1. ISQS 6339, Business Intelligence Creating Data Marts
Zhangxi Lin
Texas Tech University

2. Outline Illustrative Example : Adventure Works Cycles (AWC)
Hands-on Case: Maximum Miniature Manufacturing
Data Warehousing with Microsoft SQL Server 2008
Exercise 2
Types of Dimension
Exercise 3

3. Illustrative Example : Adventure Works Cycles (AWC)

4. Adventure Works Cycles (AWC)
A fictitious multinational manufacturer and seller of bicycles and accessories
Based on Bothell, Washington, USA and has regional sales offices in several countries
ISQS 6339, Data Mgmt & BI, Zhangxi Lin

5. Basic Business Information
Product orders by category
Product Orders by Country/Region
Product Orders by Sales Channel
Customers by Sales Channel Snapshot
ISQS 6339, Data Mgmt & BI, Zhangxi Lin

6. AWC Business Requirements - Interview summary
Interviewee: Brian Welker, VP of Sales
Sales to resellers: $37 million last year
17 people report to him including 3 regional sales managers
Previous problem: Hard to get information out of the company’s system
Major analytic areas:
Sales planning
Growth analysis
Customer analysis
Territory analysis
Sales performance
Basic sales reporting
Price lists
Special offers
Customer satisfaction
International support
Success criteria
Easy data access, Flexible reporting and analyzing, All data in one place
What’s missing? – A lot – No indication of business value
ISQS 6339, Data Mgmt & BI, Zhangxi Lin

7. Business Processes Purchase Orders Distribution Center Deliveries
Distribution Center Inventory
Store Deliveries
Store Inventory
Store Sales
ISQS 6339, Data Mgmt & BI, Zhangxi Lin

8. Analytic Themes
See the Excel file \\TechShare\coba\d\isqs3358\Repository\AWC\ AW_Analytic_Themes_List.xls
SQL Server 2008 R2 – Data Warehousing Scaling and Performance 41’28”
ISQS 6339, Data Mgmt & BI, Zhangxi Lin

9. AWC’s Bus Matrix Dimensions Business Process Date Product Employee
Dimensions
Business
Process
Date
Product
Employee
Customer (Reseller)
Customer (Internet)
Sales Territory
Currency
Channel
Promotion
Call Reason
Facility
Sales
Forecasting X
Orders
Call
Tracking
Returns X   X
ISQS 6339, Data Mgmt & BI, Zhangxi Lin

10. Prioritization Grid High Customer Profitability Orders Product
Forecast
Business
Value /
Impact
Call
Tracking
Exchange
Rates
Returns
Manufacturing
Costs
Feasibility
Low
Low
High
ISQS 6339, Data Mgmt & BI, Zhangxi Lin

11. Hands-on Case: Maximum Miniature Manufacturing

12. Maximum Miniatures Manufacturing – Designing Data Mart
General business needs
To analyze the statistics available from the manufacturing automation systems. The VP would like an interactive analysis tool, rather than printed reports, for the analysis.
The manufacturing automation system controls all the machines to create figurines
Filling a mold with the raw material
Aiding the hardening of this materials
Removal from the mod when hardening is complete
Computerized painting of the figurines
Curing the paint if necessary

13. Maximum Miniatures Manufacturing – Creating Data Mart
Specific Business Needs
Analyzing the following numbers
Dollar value of products sold
Number of products sold
Sale tax charged on products sold
Shipping charged on products sold
These numbers should be viewable by:
Store
Sales Promotion
Product
Day, Month, Quarter, and Year
Customer
Sales Person

14. Data Requirements
Number of accepted products by batch by product by machines by day
Number of rejected products by batch by product by machines by day
Elapsed time for molding and hardening by product by machine by day
Elapsed time for painting and curing by curing type by product by machine by day
Product rolls up into product subtype, which rolls up into product type
Machine rolls up into machine type, which rolls up into country
Day rolls up into month, which rolls up into quarter, which rolls up into year
The information should be able to be filtered by machine manufacturer and purchase date of the machine

15. Business Need of Sales
The VP of sales for Max Min, Inc. would like to analyze sales information. This information is collected by three OLTP systems: the Order Processing System, the Point of Sale (POS) system, and the MaxMin.com Online system.
To analyze the following numbers
Dollar value of products sold
Number of products sold
Sales tax charged on product sold
Shipping charged on product sold
These number should be viewable by: store, sales promotion, product, time, customer, sales person

16. Snowflake Schema of the Data Mart
Manufacturingfact
DimBatch
DimMachine
DimProduct
DimMachineType
DimPlant
DimProductSubType
DimMaterial
DimCountry
DimProductType

17. Data Warehousing with Microsoft SQL Server 2008
ISQS 6339, Data Mgmt & BI, Zhangxi Lin

18. Unified Dimensional Model (UDM)
A SQL Server 2008 technology
A UDM is a structure that sits over the top of a data mart and looks exactly like an OLAP system to the end user.
Advantages
No need for a data mart.
Can be built over one or more OLTP systems.
Mixed data mart and OLTP system data
Can include data from database from other vendors and XML- formatted data
Allows OLAP cubes to be built directly on top of transactional data
Low latency
Ease of creation and maintenance
Features
Data sources
Data views
Proactive caching for preprocessed aggregates
ISQS 6339, Data Mgmt & BI, Zhangxi Lin

19. Microsoft BI Toolset Relational engine (RDBMS)
T-SQL
.NET Framework Command Language Runtime (CLR)
SQL Server Integration Services (SSIS) – ETL
Data Transformation Pipeline (DTP)
Data Transformation Runtime (DTR)
SQL Server Analysis Service (SSAS) – queries, ad hoc use, OLAP, data mining
Multi-Dimensional eXpressions (MDX) – a scripting language for data retrieval from dimensional database
Dimension design
Cube design
Data mining
SQL Server Reporting Services (SSRS) – ad hoc query, report building
Microsoft Visual Studio .NET is the fundamental tool for application development
Design Facts, Dimensions and Transformation/Load Processes 3’46”

20. Structure and Components of Business Intelligence
MS SQL Server 2008
SSMS
SSIS
SSAS
BIDS
SSRS
SAS EG
SAS EM

21. OLAP Architecture ROLAP – Relational OLAP
Stores cube structure in a multidimensional database.
Measures are left in the relational data mart.
The preprocessed aggregates are also stored in a relational database table.
Multidimensional OLAP (MOLAP)
Cube structure and preprocessed aggregates - multidimensional database
A copy of measures is also access in multidimensional database
Faster but takes extra time to copy measures - latency
Hybrid OLAP (HOLAP)
Measures is stored in relational database

22. Disadvantages of OLAP Complexity to administer Requires data mart
Latency
Read-only

23. Understanding the Cube Designer Tabs
Cube Structure: Use this tab to modify the architecture of a cube.
Dimension Usage: Use this tab to define the relationships between dimensions and measure groups, and the granularity of each dimension within each measure group.
Calculations: Use this tab to examine calculations that are defined for the cube, to define new calculations for the whole cube or for a subcube, to reorder existing calculations, and to debug calculations step by step by using breakpoints.
KPIs: Use this tab to create, edit, and modify the Key Performance Indicators (KPIs) in a cube.
Actions: Use this tab to create or modify drillthrough, reporting, and other actions for the selected cube..
Partitions: Use this tab to create and manage the partitions for a cube. Partitions let you store sections of a cube in different locations with different properties, such as aggregation definitions.
Perspectives: Use this tab to create and manage the perspectives in a cube. A perspective is a defined subset of a cube, and is used to reduce the perceived complexity of a cube to the business user.
Translations: Use this tab to create and manage translated names for cube objects, such as month or product names.
Browser: Use this tab to view data in the cube.
ISQS 6339, Data Mgmt & BI, Zhangxi Lin

24. Types of Dimension

25. Types of dimension There are 7 types of frequently referred dimensions
Conformed dimensions
Junk dimensions
Role playing dimensions
Slowly changing dimensions (SCD)
Aggregate dimensions
Degenerate dimensions
Many-to-many or multivalued dimensions
For more information about types of dimension, check The Microsoft Data Warehouse Toolkit, Joy Mundy and Warren Thornthwaite, Wiley, 2006

26. Conformed Dimensions
A set of data attributes that have been physically implemented in multiple database tables using the same structure, attributes, domain values, definitions and concepts in each implementation.
Dimension tables are not conformed if the attributes are labeled differently or contain different values.
Conformed dimensions come in several different flavors. At the most basic level, conformed dimensions mean exactly the same thing with every possible fact table to which they are joined.
E.g. The date dimension table connected to the sales facts and the one connected to the inventory facts.

27. Conformed Dimensions
Dimensions are conformed when they are either exactly the same (including keys) or one is a perfect subset of the other. Most important, the row headers produced in the answer sets from two different conformed dimensions must be able to match perfectly.
Conformed dimensions are either identical or strict mathematical subsets of the most granular, detailed dimension.

28. Junk Dimensions Also called miscellaneous or mystery dimensions
They are miscellaneous attributes that don’t belong to any existing dimension.
Typically flags or indictors that describe or categorize the transaction in some way.
Contents are often important
Four alternatives for dealing with them
Leave them in the fact table
Create a separate dimension for each attribute
Omit them
Group them into a single junk dimension 28

29. Degenerate Dimensions
A degenerate dimension is a dimension key in the fact table that does not have its own dimension table, because all the interesting attributes have been placed in analytic dimensions.
Features
No description of its own
No joining to an actual dimension table
No attributes
Example: transaction ID 29

30. Junk Dimension Example

31. Role-playing dimensions
A table with multiple valid relationships between itself and a fact table is known as a role-playing dimension.
For instance, a “Time" dimension can be used for “Order Day", as well as “Ship Date", or “Close Day".

32. Slowly Changing Dimensions
The dimensions that have changeable attribute values are slowly changing dimensions (SCDs)
The attribute values of SCD may change over time, which are critical to understand the dynamics of the business. The ability to track the changes of facts over time is critical to a DW/BI system.
Examples
Employees changed their departments
Home moving (16.8% American moved per year) – zip code changes possible.
More information 32

33. Three Types of SCD
Type 1 SCD overwrites the existing attribute value with a new value. You don’t care about keeping track of historical values
Type 2 SCD change tracking – ETL process creates a new row in the dimension table to capture the new values of the changed item
Type 3 SCD – Similar to Type 2 SCD but only track current state and the original state; two additional attribute: SCD Start Date, SCD Initial Value

34. Aggregate Dimensions
Situation: data at different levels of granularities
Two resolutions
Removing a dimension
Rolling up a dimension’s hierarchy and provide a new, shrunken dimension at the aggregate level
In the following case, the number of possible aggregates is the number of levels in each hierarchy of each dimension multiplied together. 34

35. Many-to-many or Multivalued Dimensions
Relationship between a dimension table and fact table is called one-to-many: one row in the dimension table may join to many rows in the fact table.
Many-to-many or Multivalued Dimensions are referred to as there are more than one row in a dimension table joining to multiple rows in a fact table
Bridge table supports many-to-many relationship:
fact-dimension
dimension-dimension. 35

36. Many-to-many or Multivalued Dimensions
 A dimensional model for a sales fact that captures multiple sales reasons

37. Exercise 2

38. Exercise 2 – Creating a data mart with SSMS
Learning Objectives
How to design a dimensional model
How to create a data mart with SSMS
How to create a cube for a data mart.
Tasks
Manually create the fact table and DimProduct table using SSMS (see the detailed information from file DW_MMM.PDF in the shared directory under \Docs)
Import remaining tables from oredb.lin.mmm.empty
Define the primary keys of tables and the relationships among them
Create a cube
The primary key of the fact table is composed of three foreign keys plus one time dimension key: ProductCode, BatchNumber, MachineNumber, and DateOfManufacture.
Deliverable:
The printout of the screenshot of the cube structure and the success of the deployment

39. Hints for Deploying the OLAP Cube
Due to the security restrictions, you need to:
Double click the entry in Data Source
Type in your eRaider login information in the Impersonation Information panel
Change the server to OREDB 39

40. The screenshot of impersonation information

41. The properties of the project
After this step you can proceed to deploy the cube

42. Surrogate Key
A natural key is a value that has meaning to the user, but ought to be unique for every row. A good example of a natural key would be a license plate number for a car.
A surrogate key is an artificial value that has no meaning to the user, but is guaranteed to be unique by the database itself.
Surrogate keys are created when doing data warehousing. They are new from the keys in original database
They are also called meaningless keys, substitute keys, non-natural keys, artificial keys
Specifically, surrogate keys are used in slowly changed dimensions (SCD) management 42

43. Benefits of surrogate keys
Protect the DW/BI system from changes in the source system
Allow the DW/BI system to integrate data from multiple source system
Enable developers to add rows to dimensions that do not exist in the source system
Provide the means for tacking changes in dimension
Are efficient in the relational database and analysis services

44. Heterogeneous Products
Several products with differentiated attributes
Problem: sharing one dimension or use different dimension?
Resolutions
One family-oriented dimension with core fact and product tables plus specific information for each line of product 44

45. EXERCISE 3

46. Exercise 3 – Creating a data mart with SSAS
Learning Objectives
Learn an alternative way to create a data mart
How to deploy a data mart
Tasks:
Create data mart MaxMinSales2014.<lastname> with BIDS from a cube template
Deploy the data mart
Deliverable:
The printout of the screenshot of the cube structure and the success of the deployment
Reference: DW pp Also check file MaxMinSalesDM.pdf in \Docs of the shared network drive.
View videos:
Create data mart with BIDS 8’34”, Add in more attributes to the database 5’42”, Cube Deployment 3’11”

49. Cube structure

50. Data Mart Tables