New Developments in Business Intelligence ( Decision Support Systems) BUS 782.
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Transcript of New Developments in Business Intelligence ( Decision Support Systems) BUS 782.
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New Developments in Business Intelligence
( Decision Support Systems)
BUS 782
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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
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New Developments in Decision Support Systems
• Data visualization: Representing data in graphical/multimedia formats for analysis.– Microsoft Soft Pivot: http://www.getpivot.com/
• Data Warehouse– Data Mart: A data warehouse that is limited in scope
• Data Mining
• Geological Information System, GIS
• What-if scenarios
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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.
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Example:Transaction Database
Customer Order
Product
Has
Has
1 M
M
M
CID Cname City OID ODate
PIDPname
Price
RatingSalesPerson
Qty
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Analyze Sales DataDetailed 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
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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
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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.
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Star Schema
FactTableLocationCodePeriodCode
RatingPIDQty
Amount
LocationDimension
LocationCodeStateCity
CustomerRatingDimension
RatingDescription
ProductDimension
PIDPname
Category
PeriodDimensionPeriodCode
YearQuarter
Can group by State, City
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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
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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 -> 20034• 2003, 1 -> 20031
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The ETL Process
E
T
LOne, company-wide warehouse
Periodic extraction data is not completely current in warehouse
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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
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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
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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
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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
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Star schema with sample data
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Snowflake Schema
FactTableLocationCodePeriodCode
RatingPIDQty
Amount
LocationDimension
LocationCodeStateCity
CustomerRatingDimension
RatingDescription
ProductDimension
PIDPname
CategoryID
ProductCategory
CategoryIDDescription
PeriodDimensionPeriodCode
YearQuarter
Can group by State, City
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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
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History in the Development of Data Mining.Evolutionary Step Business Question Enabling
TechnologiesCharacteristics
Data Collection(1960s)
"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(Emerging Today)
"What’s likely to happen to Boston unit sales next month? Why?"
Advanced algorithms, multiprocessor computers, massive databases
Prospective, proactive information delivery
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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
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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
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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}.
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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.
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Geological Information SystemGIS
• 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.
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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:
http://developer.yahoo.com/maps/rest/V1/geocode.html
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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
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Creating a Scenario
– Add scenario• Changing cells• Resulting cells
• Demo: benefit.xls