bus matrix… the foundation of your data warehouse

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Bus Matrix… the foundation of your Data Warehouse. Bill Anton Prime Data Intelligence. About Me. I Love Data! …also, Microsoft DW/BI (MCTS/MCITP, MCSA/MCSE) Independent Consultant @ Prime Data Intelligence, LLC Atlanta BI SQL Server Users Group Twitter: @ SQLbyoBI - PowerPoint PPT Presentation

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Bus Matrix…the foundation of your Data Warehouse

Bill Anton Prime Data Intelligence

About Me• I Love Data!• …also, Microsoft DW/BI (MCTS/MCITP, MCSA/MCSE)

• Independent Consultant @ Prime Data Intelligence, LLC• Atlanta BI SQL Server Users Group

• Twitter: @SQLbyoBI• Blog: http://byoBI.com • Email:william.anton@gmail.com

What we will cover today • Dimensional Modeling 101• What, Why, How• Common Challenges

• Bus Matrix• What is it?• How does it help?• Examples

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

What is Dimensional Modeling?

FACT

DIMENSION

DIMENSION DIMENSION

DIMENSIONDIMENSION

“Star Schema”

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

Dimensional Modeling 101Question: What are the most common types of Data Warehouse methodologies/architectures?

• Kimball• Inmon• Data Vault

Question: For which of these DW methodologies should you include a dimensional model?

Kimball, Inmon, Data Vault

All of them

Dimensional Modeling 101

Kimball Dimensional DW

Source Stage DW Cubes

Inmon 3NF EDW + Data Mart(s)

Source Stage 3NF DW CubesData Marts

Data Vault + Data Mart(s)

Source Stage Data Vault DW CubesData Marts

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:http://www.prologika.com/Newsroom/Newsletter2013Fall.aspx

Intuitive to Business Users

Dimensions

Dimensions

Facts

How many bikes did we sell last year?

Do we sell more bikes to single or married females?

What was our most/least profitable product this year?

What was the Average Monthly Gross Margin Return on Inventory Investment (GMROII) by Product Category for the trailing 6 months?

It’s Complicated

Star-Schema

FACT

DIMENSION

DIMENSION DIMENSION

DIMENSIONDIMENSION

Sales

Product

Store Sales Person

Customer

Date

1 “Star” per Fact table

Sales Process Inventory Process

Sales

Product

Sales Person Store

Customer

Date

Inventory

ProductStore

Date

Facts are related through dimensions…

Sales

Product

Sales Person Store

Customer

Date

Inventory

ProductStore

Date

Sales Process Inventory Process

Facts are related through dimensions…

Sales Product

Sales Person Store

Customer Date

Inventory

“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.

Dimensions: Conformed vs Unconformed

Revisiting Average Monthly Gross Margin Return on Inventory Investment (GMROII)

Sum of each month ending inventory cost

Sales Product

Sales Person Store

Customer Date

Inventory

Profit for total time period

Average Monthly GMROII

What was the Average Monthly Gross Margin Return on Inventory Investment (GMROII) by Product Category for the trailing 6 months?

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

Dimensional Model becomes a “Galaxy of Stars”

Sales

FinanceProduction

HR

Distribution

ER Diagram: Adventure Works Sample DW

For bigger Data Warehouses…

This ^^ Turns into this ^^

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)

What’s the Solution?• 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?

What is a Bus Matrix?

2-dimensional visualization showing the intersection of facts and dimensions

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 For Business

Documentation for IT

Master Bus Matrix

Team DevelopmentSprint 1Internet Sales

Sprint 2Reseller Sales

Road-Mapping

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)

How To Create a Bus MatrixManual via Excel Automated via SSRS

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

Automated

Based on example from Alex Whittleshttp://www.purplefrogsystems.com/blog/2010/09/olap-cube-documentation-in-ssrs-part-1/

• 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

QUESTIONS

References

http://byobi.com/blog/bus-matrix/

Twitter: @SQLbyoBIBlog: http://byoBI.com Email: william.anton@gmail.com

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