MISMod03

Module Overview for Data and Business Intelligence

Introduction

• Reference: Bidgoli, MIS, 11th Edition (© 2024 Cengage)

Module Objectives

• By the end of this module, you should be able to:

  • 3.1 Define a database and a DBMS.

  • 3.2 Explain logical database design and the relational database model.

  • 3.3 Define the five components of a database management system.

  • 3.4 Summarize three recent trends in database design and use.

  • 3.5 Analyze the four major components and functions of a data warehouse and their use for business.

  • 3.6 Describe the functions of a data mart.

  • 3.7 Compare and contrast data lakes with data warehouses.

  • 3.8 Describe the role of business analytics in the decision-making process.

  • 3.9 Examine the advantages and challenges of big data and predictive analytics for a business.

  • 3.10 Explain database marketing and its business applications.

  • 3.11 Explain key features of Tableau and Power BI as two popular business intelligence and visualization platforms.

Databases

• Definition: Collection of related data stored centrally or across multiple locations.

• Data Hierarchy:

  • Structure and organization of data including fields, records, and files.

• DBMS: Software for creating, storing, maintaining, and accessing database files.

Types of Data in a Database

• Internal Data:

  • Stored within the organization’s databases for functional information systems.

  • Examples: Transactions, sales, personnel records.

• External Data:

  • Stored in data warehouses.

  • Examples: Competitors, customers, tax records.

Methods for Accessing Files

1. Sequential Access File Structure:

   • Organized and processed numerically or sequentially based on a primary key.

   • Suitable for backup and archived files, typically on magnetic tape.

2. Random Access File Structure:

   • Records accessed in any order regardless of physical location.

   • Fast processing, typically stored on magnetic disks.

3. Indexed Sequential Access Method (ISAM):

   • Records accessed either sequentially or randomly, based on holdings.

   • Uses an index structure comprising indexed value and pointer to disk location.

Logical Database Design

• Viewed in two ways:

  • Physical View: How data is stored and retrieved (e.g., hard disks).

  • Logical View: Appearance and organization of data viewed by users.

• Data Model: Determines data creation, representation, organization, and maintenance.

  • Includes:

    • Data structure (organization)

    • Operations (methods, calculations)

    • Integrity rules (boundaries)

Models of Database Design

1. Hierarchical Model:

   • Relationships between records organized as branches.

2. Network Model:

   • Similar to the hierarchical model but allows multiple parent and child records.

3. Relational Model:

   • Utilizes a two-dimensional table of rows (records) and columns (fields).

   • Data Dictionary: Stores definitions of fields, types, defaults, and validation rules.

   • Keys:

     • Primary Key: Uniquely identifies records.

     • Foreign Key: Links child table to parent table.

   • Normalization: Eliminates redundant data through defined normal forms (1NF to 5NF).

Components of DBMS

1. DBMS Software Components:

   • Database Engine: Storage and retrieval of data.

   • Data Definition: Creates and manages data dictionaries.

   • Data Manipulation: Adds, deletes, modifies, and retrieves records.

   • Application Generation: Designs application interface elements.

   • Data Administration: Security, backup, recovery, and CRUD permissions.

2. Database Administrators (DBAs):

   • Responsible for design, management, security, recovery, and performance.

Data-Driven Web Sites

• Interface to databases for:

  • Data retrieval

  • User data entry

• Benefits include improved access and reduced maintenance overhead.

Distributed Databases

• DDBMS:

  • Stores data across multiple servers.

  • Advantages include better reflective design, reduced response time, and fault tolerance.

• Setup Approaches:

  • Fragmentation: Tables divided by location.

  • Replication: Sites store data copies.

  • Allocation: Combination of fragmentation and replication.

Object-Oriented Databases

• Objects contain data and related methods.

• Advantages:

  • Supports complex data management and multimedia.

Data Warehouses

• Definition: Collection of data from various sources for decision-making support.

• Characteristics:

  • Subject-oriented, integrated, time-variant, and focused on analytical purpose.

• ETL Process:

  • Extraction, Transformation, and Loading of data.

Outputs from Data Warehouses

• Generates reports via OLTP and OLAP for decision-making.

• Data mining for pattern discovery and text mining for analyzing textual information.

• Benefits include fast query responses and the ability to analyze historical data.

Data Marts

• Definition: Smaller, focused versions of data warehouses.

• Advantages: Faster access and improved response time.

• Disadvantages: Limited scope and consolidation difficulties.

Data Lakes

• Stores both structured and unstructured data for analytics and machine learning.

Business Analytics

• Utilizes data and statistical methods to provide actionable insights.

• Methods include:

  • Descriptive: Reviews past data.

  • Predictive: Prepares for future events.

  • Prescriptive: Suggests likely outcomes for decisions.

The Big Data Era

• Definition: Characterized by high volume, variety, velocity, veracity, and value (5 Vs).

• Privacy risks include discrimination and loss of anonymity.

Database Marketing

• Leverages an organization’s customer database for proactive marketing initiatives.

• Successful tasks include Customer Lifetime Value (CLTV) and RFM analysis.

Summary of Learning Objectives

• Reaffirmation of objectives covered in the module.