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

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

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

Normalized db - before

Normalized db - after

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…

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

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?

From transaction processing to supporting decision making

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

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

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

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

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

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

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

Simple Star Schema Figure 13.12

Slice and Dice View of Sales Figure 13.14

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

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.

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

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

OLAP Client/Server Architecture Figure 13.6

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

What Are Data-Mining Tools?

Data Mining Process

Business Intelligence AB113 - Information Technology

MS SQL 2008 Architecture Relational Model Dimensional Model/Star Schema

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

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.