1. Bus Matrix… the foundation of your Data Warehouse
The Bus Matrix is the cornerstone of a successful Dimensional Data Warehouse strategy. It serves many purposes: from communicating requirements, capabilities, and expectations with the business users down to the prioritization and delegation of tasks across the development team. Join me in this session and learn what a Bus Matrix is, why it is the single most important document in your Data Warehouse project, and what can go wrong without it. We'll also cover several approaches for creating and maintaining the Bus Matrix document.
Bill Anton is an independent consultant whose primary focus is designing and developing Data Warehouses and Business Intelligence solutions using the Microsoft BI stack. When he's not working with clients to solve their data-related challenges, he can usually be found answering questions on the MSDN forums, attending PASS meetings, or writing blog entries over at
Bill Anton
Prime Data Intelligence

2. About Me I Love Data! …also, Microsoft DW/BI (MCTS/MCITP, MCSA/MCSE)
Independent Prime Data Intelligence, LLC
Atlanta BI SQL Server Users Group
Twitter: @SQLbyoBI
Blog:

3. What we will cover today 
Dimensional Modeling 101
What, Why, How
Common Challenges
Bus Matrix
What is it?
How does it help?
Examples
Links on resource page of blog

4. What is Dimensional Modeling?
Facts
additive amounts
E.g. Sales amount, inventory quantity
SUM, AVERAGE, MAX, MIN, COUNT
Dimensions
descriptive attributes
E.g. Date, Product, Location, Customer
GROUP BY <attribute>, <attribute>, etc

5. What is Dimensional Modeling?
Each fact forms the center of a star
Ex. this customer, bought this product on this date….
“Star Schema”

6. What is Dimensional Modeling?
Denormalization
“Repeating Values”
Opposite of “normalized” (e.g. 3rd Normal Form)
Optimized for reads (not writes)

7. Dimensional Modeling 101
Question: What are the most common types of Data Warehouse methodologies/architectures?
Kimball
Inmon
Data Vault
Kimball: star-schema, conformed dimensions
Inmon: 3NF data warehouse, Coporate Information Factory, Hub-n-Spoke
Data Vault: highly controversial…I love it.
Is Anyone Familiar with Data Vault?

8. All of them  Dimensional Modeling 101
Question: For which of these DW methodologies should you include a dimensional model? Kimball, Inmon, Data Vault
All of them 
With Kimball it is built in.
With Inmon/DataVault you need to add a dimensional layer.

9. Kimball Dimensional DW

10. Kimball Dimensional DW
Sales
Production
Finance
Supply Chain
bus architecture

11. Inmon 3NF EDW + Data Mart(s)
Does anyone know what “EDW” stands for?
How is that different from “Kimball” method?

12. Data Vault + Data Mart(s)
time-invariant system of record (S-O-R)
Copies of source systems
Business rules applied downstream
Hubs/links/satellites
dan linstedt
Data Vault Method: Since no business rule is applied to the data on the way into the EDW, the EDW becomes a ‘statement of fact’.  This single version of facts now becomes a time-invariant system of record (S-O-R) where the facts as they were known to the business at any point in history are stored. In our example above, the EDW stores both the addresses, that borrower has.

13. Why Dimensional Modeling
Intuitive to Business Users
Simpler than OLTP/3NF
Rise of Self-Service (E.g. Power Pivot, Power View)
Iterative Development
“Agile”
Performance
Optimized for analytical queries e.g. sales amount by product in 2013 for top 10 all-time customers
And many more…
See Teo Lachev’s “WHY SEMANTIC LAYER” newsletter:
With Kimball it is built in
With Inmon/DataVault you need to add a dimensional layer
Self-Service BI = implies more and more users will be looking for access to data (think: PowerPivot)
“Semantic Layer”

14. Intuitive to Business Users
Business users think in terms of Dimensions and Fact whether they know it or not.
Teach a Business User how to use a pivot table…

15. How many bikes did we sell last year?

16. Do we sell more bikes to single or married females?

17. What was our most/least profitable product this year?

18. What was the Average Monthly Gross Margin Return on Inventory Investment (GMROII) by Product Category for the trailing 6 months?
It’s Complicated
We’ll come back to this…

19. Star-Schema Each fact forms the center of a star
Ex. this customer, bought this product on this date….

20. 1 “Star” per Fact table Sales Process Inventory Process
Each fact forms the center of a star
Sales Process
Inventory Process

21. Facts are related through dimensions…
But there are usually dimensions in common
GMROII (Gross Margin Return on Inventory Investment)
Sales Process
Inventory Process

22. Facts are related through dimensions…
“Conformed Dimensions” A conformed dimension is a set of data attributes that have been physically referenced by multiple fact tables using the same key value to refer to the same structure, attributes, domain values, definitions and concepts.
Dimensions are conformed when they are either exactly the same (including keys) or one is a perfect subset of the other.
Dimension tables are NOT conformed if the attributes are labeled differently or contain different values.
MUST BE IDENTICAL
By linking facts through conformed dimensions, cross-process analysis is possible
Wikipedia (

23. Dimensions: Conformed vs Unconformed

24. Revisiting Average Monthly Gross Margin Return on Inventory Investment (GMROII)
By linking facts through conformed dimensions, cross-process analysis is possible
Sales + Inventory  GMROII
Average Monthly GMROII
Profit for total time period
Sum of each month ending inventory cost

25. What was the Average Monthly Gross Margin Return on Inventory Investment (GMROII) by Product Category for the trailing 6 months?
This becomes easy…
* As long as users understand the dimensions in common

26. Where things start to get complex…
1 Star per Fact table
Multiple Fact tables per business process
Multiple business processes in an enterprise
Modeling business processes

27. Dimensional Model becomes a “Galaxy of Stars”
Finance
Production
Sales
Distribution
Modeling business processes HR

28. ER Diagram: Adventure Works Sample DW
Common Method for Visually Documenting a Database
Not so bad…
Just a sample – not realistic
Business Users don’t understand
Simpler than Normalized data model

29. For bigger Data Warehouses…
Multiple fact tables per business process
Specialty fact tables for certain metrics
Inventory Control Process
Work Orders
Purchasing/Procurement
Model to the metrics
SQL Bits: Data Modeling for Analysis Services Cubes
Alex Whittles
This ^^
Turns into this ^^

30. Variety of Problems to Overcome with Dimensional Modeling
Communication & Strategy
What’s the short term plan of attack?
What’s the long term plan of attack?
Documentation
What’s in our Data Warehouse?
Business Users can’t read ER diagrams
Business Users are typically only familiar with a 1 or 2 business processes
E.g. Sales User vs Inventory User; Warehouse Supervisor vs CEO
Conforming Dimensions is hard…REALLY hard
So are changes (E.g. Impact Analysis)
POA
Boil the ocean vs Iterative
Conforming Dimensions
political battle
takes time (e.g. MDM)
business needs to understand the importance of it

31. What’s the Solution? What about a Bus Matrix?
Train business users to read ER Diagrams?
Simplify Data Model?
Ignore certain business processes?
Don’t use Conformed Dimensions?
Force business users to manually map data between processes?
What about a Bus Matrix?
How do events in one business process impact/correlate with other Business Processes
Not a single answer – but a Bus Matrix can address most of the problems

32. What is a Bus Matrix?
2-dimensional visualization showing the intersection of facts and dimensions
In the most basic form
Understandable by business users

33. Variety of Use-Cases for a Bus Matrix
Documentation, Communication, Training
Facilitate User Adoption of BI tools
Communicate Expectations w/ Business
New users unfamiliar with new business process
Team Development
Agile
Prioritization of Tasks
Divide & Conquer
Road-Mapping
Prioritization of Business Processes in a Business Intelligence “Program”
Documentation:
Facilitate User Adoption of BI tools
Communicate Expectations w/ Business
New users unfamiliar with new business process
Team Development:
Agile, Prioritization of Tasks
Roadmapping Tool:
Prioritization of Business Processes in Business Intelligence “Program”
How many people understand the difference between a BI project and BI program?

34. Documentation For Business
Easier than an ER diagram
In Business Terms
Users can now understand what dimensions to slice/dice measure groups from a specific business process
Shows how difference business processes are related

35. Documentation for IT Adding context to relationships
Differentiating between role playing dimension and base dimension
Indicating type of Dimension, type of Fact, and grain

36. Master Bus Matrix
Impact Analysis

37. Team Development Sprint 1 Internet Sales Sprint 2 Reseller Sales
Break dimensions/facts into separate tasks (database, ETL, cube)
Prioritize Dimensions before Fact Tables
Next sprint, only create the missing dimensions

38. Road-Mapping Quadrant: Business Value vs Implementation Complexity
Bus Matrix Version
Context for delivery dates
Source Systems contain data for multiple business processes

39. When To Create a Bus Matrix
During Requirements Gathering
Before You Start Development!
Updated Over Time
Changes to Business Processes
New Source Systems (E.g. mergers/acquisitions)

40. How To Create a Bus Matrix
Manual via Excel
Automated via SSRS

41. Manual Only option when starting out ;-)
Updates can be made quickly made as requirements come in
Adds development overhead, but the ROI is well worth it

42. Automated
Reporting pack with drill-through to data dictionary information
Can be based on Cube or Relational Database (*FK required)
Incorporate query statistics to visualize common usage patterns
Use MDS to allow SME’s to manage business definitions
Based on example from Alex Whittles
Based on example from Alex Whittles

43. QUESTIONS

44. http://byobi.com/blog/bus-matrix/ References Twitter: @SQLbyoBI
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