International, National, and Industry-Specific Regulations: Study Questions | Saylor Academy

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Modern information systems can raise various legal and ethical issues in addition to those associated with intellectual property. After reading the selection, what are some of the most significant ethical challenges that professionals using and developing BI systems should consider? How do these ethical issues translate into specific laws and regulations? Is the legal structure keeping up with the development of new technology?

Study Questions

What does the term information systems ethics mean?
What is a code of ethics? What is one advantage and one disadvantage of a code of ethics?
What does the term intellectual property mean? Give an example.
What protections are provided by a copyright? How do you obtain one?
What is fair use?
What protections are provided by a patent? How do you obtain one?
What does a trademark protect? How do you obtain one?
What does the term personally identifiable information mean?
What protections are provided by HIPAA, COPPA, and FERPA?
How would you explain the concept of NORA?
What is GDPR and what was the motivation behind this regulation?

COURSE INTRODUCTION

Course Syllabus

Unit 1: Business Intelligence and Its Role in Organizations

1.1: Fundamentals of Business Intelligence (BI)

BI in Modern Organizations

Evolution of BI Strategies

Data Sources, Analytics, and Decision Support Systems

1.2: Practical Applications of BI

Supporting Decision-Making with BI

Successful BI Implementations

BI and Competitive Advantage

1.3: Fundamentals of Data Management

BI Systems Architecture

Data Warehousing and Modeling

Managing Diverse Data Sources

1.4: BI Concepts in Action

Popular BI Tools and Technologies

Key Performance Indicators (KPIs)

Unit 1 Assessment

Unit 1 Assessment

Unit 2: Sources of Data in BI Systems

2.1: Selecting Data to Support Business Decision-Making

Types of Data Sources

Classifying Data Sources by Structure

2.2: Evaluating Data Quality and Relevance

Quality, Accuracy, Completeness, and Timeliness

Evaluating the Relevance of Data

Implications of Poor Data Quality

2.3: Effective Data Integration Strategies and Technologies

Batch Processing, Real-Time Integration, and Data Virtualization

Data Integration Technologies and Tools

Trade-Offs of Integration Approaches

2.4: Big Data Models and NoSQL Sources

Role of Big Data Sources in BI Systems

NoSQL Databases

Using Technology to Enhance BI Capabilities

Unit 2 Assessment

Unit 2 Assessment

Unit 3: Data Management and Data Warehousing

3.1: Data Management Principles

Foundations of Data Management

Data Quality and Accuracy

Data Governance Frameworks and Best Practices

3.2: Data Warehousing Concepts

Data Warehouses as Centralized Repositories for Data

Architecture and Components of Data Warehouses

Designing and Implementing Data Warehouses

3.3: Data Modeling Techniques

Data Modeling Techniques

Dimensional Modeling

3.4: Integrating Data Management and Data Warehousing

Integrating Data Management and Data Warehousing

Selecting and Preparing Data

Unit 3 Assessment

Unit 3 Assessment

Unit 4: Data Analysis and Interpretation

4.1: Data Analysis Techniques

Data Analysis Techniques

Statistical Algorithms and Exploratory Data Analysis

Data Analysis Tools

4.2: Interpreting Data Analyses

Interpreting Analytical Results

Context and Domain Knowledge

4.3: The Data Mining Process

Data Mining Techniques

Knowledge Discovery

Text Mining and the Complications of Language

Exploring Text Data

Unit 4 Assessment

Unit 4 Assessment

Unit 5: Data Visualization and Reporting

5.1: Data Visualization Techniques

Communicating Analytical Results

Visualization Tools and Techniques

Creating Useful Dashboards and Reports

Principles of Effective Data Visualization

5.2 Creating Common Data Visualizations

Introduction to Tableau

Charts, Heatmaps, and Treemaps

Waterfall and Bubble Charts