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by David Haertzen
The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics
Cover Page
Title Page
Copyright Page
Contents at a Glance
Table of Contents
Acknowledgements
Introduction
Who Should Read this Book
Prerequisites
How to Benefit from this Book
Chapter Expectations
Learn More
Case Study: 3M a Global Manufacturing Firm
Companion Website and Templates
About the Author
Confessions of the Author
Chapter 1 Data Warehousing Perspectives
What are Business Intelligence (BI) and Analytics?
Decisions Impact the Bottom Line
Examples of Business Intelligence Results
What is Enterprise Data Warehousing (EDW)?
What a Data Warehouse Is and Is Not
Beyond Data Warehousing
Assessing Data Warehouse BI Maturity
DW and BI are Management Disciplines
Operational Data vs. Data Warehouse Data
High Quality Data
Decision Support Goals
Data Warehousing Enterprise Architecture
Data Warehousing Trends and Hot Topics
Learn More
Chapter 2 Business Case and Project Management
Six Steps to Developing the Business Case
BC Step 1.0 – Initiate Business Case
BC Step 2.0 – Analyze Current Approach
BC Step 3.0 – Analyze Alternative Approaches
BC Step 4.0 – Determine Transition Costs
BC Step 5.0 – Assemble Business Case
BC Step 6.0 – Present Business Case
Case Study: 3M, Global EDW Business Case
Data Warehousing Project Management
Defining Scope and Objectives
Finding the Right Sponsor
Producing the Project Roadmap and Plans
Organizing the Team
Training the Team
Executing the Plan
Finishing the Project
Case Study: 3M, Enterprise Data Warehouse Continued
Project Management Tips
Avoiding Major Data Warehouse Missteps
Learn More
Chapter 3 Business Architecture and Requirements
Business Architecture
Data Warehousing Business Requirements
Homework for Data Warehouse Requirements Elicitation
Identify Business Intelligence User Groups
Data Exploration
Interview Business Intelligence Users
Group Methods
Documenting Requirements
Functional Requirements
Non-Functional Requirements
Case Study: 3M, a Global Manufacturing Firm
Data Warehousing Requirements Workshop
Roles and Responsibilities
Preparing for the Session
Executive Sponsor and User Manager Roles
Room Layout
Group Methods and Techniques
Outcomes of Effective BI Requirements Sessions
Requirements Session Follow Up
Learn More
Chapter 4 Data Warehousing Technical Architecture
Technical Architecture
Functional and Non-functional Requirements
Technical Architecture Principles
Buy, Build, or Re-use Approach
Technical Architecture Roadmaps
Data Architecture
Data Warehousing Technical Architecture
Data Sources
Data Integration
Case Study: 3M, a Global Manufacturing Firm
Data Storage
Data Warehouse vs. Data Mart
BI / Analytics
Data Governance, Metadata and Data Quality
Data warehousing Infrastructure
Data Warehousing Technology Stack
Data Warehouse in the Cloud
Managing, Operating, and Securing the Data Warehouse
Data Warehouse Architecture Tips
Data Warehouse Architecture Traps
Learn More
Chapter 5 Data Attributes
Raw Data
Attributes
Qualitative and Quantitative Attributes
Naming Attributes
Key Performance Indicators (KPIs)
Learn More
Chapter 6 Data Modeling
Data Modeling Tools
Data Modeling Levels
Industry Data Models
Section 6A – Entity Relationship Modeling
Entities
Attributes
Relationships
Cardinality
Exclusive Supertype / Subtype
Non-Exclusive Supertype / Subtype
Derived Data
Normalization
First Normal Form
Second Normal Form
Third Normal Form
Fourth Normal Form
Fifth Normal Form
Summary: The Entity Relationship Model
Real World Data Models
Learn More
Section 6B – Data Warehouse Modeling
Source Systems
Landing Stage
Delta Stage
Atomic Stage
Support Tables
Atomic Data Warehouse
Learn More
Chapter 7 Dimensional Modeling
Dimensional Data Modeling
Star Schema
Example Star Schema
Facts – the Data Mart Measuring Stick
Granularity
Fact Width and Storage Utilization
Mathematical Nature of Facts – Can it “Add up”?
Event or Transaction Fact
Snapshot Fact
Cumulative Snapshot Fact
Aggregated Fact
“Factless Fact”
Dimensions Put Data Mart Facts in Context
Dimension Keys
Data Modeling Slowly Changing Dimensions
SCD Type 0 – Unchanging Dimensions
SCD Type 1 – Overwrite
SCD Type 2 – New Row
SCD Type 3 – New Column
Date and Time Dimension
One Dimension – Multiple Roles
Snowflake Schema
Dimensional Hierarchies
Bridge Tables Support Many to Many Relationships
Degenerate Dimension
Profile Dimension
Junk Dimension
Specifying a Fact
Specifying Data Mart Attributes
The Big Picture – Integrating the Data Mart
Data Warehouse Bus
The Bigger Picture – The Federated Data Mart
Learn More
Chapter 8 Data Governance and Metadata Management
Five Steps to Data Governance (DG)
DG Step 1.0 – Assess Data Governance Maturity
DG Step 2.0 – Design Data Governance Structures
DG Step 3.0 – Create Data Governance Strategy
DG Step 4.0 – Create Data Governance Policies
DG Step 5.0 – Monitor and Maintain Data Governance
Metadata – “Data about data”
Data Warehouse Metadata
How Can Data Warehousing Metadata be Managed?
Metadata Manager / Repository
Learn More
Chapter 9 Data Sources and Data Quality Management
Understanding Data Sources
Identifying Data Sources
Data Source Questions
Dimension Data Sources for the Data Mart
Identifying Fact Data Sources for the Data Mart
Detailed Data Source Understanding for Data Warehousing
Obtain Existing Documentation
Model and Define the Input
Profile the Data Source
Data Profiling and Data Quality
Grey Data
Six Steps to Data Quality Management (DQM)
Learn More
Chapter 10 Database Technology
Relational Databases
Big Data – Beyond Relational
Federated Databases
In Memory Databases
Column Based Databases
Data Warehouse Appliances
OLAP Databases
ROLAP Uses SQL for Business Intelligence
MOLAP Business Intelligence Benefits
Learn More
Chapter 11 Data Integration
Data Integration
Data Mapping
Extracting Data to Staging Area
Applying Data Transformations
Loading the Data
Loading the Atomic Data Warehouse
Loading the Data Mart
Loading Data Mart Dimensions
Loading Data Mart Facts
ETL Tools
Programmer Written ETL Applications
Data Warehousing ETL Tools At Hand
COTS Dedicated ETL Tools
Open Source ETL
Learn More
Chapter 12 Business Intelligence Operations and Tools
Section 12A – Business Intelligence Operations
Slice and Dice
Roll Up
Drill Down
Pivot
BI Operations Tips and Traps
Section 12B – Business Intelligence Tools
Interactive Query and Analysis Tools
Reporting Tools
Data Visualization Tools
Data Mining and Statistical Tools
Evaluating BI Tools
Learn More
Chapter 13 Number Crunching Part 1: Statistics
Collecting Data
Descriptive Statistics
Measures of Dispersion
Inferential Statistics – Regression Models
R and Octave Open Source Software
Learn More
Chapter 14 Number Crunching Part 2: Data Mining
Predictive Data Mining
Descriptive Data Mining
Clusters
Predictive Data Mining – Decision Trees
Predictive Data Mining – Neural Nets
Data Mining Tips and Traps
Learn More
Chapter 15 Number Crunching Part 3: An Analytic Pattern
Analytical Application Example Architecture
Chapter 16 Presenting Data: Scorecards and Dashboards
Scorecards
Obtaining Scorecard Data
Scorecard Tips and Traps
Dashboards
Obtaining Dashboard Data
Dashboard Tips and Traps
Data Visualization Graphics Types
Mobile Device Considerations
Learn More
Chapter 17 Business Intelligence Applications
Financial BI Applications
Supply Chain and Manufacturing BI Applications
Operations BI Applications
Performance Management BI Applications
Risk Management BI Applications
Government BI Applications
Case Study: Richmond, VA Policy Department
Chapter 18 Customer Analytics
Single Customer View
Common Customer Data Model
Identifiers
Demographics
Psychographics
Locations (Geographics)
Products
Interactions
Transactions
Measures
Relationships / Hierarchies
Single Customer View Tips and Traps
Customer Segmentation
Bands
Clusters
Customer Analytics Terms
Analysis Types
Learn More
Chapter 19 Testing, Rolling Out, and Sustaining the Data Warehouse
Section 19A – Testing the Data Warehouse
Data Warehouse Testing Responsibilities
Business Requirements and Testing
Data Warehousing Test Plan
Testing Environments and Infrastructure
Unit Testing for the Data Warehouse
QA Testers Perform Many Types of Tests
Can the Data Warehouse Perform?
Business Users Test Business Intelligence
Business Intelligence Must Be Believed
Section 19B – Business Intelligence Rollout
Pre-deployment for Business Intelligence Rollout
Deployment of Data Warehouses
Training Business Intelligence and Data Warehouse Users
Follow-up Support for the Data Warehouse
Business Intelligence Follow-up Assessment
Section 19C – Sustainable Business Intelligence
BI / Data Warehousing People and Process Tips
BI / Data Warehousing People and Process Traps
Sustaining BI / Data Warehousing Tips
Sustaining BI / Data Warehousing Traps
Appendix A Glossary
Appendix B Suggested Readings
Data Warehouse
Appendix C References
Index
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