting

• Efficiency and Accuracy:

  • Automates repetitive tasks like data entry and reporting, reducing errors and improving accuracy.
  • Allows accountants to focus on value-added activities like analysis and decision-making.

• Streamlined Processes and Cost Reduction:

  • Automates the entire financial reporting cycle.
  • Reduces manual intervention and accelerates processing.

• Evolving Accountant Roles:

  • Requires accountants to develop new skills like data analytics and business intelligence.
  • Accountants become strategic advisors, providing insights and interpreting complex data.

Challenges and Opportunities

• Job Displacement Concerns: Automation might replace some accounting tasks, causing concerns about job security.
• Upskilling and Professional Growth: Opportunities to acquire new skills and enhance value proposition through technology proficiency.

Key Terms

• Redundancy: The same data being stored in multiple places without adding value or new information.
• Relational Database Management System (RDBMS): A DBMS that uses a relational data model, storing data in connected tables.
• Entity/Relation: Any object you want to store attributes about, such as a supplier or customer.
• Transactional Information: Data contained within a single business process, supporting daily operational tasks.