New Developments in Business Intelligence ( Decision Support Systems) BUS 782

Decision supports Systems Components Data management function –Decision Support Database Data warehouse Model management function –Analytical models: Statistical model, management science model Data Mining User interface –Data visualization

New Developments in Decision Support Systems Data visualization: Representing data in graphical/multimedia formats for analysis. –Microsoft Soft Pivot: Data Warehouse –Data Mart: A data warehouse that is limited in scope Data Mining Geological Information System, GIS What-if scenarios

Data Warehouse Data warehouse is a repository of an organization's electronically stored data. A data warehouse houses a standardized, consistent, clean and integrated form of data that: –sourced from various operational systems in use in the organization, –structured in a way to specifically address the reporting and analytic requirements.

Example: Transaction Database Customer Order Product Has 1 M M M CID Cname City OIDODate PID Pname Price Rating SalesPerson Qty

Analyze Sales Data Detailed Business Data Total sales: –by product: Qty*Price of each detail line Sum (Qty*Price) Detailed business data: qty*price Total quantity sold: –By product: Sum(Qty) Detailed business data: Qty

Dimensions for Data Analysis: Factors relevant to the business data Analyze sales by Product Analyze sales related to Customer: –Location: Sales by City –Customer type: Sales by Rating Analyze sales related to Time: –Quarterly, monthly, yearly Sales Analyze sales related to Employee: –Sales by SalesPerson

Data Warehouse Design - Star Schema - Dimension tables –contain descriptions about the subjects of the business such as customers, employees, locations, products, time periods, etc. Fact table –contain detailed business data with links to dimension tables.

Star Schema FactTable LocationCode PeriodCode Rating PID Qty Amount Location Dimension LocationCode State City CustomerRating Dimension Rating Description Product Dimension PID Pname Category Period Dimension PeriodCode Year Quarter Can group by State, City

Define Location Dimension Location: –In the transaction database: City –In the data warehouse we define Location to be State, City San Francisco -> California, San Francisco Los Angeles -> California, Los Angeles –Define Location Code: California, San Francisco -> L1 California, Los Angeles -> L2

Define Period Dimension Period: –In the transaction database: Odate –In the data warehouse we define Period to be: Year, Quarter Odate: 11/2/2003 -> 2003, 4 Odate: 2/28/2003 -> 2003, 1 –Define Period Code: 2003, 4 -> , 1 -> 20031

The ETL Process E T L One, company- wide warehouse Periodic extraction  data is not completely current in warehouse

The ETL Process Capture/Extract Transform –Scrub(data cleansing),derive –Example: City -> LocationCode, State, City OrderDate -> PeriodCode, Year, Quarter Load and Index ETL = Extract, transform, and load

From SalesDB to MyDataWarehouse Extract data from SalesDB: –Create query to get the fact data FactData –Download to MyDataWareHouse Transform: –Transform City to Location –Transform Odate to Period Query FactDataScrubing Load data to FactTable

Performing Analysis Analyze sales: – by Location –By Location and Customer Type –By Location and Period –By Period and Product Pivot Table: –Drill down, roll up, reaggregation

Star schema example Fact table provides statistics for sales broken down by product, period and store dimensions Dimension tables contain descriptions about the subjects of the business

Star schema with sample data

Snowflake Schema FactTable LocationCode PeriodCode Rating PID Qty Amount Location Dimension LocationCode State City CustomerRating Dimension Rating Description Product Dimension PID Pname CategoryID Product Category CategoryID Description Period Dimension PeriodCode Year Quarter Can group by State, City

Data Mining Knowledge discovery using a blend of statistical, Artificial Intelligence, and computer graphics techniques Goals: –Explain observed events or conditions –Explore data for new or unexpected relationships

History in the Development of Data Mining. Evolutionary StepBusiness QuestionEnabling Technologies Characteristics Data Collection(196 0s) "What was my total revenue in the last five years?" Computers, tapes, disks Retrospective, static data delivery Data Access(1980s ) "What were unit sales in New England last March?" Relational databases (RDBMS), Structured Query Language (SQL), ODBC Retrospective, dynamic data delivery at record level Data Warehousing &Decision Support (1990s) "What were unit sales in New England last March? Drill down to Boston." On-line analytic processing (OLAP), multidimensional databases, data warehouses Retrospective, dynamic data delivery at multiple levels Data Mining(Emergi ng Today) "What’s likely to happen to Boston unit sales next month? Why?" Advanced algorithms, multiprocessor computers, massive databases Prospective, proactive information delivery

Typical Data Mining Techniques Statistical regression Decision tree induction Clustering – discover subgroups Affinity – discover things with strong mutual relationships Sequence association – discover cycles of evens and behaviors Rule discovery – search for patterns and correlations

Typical Data Mining Applications Profiling populations –High-value customers, credit risks, credit card fraud Analysis of business trends Target marketing Campaign effectiveness Product affinity –Identifying products that are purchased concurrently Up-selling –Identifying new products and services to sell to a customer based on critical events

Affinity Analysis: Market Basket Analysis Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. The set of items a customer buys is referred to as an itemset, and market basket analysis seeks to find relationships between purchases. Typically the relationship will be in the form of a rule: Example: –IF {beer, no bar meal} THEN {chips}.

Basket Analysis and Cross- Selling For instance, customers are very likely to purchase shampoo and conditioner together, so a retailer would not put both items on promotion at the same time. The promotion of one would likely drive sales of the other. A widely used example of cross selling on the internet with market basket analysis is Amazon.com's use of suggestions of the type: –"Customers who bought book A also bought book B", e.g.

Geological Information System GIS GIS is a computer-based tool for mapping and analyzing things that exist and events that happen on earth. GIS technology integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps.

Data of GIS Geodatabase: –A geodatabase is a database that is in some way referenced to locations on the earth. Longitude, latitude Attribute data: –Attribute data generally defined as additional information, which can then be tied to spatial data. Example: –Google Earth –GeoCode service: l

Scenario A scenario is an assumption about input variables. Excel’s Scenarios is a what-if-analysis tool. A scenario is a set of values that Microsoft Excel saves and can substitute automatically in your worksheet. You can use scenarios to forecast the outcome of a worksheet model. You can create and save different groups of values on a worksheet and then switch to any of these new scenarios to view different results. Data/What If analysis/Scenario

Creating a Scenario –Add scenario Changing cells Resulting cells Demo: benefit.xls