Business Intelligence and Data Analysis in the Age of AI
1st Edition
Published on 13. May 2025
340 pages
978-1-5015-2297-0 (ISBN)
Description
Unlock data-driven decision-making with Business Intelligence and Data Analysis in the Age of AI. This guide combines traditional BI with AI's transformative power to help professionals and newcomers excel in the data era. Whether you're a seasoned professional or new to BI, this book provides actionable strategies to navigate the complexities of modern data analysis. Embark on this illuminating journey to master the tools, strategies, and ethical considerations that define modern business intelligence and AI.
FEATURES
* BI Fundamentals: Master analytics processes and tools
* Ethical and Regulatory Challenges: Navigate governance, security, privacy, and ethical frameworks
* BI Tools: Learn the power of tools like R, SQL, Python, and data manipulation techniques
* Visualize and Predict: Learn data visualization and predictive analytics to forecast trends and drive innovation
* Embrace the Future: Discover how AI transforms BI, unlocking new opportunities and navigating emerging risks.
FEATURES
* BI Fundamentals: Master analytics processes and tools
* Ethical and Regulatory Challenges: Navigate governance, security, privacy, and ethical frameworks
* BI Tools: Learn the power of tools like R, SQL, Python, and data manipulation techniques
* Visualize and Predict: Learn data visualization and predictive analytics to forecast trends and drive innovation
* Embrace the Future: Discover how AI transforms BI, unlocking new opportunities and navigating emerging risks.
More details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
US School Grade: College Graduate Student
File size
2,22 MB
ISBN-13
978-1-5015-2297-0 (9781501522970)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
03/2025
1st Edition
De Gruyter
€44.95
Article not available at the moment
Person
Arshad Khan is a distinguished expert in analytics, training, and education, with a career spanning decade of impactful contributions to leading organizations. He has consulted for renowned companies such as Accenture, PwC, Deloitte, Chevron, Bose Corporation, Chrysler, Genentech, PepsiCo, and Hitachi-America, among many others. An accomplished author, Mr. Khan has written 20 books, including two focused on artificial intelligence and data-related topics.
Content
- Half Title
- Title
- Copyright
- Contents
- Preface
- Chapter 1: Introduction to Business Intelligence
- Overview
- Definition
- Evolution
- Concept
- Importance
- Application
- Usage
- Decision-Making
- Usage Categorization
- User Diversity
- Technology
- Techniques
- Tool Categories
- Core Components
- Data Sources
- Data Integration
- Data Warehousing
- Extract, Transform, Load (ETL)
- Data Modeling
- Reporting and Querying
- Data Analysis
- Data Visualization
- Performance Management
- Metadata Management
- Security and Access Control
- Types
- Operational BI
- Differences Between Enterprise BI and Operational BI
- Benefits
- Drive Business Value
- Support Decision-Making Processes
- Facilitate Data Analysis
- Beneficiaries
- Leading BI Vendors
- Self-Service BI
- Enterprise BI
- Cloud-based BI
- Open-Source BI
- Big Data BI
- Mobile BI
- Predictive Analytics
- Collaborative BI
- Embedded BI
- Real-Time BI
- Future of Business Intelligence
- Augmented Analytics
- Natural Language Processing
- Explainable AI
- Data Democratization
- Advanced Data Visualization
- Edge Analytics
- Embedded BI
- Continuous Intelligence
- Blockchain Integration
- Cloud-Based BI
- Hybrid and Multicloud Deployments
- Ethical and Responsible AI
- Predictive and Prescriptive Analytics
- Data Governance and Privacy
- Collaborative BI
- Self-Service BI
- Challenges and Pitfalls
- Challenges
- Common BI Pitfalls and Mistakes
- Lack of Clear Objectives
- Poor Data Quality
- Neglecting Data Governance
- Ignoring User Adoption
- Overemphasizing Technology
- Complexity Overload
- Inadequate Planning
- Ignoring Change Management
- Failure to Iterate and Improve
- Relying Solely on Historical Data
- Siloed BI Initiatives
- Chapter 2: Analytics Process and Tools
- Overview
- Analysis
- Analytics
- Analytics versus Analysis
- Application
- Unlocking Insights with Analytics
- Unraveling Insights
- Advanced Business Analytics
- Unlocking the Power of Analytics in Decision-Making
- Web Analytics
- Unveiling Online Insights
- Impact
- Web Data: Sources and Significance
- Data Analysis Process
- Identify What the Business Needs
- Collect the Data
- Review and Clean the Data
- Model the Data
- Analyze the Data
- Interpret the Results
- Predict and Optimize
- Communicate
- Features and Tools
- Analysis Functions
- Legacy Tool
- Tools for Analytics
- Challenges
- Unstructured Data
- Unlocking Insights from Unstructured Data
- Multifaceted Nature of Challenges
- Chapter 3: Understanding Data
- Types of Data Sources
- Impact of Big Data on Data Analytics
- Structured Data
- Unstructured Data
- Data Storage
- Scalable Storage Solutions
- Data Storage Volume
- Data Storage Levels
- Current Detailed Data
- Lightly Summarized Data
- Highly Summarized Data
- Aggregated Data
- Effect of Granularity
- Data Archiving
- Database Design Process
- Data Challenges in Analytics
- Understanding Data Sources
- Source Data Problems
- Data Selection
- Data Import
- Data Loading
- Data Volume
- Detailed and External Data
- Historical Data
- Data Import Cost
- Data Conversion
- Complexity and Significance
- Techniques
- Data Migration
- Overview of Data Migration
- Data Migration Drivers
- Data Migration Process
- Data Migration Complexity
- Data Volume to Be Migrated
- Data Migration Tool Selection
- Migration Effort Estimation
- Data Migration Problems
- Ensuring Data Quality
- Data Migration versus Data Integration
- Chapter 4: Big Data
- Overview
- Evolution of Data Management
- What Is Big Data?
- Big Data Sources
- Contrasting Approaches to Data Management and Analysis
- Big Data Characteristics
- Volume (Data at Rest)
- Velocity (Data in Motion)
- Variety (Data in Many Forms)
- Additional Big Data Characteristics
- Variability
- Complexity
- Veracity (Data in Doubt)
- Driving Transformation and Benefits
- Big Data: Transforming Insights
- Big Data Benefits
- Analyzing Big Data
- Big Data Analytics
- Analysis Process
- Technologies
- Operational versus Analytical
- Operational and Analytical Systems in Big Data
- Operational Big Data Workloads
- Analytical Big Data Workloads
- Combining Operational and Analytical Technologies
- Considerations for Decision-Makers
- Critical Dimensions in Big Data Technology
- Online versus Offline Big Data
- Software License Model
- Community
- Developer Appeal
- Agility
- General Purpose Versus Niche Solutions
- Big Data Challenges
- Analytics Challenges in Big Data Management
- Navigating Emerging Big Data Technologies
- Security and Management Challenges in Data Centers
- Big Data Security: Privacy and Regulations
- Archiving and Disposal
- Demand for Skilled Big Data Professionals
- Implementation Challenges
- Analysis Challenges in Big Data
- Heterogeneity and Incompleteness
- Scale
- Timeliness
- Privacy
- Human Collaboration
- Chapter 5: Data Integration and Quality
- Techniques for Integrating Data
- Data Integration: Overcoming Challenges and Strategies
- Data Format and Structure Variability
- Data Quality Issues
- Incompatible Systems and Technologies
- Scalability and Performance
- Advances in Data Integration Technologies
- Real-Time Data Integration
- Governance, Security, and Privacy
- Tools, Processes, and Frameworks
- Data Profiling and Cleansing Tools
- Standardization and Normalization Processes
- Middleware and Integration Platforms
- Data Governance Frameworks
- Security Protocols and Access Controls
- Scalable Architectures and Performance Tuning
- Data Quality
- Key Principles and Challenges
- Characteristics of Good Quality Data
- Data Quality Improvement Benefits
- Improving Data Quality
- Data Cleansing
- Fostering Data Quality Excellence
- Data Quality and MDM
- Data Cleansing Tools
- Data Validation
- Resources for Data Clean-up and Validation
- Chapter 6: Governance, Security, Privacy, and Ethics
- Data Governance
- Need for Governance
- Data Governance Policies
- Security
- Privacy
- Ethics
- Chapter 7: Data Warehousing
- Background
- Objective
- Definition
- Evolution of Information Processing Requirements
- Evolution of Data Chaos
- Limitations of OLTP Systems
- Evolution of Data Warehousing
- Decentralization to Centralization with a Twist
- Architecture
- Role of Data Warehouses in BI
- Benefits
- Shortcomings
- Types of Data Warehouses
- Enterprise Data Warehouse
- Data Mart
- Operational Data Store
- Data Warehouse Applications
- Design Objective
- OLTP Systems
- Data Warehouse Systems
- Characteristics
- Basic Data Warehouse Characteristics
- Subject-Oriented Characteristic
- Integrated Characteristic
- Time Variant Characteristic
- Nonvolatile Characteristic
- OLTP versus Data Warehouse Databases
- Characteristics of an OLTP Database
- Characteristics of a Data Warehouse Database
- Database Differences
- Contrasting Design Requirements
- Data Storage in Data Warehouses
- Design
- Star Schema
- Star Schema Characteristics
- Star Schema Benefits
- Benefit of De-Normalization
- Process and Components
- Data Warehousing Process
- Data Warehouse Process Components
- Acquisition Component
- Sources
- Preimport Operations
- Acquisition Process
- Storage Component
- Source and Target Databases
- Intermediate Database
- Access Component
- Access Tools
- Complexity of Data Access Tools
- Architecture
- Architecture Foundation
- Architecture Drivers
- Components
- Architecture Objective
- Infrastructure Components Supporting the Architecture
- Manifestation of Poor Architecture
- Data Warehouse Application Architecture
- Presentation Layer
- Functional Logic Layer
- Data Layer
- Designing for a Specific Technology
- Chapter 8: Data Lakes
- Overview
- Unlocking Data Lake's Potential
- Key Characteristics
- Benefits
- Challenges
- Architectural Components
- Storage Layer
- Data Ingestion
- Metadata Management
- Data Processing and Analytics
- Security and Governance
- Query and Exploration Interfaces
- Integration with External Systems
- Data Lakes Backbone: Data Ingestion and Integration
- Data Ingestion
- Data Integration
- Data Governance and Security
- Data Governance
- Data Security
- Use Cases and Applications
- Advanced Analytics and Business Intelligence
- Data Science and Machine Learning
- Real-Time Data Processing
- Enhancing Customer Experience
- Healthcare Analytics
- Risk Management and Compliance
- Media and Entertainment
- Supply Chain Optimization
- Data Lakes and Data Warehousing: Comparative Analysis
- Data Structure and Schema
- Data Storage
- Data Processing
- Schema Evolution
- Use Cases
- Costs
- Scalability
- Status of Data Lakes in Data Analysis
- Integration with Data Warehouses
- Improved Tooling and Ecosystem
- Advancements in Metadata Management
- Schema-on-Read and Schema Evolution
- Data Governance and Security
- Machine Learning and Advanced Analytics
- Serverless and Managed Services
- Real-Time Data Processing
- Cost Optimization
- Future Trends in Data Lake Technology
- Convergence
- Enhanced Metadata Management
- Advanced Data Governance and Security
- Augmented Data Management and Analytics
- Serverless and Managed Services Adoption
- Data Mesh Architecture
- Integration of Real-Time Data Processing
- Data Observability and Explainability
- Sustainability and Green Computing
- Multicloud and Hybrid Deployments
- Chapter 9: Extraction, Transformation, and Loading (ETL)
- ETL Process
- Crucial ETL Role in Data Management and Analytics
- ETL Components
- Extraction
- Transformation
- Loading
- ETL Operations
- ETL Process Duration
- ETL Testing
- Completeness
- Transformation
- Data Quality
- Performance
- Scalability
- ETL Challenges and Issues
- Challenges
- Underestimation
- Tools
- Tools for ETL
- Tasks Performed by ETL Tools
- ETL Tool Categories
- ETL Tool Selection Criteria
- Chapter 10: Data Design
- Data Modeling for Business Decision-Making
- Basics of Data Modeling
- Data Modeling in Business Decision-Making
- Entities and Attributes
- Significance of Entities in Data Modeling
- Identifying and Defining Entity Attributes
- Relationships
- Role of Entity Relationships in Data Modeling
- Relationship Types
- Normalization
- Role of Normalization in Data Modeling
- Normalization for Data Integrity
- Primary Keys and Foreign Keys
- Primary Keys
- Foreign Keys
- Data Types
- Data Types and Their Uses
- Choosing Data Types
- Modeling Tools
- Role of Data Modeling Tools
- Enhancing Database Design
- Cardinality
- Understanding Cardinality in Data Modeling
- Impact of Cardinality in Relational Databases
- De-Normalization
- Objective
- Balancing Normalization and Performance
- Multidimensional Data Modeling
- Concept
- Benefits
- Practical Applications
- Challenges
- Integration with BI and Analytics Tools
- Online Analytical Processing (OLAP)
- Concept
- OLAP Data Characteristics
- Benefits
- OLAP versus OLTP
- Purpose
- Data Structure
- Operations
- Concurrency
- Data Storage
- Data Design for Business
- Role of Data Design in Business Operations
- Strategic Importance of Data Structures
- User-Centric Data Design
- Needs of Business Users
- Benefits of User-Centric Data Design
- Versatility and Impact of Effective Data Design
- Effectiveness of Data Design
- Applications of Effective Data Design
- Best Practices for Effective Data Design
- Fostering Collaborative Data Design
- Key Considerations
- Trends in User-Focused Data Design
- Self-Service Analytics
- Augmented Analytics
- Evolution of Tools
- Chapter 11: R, SQL, and Python
- R for Business Users
- Overview
- Evolution
- Key Features
- Business Applications of R
- Data Analysis
- Statistical Modeling
- Visualization
- Advantages and Limitations of R
- Advantages
- Limitations
- Case Study: Predictive Maintenance
- SQL for Business Users
- Overview
- Fundamental Concepts
- Database Management Systems
- Business Applications of SQL
- Data Retrieval
- Data Modification
- Data Definition
- Advanced SQL Applications in BI
- Reporting and Analysis
- Data Warehousing
- Best Practices
- Writing Efficient Queries
- Security Considerations
- Python for Business Users
- Overview
- Evolution
- Role in Business Analytics
- Business Applications of Python
- Automation and Scripting
- Machine Learning and Predictive Analytics
- Web Development and Integration
- Integrating Python with Other Business Tools
- Connecting to Databases
- API Integration
- Python Challenges
- Learning Curve
- Maintenance and Support
- Chapter 12: Reporting in BI
- Data Visualization
- Types of Data Visualization
- Choosing a Visualization
- Best Practices
- Data Visualization Tools
- Storytelling
- Key Performance Indicators (KPIs)
- Strategic Selection of KPIs
- Performance Analysis through KPIs
- Setting KPI Targets and Benchmarks
- Monitoring and Analyzing KPI Trends
- Aligning KPIs with Business Objectives
- Ad Hoc Reporting
- Role of Ad Hoc Reporting
- Creating Ad Hoc Reports in BI Tools
- Customizing Ad Hoc Reports
- Ad Hoc Query Builders and Filters
- Best Practices
- Scheduled Reporting
- Automated Report Scheduling
- Frequency for Scheduled Reports
- Managing and Customizing Reports
- Integrating Scheduled Reports
- Data Accuracy
- Drill-Down and Drill-Up Capabilities
- Drill-Down and Drill-Up in BI
- Drill-Down Hierarchies
- Data Exploration Through Drill-Down
- Drill-Up for Summarizing Data Levels
- User Interaction and Experience
- Interactive Reporting
- Interactive Features of BI Tools
- User Interaction with Reports
- Filtering and Sorting
- Real-Time Data Interaction
- Balancing Complexity and Simplicity
- Mobile Reporting
- Empowering Decisions with Mobile Reporting
- Responsive Design for Mobile BI
- Mobile BI Tools and Platforms
- Offline Access and Mobile BI
- Security in Mobile Reporting
- Dashboarding
- Design Principles
- Crafting Actionable Dashboards
- Integrating Data Sources in Dashboards
- Real-Time Dashboard Updates
- Enhancing Dashboard Usability
- Design Basics
- Best Practices
- Reporting Tools
- Tool Ecosystem
- User-Friendly Interfaces
- Connectivity and Integration
- Scalability
- Collaboration
- Security
- Tool Features
- Excel
- Role in Data Analysis
- Features
- Analysis Functions
- Chapter 13: Cloud Computing
- Overview
- Understanding Cloud Computing
- Computing Resources
- Sharing of Resources
- Enabling Technologies
- Shared Characteristics
- Characteristics of Cloud Computing
- On-Demand Self-Service
- Broad Network Access
- Resource Pooling
- Rapid Elasticity
- Measured Service
- Benefits
- Cloud Service Models
- Infrastructure as a Service
- Platform as a Service
- Software as a Service
- Cloud Clients
- Deployment Models
- Private Cloud
- Public Cloud
- Hybrid Cloud
- Essential Cloud Capabilities
- Miscellaneous
- Challenges
- Myths About Cloud Computing
- Managing Operational Risks
- Big Data Meets the Cloud
- Chapter 14: Data Visualization
- Overview
- Techniques, Goals, and Challenges
- Benefits
- Data Visualization and Decision-Making
- Principles of Effective Data Visualization
- Clarity and Simplicity
- Relevance
- Accuracy
- Consistency
- Interactivity
- Hierarchy, Emphasis, and Narrative
- Accessibility
- Generating Best Visuals
- Using Data Visualization Effectively
- Characteristics of Effective Graphics
- Quantitative Messages
- Time-Series
- Ranking
- Part-to-Whole
- Deviation
- Frequency Distribution
- Correlation
- Nominal Comparison
- Geographic or Geospatial
- Common Forms of Data Visualization
- Types of Presentation Media
- Charts
- Popular Visualizations
- Advanced Visualizations
- Status Indicators
- Visual Display Elements
- Tools for Data Visualization
- Based on Integration and Ecosystem
- Based on Programming Language
- Cloud-Based Platforms
- Open-Source Tools
- Best Practices
- Empower Nontechnical Users
- Tailor Visualization Capabilities
- Broaden Visualization Functionality
- Create Unified Dashboards
- Prioritize Self-Service Capabilities
- Address Time Series Analysis
- Leverage Geospatial Analysis
- Strategize Desktop and Mobile Integration
- Explore In-Memory Computing
- Develop Operational Efficiency
- Chapter 15: Predictive Analytics
- Advanced Analytics in BI
- Evolution Beyond Descriptive Analytics
- Landscape of Advanced Analytics
- Predictive Analytics in BI
- Addressing Business Challenges with Advanced Analytics
- Interplay of Data Science and BI
- Unleashing Predictive Analytics
- Role of Predictive Analytics
- Predictive Analytics: Shaping Organizational Success
- Benefits
- Big Data and Predictive Analytics
- Unique Predictive Analytics Role in Data Analysis
- Use Cases
- Barriers
- Predictive Analytics Process
- Business Understanding
- Data Understanding
- Data Preparation
- Modeling
- Evaluation
- Deployment
- Predictive Analytics Techniques
- Decision Analysis and Optimization
- Transaction Profiling
- Predictive Search
- Predictive Modeling
- Descriptive Models
- Decision Models
- Predictive Model Development Process
- Project Definition
- Exploration
- Data Preparation
- Model Building
- Deployment
- Model Management
- Analytical Techniques
- Regression
- Machine Learning
- Predictive Analytic Tools
- Evolution
- Current Status
- Types of Tools
- Open-Source Predictive Analytic Tools
- Commercial Predictive Analytic Tools
- Chapter 16: BI and Data Analysis
- BI and Data Analysis Relationship
- Definition
- Business Intelligence
- Data Analysis
- Scope
- Business Intelligence
- Data Analysis
- Purpose
- Business Intelligence
- Data Analysis
- Tools and Technologies
- Business Intelligence
- Data Analysis
- Time Frame
- Business Intelligence
- Data Analysis
- Integration
- Business Intelligence
- Data Analysis
- Unveiling the Analytical Landscape
- Evolution of Data Analysis
- Data Analysis: Techniques, Insights, and Ethics
- Navigating Analytical Challenges
- Handling Outliers and Missing Data
- Addressing Data Biases
- Managing Limitations of Analytical Tools
- Chapter 17: Data Analysis: Fundamentals and Analytics
- Data Analysis Essentials
- Essence and Evolution of Data Analysis
- Decoding Data Analysis
- Historical Context and Evolution
- Data Types and Sources
- Types of Data
- Sources of Data
- Data Collection and Cleaning
- Methods of Data Collection
- Data Cleaning and Preprocessing Techniques
- Exploratory Data Analysis
- Overview
- Techniques to Summarize and Visualize data
- Statistical Foundations and Hypothesis Testing
- Statistical Foundations
- Basic Statistical Concepts
- Role of Probability Distributions
- Hypothesis Testing
- Significance of Hypothesis Testing
- Steps of Hypothesis Testing
- Correlation and Regression Analysis
- Relationships Between Variables
- Exploring Correlation and Regression Analysis
- Chapter 18: Data Analysis: Advanced Insights
- Data Visualization
- Role in Analysis
- Techniques
- Machine Learning
- Basic Concept
- Machine Learning in Data Analysis
- Data Analysis Tools
- Popular Data Analysis Tools
- Data Visualization Tools and Libraries
- Advanced Analytics
- Harnessing Big Data for Advanced Analysis
- Advanced Analytics Techniques in Data Analysis
- Time Series Analysis
- Data Interpretation and Reporting
- Chapter 19: AI in BI and Data Analysis
- Understanding AI
- Definition of AI
- Objective
- Integration
- Interconnected Landscape: AI, BI, and Data Analysis
- Data-Driven Decision-Making
- Historical Context and Evolution of AI in BI
- Exploring AI's Role in BI and Data Analysis
- BI and Data Analysis Transformation with AI
- Application of AI Technologies in BI
- The Impact of AI on Data Processing
- AI Integration with BI Tools and Platforms
- AI-Driven Innovations in Data Analysis
- Benefits of AI Adoption in BI
- Leveraging AI for Smarter Decision-Making
- Role of Data-Driven Decision Making
- The Vital Role of AI in Data-Driven Insights
- AI: Enhancing Decision Accuracy
- AI for Real-Time Decisions
- AI Implementations in Decision-Making Processes
- Case Study 1: Enhanced Risk Management
- Case Study 2: Optimized Supply Chain Management
- Case Study 3: Predictive Maintenance in Manufacturing Company
- Case Study 4: Customer Service at a Financial Institution
- How AI Enhances Data Analysis
- Power of AI-Driven Insight Generation
- Harnessing Predictive Analytics with AI
- Power of Prescriptive Analytics in BI
- Impact of AI Recommendation Systems
- Case Studies: The Impact of AI on Business Performance
- AI Applications in BI
- AI Revolutionizes Marketing Analytics
- AI-Powered Customer Segmentation and Personalization
- AI for Sales: Forecasting and Revenue Prediction
- AI Revolutionizes Supply Chain Management
- Case Studies: Real-World AI Applications in BI
- Case Study 1: Recommendation System
- Case Study 2: Optimization of Inventory Management
- Case Study 3: Patient Outcome Improvement
- Enhancing Data Analysis with AI Algorithms
- Machine Learning Algorithms in AI
- Supervised versus Unsupervised Learning in BI
- The Power of Deep Learning
- Machine Learning in Data Organization
- Challenges in Machine Learning Implementation
- AI in BI: Benefits and Challenges
- Benefits of AI Adoption in BI and Data Analysis
- AI Integration Challenges in BI Infrastructure
- AI Integration in Established BI Systems
- Data Privacy and Security in AI Implementations
- Resistance to AI Adoption
- Chapter 20: AI in BI and Data Analysis: Risks, Ethics, and Future
- Risks
- Bias and Fairness
- Accuracy and Reliability
- Overreliance
- Interpretability and Transparency
- Employment
- Security
- Privacy
- Regulatory/Compliance
- Legal
- Ethics
- Ethical Dilemmas
- Ethical AI Practices
- Bias
- Fairness and Transparency
- Future of AI in BI and Data Analysis
- Current Trends and Developments in AI for BI
- Predictions for the Future of AI-Driven Data Analysis
- Emerging Technologies Shaping the Future
- Strategies to Stay Ahead in the AI Revolution
- Index
