1. Business Intelligence Process Grain of the Fact Table Dr. Chang Liu

2. Business Intelligence Process
Analysis Services Multidimensional Cube Data
Operational
Data Sources (Normalized)
Staging Database
Data Warehouse (Denormalized)
Client
Distribution
Data
Data Mart
Data Mart
* Cubes are normally created as part of a Business Intelligence process.

3. Business Intelligence Process Demo
Create a dimensional model for a BI application
Use Excel as a front end tool to analyze data

4. BI Benefits in Modern Organizations
Improvement of Operational Performance
Improvement in Customer Service
Identification of New Opportunities

5. BI Obstacles/Challenges
BI requires large initial investment
BI requires substantial ongoing costs
BI return-on-investment is difficult to justify
Organizations lack of preparation for BI:
Business events are not consistently defined throughout the enterprise
BI Tools may be difficult to use for certain users

6. BI for Competitive Advantages?
IT and Business together must tackle their data issues by answering the following questions:
Data Relevance – what data is needed to complete on analytics?
Data Sourcing – where can this data be obtained?
Data Quantity – How much data is needed?
Data Quality – How can the data be made more accurate and valuable for analysis?
Data Governance – What rules and processes are needed to manage data from its creation through its retirement?

7. Reading Assignment
Predicts 2014: Business Intelligence and Analytics will remain CIO’s Top Technology Priority
Analytics 3.0

8. BI Tools
Personal BI
Team BI
Organizational BI

9. PowerPivot for EXCEL (Personal BI)

10. Example: Sales Data in DB

11. The Star Schema Years Customers Sales Months SalesMen DayOfWeek
Year _ID
Year
Customers
Customer _ID
Customer_Name
Sales
Customer_ID
Salesman_ID
Year_ID
Month_ID
Day_ID
Amount
Months
Month _ID
Month
SalesMen
Salesman _ID
Salesman_Name
DayOfWeek
Day _ID
Day

12. The Star Schema (2) Periods Customers Sales SalesMen Period_ID Date
Customer _ID
Customer_Name
Sales
Customer_ID
Salesman_ID
Period_ID
Amount
SalesMen
Salesman _ID
Salesman_Name

13. What is a Cube?
A cube can be thought of as a multidimensional pivot or crosstab.
It stores numeric values for all combinations of values of the business dimensions.

14. Grain of the Fact Table
Granularity of Fact Table–what level of detail do you want?
Finer grains  better market basket analysis capability
Finer grain  more dimension tables, more rows in fact table
In Web-based commerce, finest granularity is a click

15. Star schema example
Fact table provides statistics for sales broken down by product, period and store dimensions

16. Size of Fact Table
Depends on the number of dimensions and the grain of the fact table
Number of rows = product of number of possible values for each dimension associated with the fact table
Example: assume the following for Figure 1:
Total rows calculated as follows (assuming only half the products record sales for a given month):

17. Size of Fact Table (2)
The size of the fact table is many times larger than the dimension tables!
Estimate the size (in bytes) of the fact table:
Each of the above 6 fields average about 4 bytes in length
Total Size = ?
The size of the fact table depends on the number of the dimensions and the grain of the fact table.
Suppose we’d like to request the daily totals be accumulated in the fact table (assuming 20% of all products record sales on a given day)
Number of rows in the fact table?

18. Advantages of a Star Schema
The star schema is a denormalized schema
The star schema has several benefits:
Simplified the database structure
Easy to query because there is only one level of joins
Queries run much faster compared to the normalized structure
Easy to maintain
Modeled around business entities

19. Class Exercise – Size of a Fact Table

20. PowerPivot SAP Business Object Explorer Exercises