This presentation is a brief primer on Dimensional Modeling for BI

1. DIMENSIONAL MODELING
A primer on data modeling techniques for data warehouse design
By Rauf Ahmed

2. Agenda
 What is a Data Warehouse?
 What problem it solves?
 Where does Dimensional Modeling fit in?
 Basic concept of Dimensional Modeling
 Foundation of Design Concepts
 Q&A

3. Data Warehouse
“Data warehousing is the design and implementation
of processes, tools, and facilities to manage and
deliver complete, timely, accurate, and
understandable information for decision making. It
includes all the activities that make it possible for an
organization to create, manage, and maintain a data
warehouse or data mart.”
(IBM Data Modeling Techniques for Data Warehousing)

4. Data Warehouse Goals
 Easy information access
 Consistent information presentation
 Adaptive and resilient to change
 Information assets protection
 Foundation for improved decision making
 Acceptable by Business Community
(The Data Warehouse Toolkit)

5. Data Analysis Techniques
 Query
 Analyze
 Discover
(IBM Data Modeling Techniques for Data Warehousing)

6. Data Warehouse Basic Elements
(The Data Warehouse Toolkit)

7. Data Presentation Area
Key Considerations…
 Dimensional Model Vs Normalized Model
 Global Data Warehouse Vs Independent Data Marts
 Top-down Vs Bottom-up
 Atomic Vs Summarized Data
(The Data Warehouse Toolkit)

8. Dimensional Model Components
A fact is a collection of related data
items, consisting of measures and context
data. A fact contains the information the
business is interested in
A dimension is a collection of members or
units of the same type of views. A
dimension is the window to the
information contained in the facts

9. Dimensional Model Myths
 Dimensional models and data marts are for
summary data only.
 Dimensional models and data marts are
departmental, not enterprise, solutions and
Dimensional models and data marts can’t be
integrated
 Dimensional models and data marts are not scalable
 Dimensional models and data marts are only
appropriate when there is a predictable usage
pattern
(The Data Warehouse Toolkit)

10. Dimensional Model Process
 Select business process to model
 Declare grain of the business process
 Choose dimensions that apply to each fact table
row
 Identify numeric facts that will populate each fact
table row

11. Sample Dimensional Model
(The Data Warehouse Toolkit)

12. Design Concepts 1
 Snow flake vs Star Schema
 How many dimensions?
 Degenerate Dimensions
 Surrogate Keys
 Null Keys Handling
 Date Dimension and its Surrogate Key
 Factless Fact Tables
(The Data Warehouse Toolkit)

13. Design Concepts 2
 Periodic Snapshots
 Semi-additive facts
 Accumulating Snapshots
 Bus Architecture
 Conformed Dimensions
 Slowly Changing Dimensions
 Overwritingthe value
 Adding Dimension Row
 Adding Dimension Column
(The Data Warehouse Toolkit)

14. Design Concepts 3
 Role Playing Dimensions
 Junk Dimension (Indicators)
 Fact Normalisation
 Multiple Currencies
 Currency Conversion Fact
 Header & Line Facts (different granularity)
 Multiple UOM
(The Data Warehouse Toolkit)

15. Design Concepts 4
(The Data Warehouse Toolkit)

16. Design Concepts 5
 Aggregated Facts as Attributes
 Age Groups
 Volume Buckets
 Spend Buckets etc
 Dimension Outriggers
 Category Dimension (Start Date)
 Time Intelligence
 YTD, QTD, CY, LY, CM, LM, etc
(The Data Warehouse Toolkit)

17. More Design Concepts…
 Partitioning
 Rapidly changing dimensions
 Bridge Tables (Variable Depth Hierarchies)
 ClickStream Analysis
 Audit Dimensions
 Building Data Warehouse
 Basket Analysis
(The Data Warehouse Toolkit)

18. References
Books:
 The Data Warehouse Toolkit (Ralph Kimball, Margy Ross)
 Mastering Data Warehouse Design (Wiley Press)
 Building the Data Warehouse (W. H. Inmon)
 Data Modeling Techniques for Data Warehousing (IBM Press)
Internet:
http://www.kimballgroup.com/html/designtips.html
http://www.inmoncif.com/home/
http://inmoninstitute.com/