MIS_Week_3_2 - Tagged

Page 1: Introduction

• Course Title: Management Information Systems

• Week: 3 - 2

• Date: February 5, 2025

• Instructor: Yuyang Ye

• Contact: yuyang.ye@rutgers.edu

Page 2: What is a Database?

• Definition: A database is a collection of data in a structured format.

• Examples: Personal experiences where data is organized, such as spreadsheets or contact lists.

• Features:

  • Modern databases are stored electronically on computers.

  • Standardizes storage of similar data.

  • Can contain trillions of bytes of data.

  • Supports thousands of simultaneous users.

Page 3: Introduction to Data Management

• Definition: Data Management involves organizing and maintaining data within and across database systems.

• Operational Efficiencies:

  • Adding new information.

  • Updating existing data.

  • Erasing outdated or unnecessary data.

• Storage:

  • Data is typically stored in relational database tables for ease of access and management.

  • Well-organized databases ensure smooth interactions and data integrity across systems.

Page 4: Database Management Systems (DBMS)

• Definition: DBMS is software that manages data in a database.

• Types of Databases:

  • Relational Databases

  • NoSQL Databases

  • Cloud Databases

• Functions: Ensures data security, consistency, and availability.

• Complexity: Managing large databases with many users adds complexity to DBMS.

Page 5: Key Issues in Data Management

• Considerations:

  • Number of tables in a database.

  • Types of data each table should store.

  • How tables are linked (relationships).

• Example: Designing a library database (tables for books, members, loans).

• System-level Questions:

  • How many systems an organization needs.

  • Functions and information each system contains.

  • How systems interact.

Page 6: General Data Management Issues

• Increasing Data Volume: Organizations are accumulating massive amounts of data.

  • Historical data retention and slow data deletion rates contribute to increased volume.

• Accelerating Data Growth: New technologies make data collection easier and faster, leading to accelerated accumulation.

Page 7: Considerations for Data Collection

• Data Gathering: Organizations need to decide which types of data to collect (e.g., clickstream data from websites).

• Data Value: Just because data accumulates quickly, does not mean it is valuable.

  • Organizations may collect data without a clear purpose first, then seek its value.

• Traditional Approach: Data was traditionally gathered in response to specific operational questions or problems.

Page 8: Data Fusion, Accuracy, Security, and Quality

• Data Fusion: The act of combining data from multiple sources, which can enhance operations (e.g., sharing inventory data).

• Data Quality Issues: Organizations often face data accuracy problems due to redundancy, poor system coordination, and insufficient error checks.

• Data Security: The protection of data against unauthorized deletion, corruption, or misuse. Threats can be intentional (hacking) or unintentional.

Page 9: Transaction Processing (TPS)

• Definition: Involves tracking day-to-day events, such as orders and accounting entries.

• Characteristics:

  • Involves ongoing updates to data tables (adding/modifying/deleting rows).

  • Example actions include adding new customer orders and modifying customer information.

Page 10: Analytical Processing

• Definition: Combines data from many or all rows to derive insights via complex queries and summarization.

  • Examples: Total sales computation, summarizing costs by category.

• Types of Processing:

  • Classic MIS: Basic grouping and summarization.

  • Advanced: Data mining for pattern discovery using statistical techniques.

Page 11: Decision Support

• Definition: Utilizes data for complex decision formulation (e.g., logistics planning for shipments).

• Limitations:

  • Simple systems like Access can manage basic decisions but cannot perform data mining independently without additional software integration.

Page 12: Challenges with Simultaneous Processing

• Issues:

  • High transaction volumes may hinder analytical processing and vice versa.

  • Analytical processes may cause data inconsistencies if data changes during calculation.

• Solution: Create a data warehouse for analytical processing without disrupting transaction processes.

Page 13: Single Table Database

• Example: Consider a simple loan database with one table—LOAN, containing multiple attributes.

• Issues: High potential for data redundancy, update anomalies, and lack of normalization.

Page 14-21: Data Redundancy and Anomalies

• Data Redundancy:

  • Problem: Repetitive customer information leads to increased risk of inconsistencies.

• Update Anomalies:

  • Problem: Different addresses may exist for the same customer across records.

• Insertion Anomalies:

  • Problem: Difficulties in adding new data without existing examples (e.g., a customer without a loan).

• Deletion Anomalies:

  • Problem: Losing important customer information after deleting their last loan record.

Page 22: Summary of Single Table Issues

• Single-table databases result in:

  • Data redundancy

  • Update anomalies

  • Insertion anomalies

  • Deletion anomalies

• Solution: Normalize with multiple related tables to overcome these issues.

Page 23-24: Repeating Groups

• Definition: Allows multiple fields for repeated data to avoid anomalies.

  • Drawbacks: Fixed maximum number of repeats can lead to excessive storage and may not fit varying data needs.

Page 25: Solution with Multiple Tables

• Multiple Tables Approach:

  • Customer Table: Stores customer information.

  • Loan Table: Stores loan information.

  • Using foreign keys to link tables effectively eliminates redundancy and anomalies, ensuring single storage of customer information.

Page 26: ER Modeling

• Definition: Entity-Relationship (ER) modeling visualizes relationships between entities in a database.

  • Example: Customers and loans in a one-to-many relationship.

Page 27-28: ER Diagram Attributes

• Customer Entity Attributes:

  • Examples: CustomerID, FirstName, LastName, Address, etc.

• Loan Entity Attributes:

  • Examples: LoanID, Date, Amount, Rate, etc.

• Relationship representation: Each Loan references a Customer via CustomerID (foreign key).

Page 29: Example Loan Entity

• Detailed attributes of loan records displayed, demonstrating the relationship with the customer.

Page 30: Database Design Overview

• Definition: Specification of database objects such as tables, columns, data types, and indexes.

• Design Process: Involves conceptual, logical, and physical design phases for large databases.

Page 31: Conceptual Design Components

• Components:

  • Entities: Represented as rectangles with rounded corners (e.g., Person, place, activity).

  • Relationships: Lines that define connections between entities.

  • Attributes: Descriptive properties of entities.

  • ER Diagrams: Visual representation of entities, relationships, and attributes supplemented by textual descriptions.

Page 32: Example ER Diagrams

• Examples of ER diagrams presented to illustrate relationships and entities in database design.