1. Introduction to Data Warehouse and Data Mining MIS 2502 Data Analytics
From previous classes, we have learned that data has been valuable resources for businesses. 1

2. Importance of data What organizations do with data?
Transaction Processing
(E)commerce: Amazon.com; PNC Bank
B2B systems: Supply Chain Management
Web Search
Decision Making
Financial reporting
Inventory management
Budget allocation
Customer Relationship Management
Target Marketing
Product Design and Promotions
Fraud Detection

3. Unnormalized data set Patient ID Name Address DOB Doc Appt Date
Location DX
111111
Cindy Marselis
2320 Edge Hill Road
1/11/64
Armstrong
9/1/09 11:00 AM
Alter 2011
Herniated Disc
Flu
9331 Rising Sun Avenue
Morningstar
Allen
11/1/09 10:00 AM
Alter 2012
Psoriasis
222222
Kathryn Marselis
11/3/04
Dershaw
8/1/09 11:00 AM
Speakman 105
Well baby check
Cindy Schwartz
8/11/09 3:00 PM
Alter 105

4. Normalized db - before

5. Normalized db - after

6. Decision Making with Databases
Databases are used for transaction processing
Data from transaction processing is used for tactical decision making
Database provides basic reporting function
But…

7. The Need for Data Analysis
Different managers require different data and data may come from other part of the organization or outside the organization
External and internal forces require tactical and strategic decisions
Search for competitive advantage
Business environments are dynamic
Decision-making cycle time is reduced

8. Some Questions Analysts Need to Answers
Sales analysis:
What are the sales by quarter and geography?
How do sales compare in two different stores in the same state?
Profitability analysis:
Which is the most profitable store in the state CA?
Which product lines are the highest revenue producers this year?
Which products and product lines are the most profitable this quarter?
Sale force analysis
Which salesperson is the best revenue producer this year? Do salesperson X meet his sale target this quarter?

9. From transaction processing to supporting decision making

10. Operational vs. Decision Support Data
Operational data
Relational, normalized database
Optimized to support transactions
Real time updates
DSS
Snapshot of operational data
Summarized
Large amounts of data
Data analyst viewpoint
Timespan
Granularity
Dimensionality

11. Data Warehouse Integrated Centralized
Holds data retrieved from entire organization
Subject-Oriented
Optimized to give answers to diverse questions
Used by all functional areas
Time Variant
Flow of data through time
Projected data
Non-Volatile
Data never removed
Always growing

12. Data Warehouse
Extraction, transformation, and loading (ETL) – a process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse
Data mart – contains a subset of data warehouse information

13. ETL – Extraction, Transformation, Load
data from source systems
Transform: cleanse data for consistency and output exceptions
Apply business rules
Selecting certain columns to load (not null records)
Translating coded values (1, M, male = 0 )
Derive new calculated value (sale_amount = qty * unit_price)
Join data from multiple sources (lookup, merge)
Aggregate (rollup/summarize data)
Transpose/pivot (turning columns into rows)
Data validation.
Load:
data into repository

14. Data in a Data Warehouse
Data for a data warehouse is obtained from a variety of databases
E.g. customer database, transaction database, accounts database
Data in data warehouse is multidimensional

15. Multidimensional Analysis
Cube – common term for the representation of multidimensional information

16. Star Schema Data-modeling technique
Maps multidimensional decision support into relational database
Yield model for multidimensional data analysis while preserving relational structure of operational DB
Four Components:
Facts
Dimensions
Attributes
Attribute hierarchies

17. Simple Star Schema
Figure 13.12

18. Slice and Dice View of Sales
Figure 13.14

19. Star Schema Representation
Facts and dimensions represented by physical tables in data warehouse DB
Fact table related to each dimension table (M:1)
Fact and dimension tables related by foreign keys
Subject to the primary/foreign key constraints

20. Star Schema for Sales
Sales fact table and its four dimensions: location, time, product, and customer .
Allows sales to be aggregated by time, geographic location, product, and by customer.

21. Data Warehouse to Data Marts
Given the large size of a data warehouse, organizations create data marts
Subject oriented data
Subset of data in a data warehouse
Used for focused decision-making

22. Online Analytical Processing (OLAP)
Advanced data analysis environment
Supports decision making, business modeling, and operations research activities
Characteristics of OLAP
Use multidimensional data analysis techniques
Provide advanced database support
Provide easy-to-use end-user interfaces
Support client/server architecture

23. OLAP Client/Server Architecture
Figure 13.6

24. Data Mining
Seeks to discover patterns or relationships within the data
Data mining tools automatically search data for patterns and relationships
Data mining tools
Analyze data
Uncover problems or opportunities
Form computer models based on findings
Predict business behavior with models
Require minimal end-user intervention

25. What Are Data-Mining Tools?

26. Data Mining Process

27. Business Intelligence
AB113 - Information Technology

28. MS SQL 2008 Architecture Relational Model
Dimensional Model/Star Schema

29. Back Room—Data prepared from many sources
Front Room—Information presented

30. Multidimensional Analysis and Data Mining
Differences between databases and data warehouse/data mart?
Data mining – the process of analyzing data to extract information not offered by the raw data alone
To perform data mining users need data-mining tools
Data-mining tool – uses a variety of techniques to find patterns and relationships in large volumes of information and infers rules that predict future behavior and guide decision making
Business intelligence – taking data from multiple sources and turn it into useful and easy to understand information to support decision-making efforts for various kinds of people.