1. ISQS 3358, Business Intelligence Creating Data Marts Zhangxi Lin Texas Tech University 1

2. Why need Data Mart? Data mart complements the centralized data warehousing based on UDM model, for the situations where UDM cannot be used ◦ Legacy databases ◦ Data are from nondatabase sources ◦ No physical connection the centralized data warehouse ◦ Data are not clean 2

3. Data Mart Structures Measures Dimensions and Hierarchies Attributes (or columns) Dimensional modeling – Stars and Snowflakes 3

4. Dimensional Modeling Process High level dimensional model design ◦ Choosing business model ◦ Declaring the grain ◦ Choosing dimensions ◦ Identifying the facts Detailed dimensional model development Dimensional model review and validation ◦ IS ◦ Core users ◦ Business community Final design iteration 4

5. 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 5

6. 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 6

7. 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 7

8. 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 8

9. Snowflake Schema of the Data Mart 9 Manufacturingfact DimProduct DimProductSubType DimProductType DimBatch DimMachine DimMachineType DimMaterial DimPlant DimCountry

10. Exercise 3 – Creating a data mart 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 ◦ Import remaining tables from oredb.lin.mmm.empty ◦ Define the primary keys of tables and the relationships among them ◦ Create a cube ◦ Deploy the cube Deliverable: ◦ A Word file with the screenshot of the star schema and the success of the deployment will be emailed to zhangxi.lin@hotmail.comzhangxi.lin@hotmail.com ◦ The subject of the email is: “ISQS 3358 Exercise 3” 10

11. Hints for Deploying the OLAP Cube Due to the security restrictions, you need to: ◦ Create and save the project in the local drive ◦ When defining the data source, check “Use the service account” in the Impersonation Information panel 11

12. Demonstration ◦ Create data mart MaxMinSalesDM with BIDS from a cube template ◦ Deploy the data mart 12