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What is Business Intelligence? Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. Ekta Pardhi
dynamic synthesis, analysis and consolidation of large volumes of multi-dimensional data
normally implemented using specialized multi-dimensional DBMS
a method of visualizing and manipulating data with many inter-relationships
Support common analytical operations such as
slicing and dicing
Online Analytical Processing (OLAP ) It enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user. Time Product Region SQL 2000 Oracle Access SQL 2005 1KEY OLAP
OLAP Applications - multi-dimensional views of data
Core requirement of building a ‘realistic’ business model .
Provides basis for analytical processing through flexible access to corporate data.
The underlying database design that provides the multi-dimensional view of data should treat all dimensions equally.
Dimensions: Product, Region, Time Hierarchical summarization paths Product Region Time Industry Country Year Category Region Quarter Product City Month Week Office Day Conceptual Data Model Multi-dimensional view of Data
Each dimension is described by a set of attributes; the attributes of a dimension may be related via hierarchy of relationships.
Month 1 2 3 4 7 6 5 Product Toothpaste Juice Cola Milk Cream Soap Region W S N
A DW is a subject-oriented, integrated, time-varying, non-volatile collection of data that is used primarily in organizational decision making
Graphically Data Warehouse Subject Oriented Credit Risk Interest Rate Risk Forex Risk ALM Subject Area OLTP Systems Integrated Credit Risk Code: XXXX Interest Rate Risk Code: CXXYY Forex Risk Code: XX/XX.XX Common Code for various source codes ALM Subject Area Non Volatile OLTP U S E R S Data Warehouse U S E R S Read Write Read Time variant Dec 98 1995 1996 1997 1998 OLTP OLAP
Star schema : A fact table in the middle connected to a set of dimension tables
A star schema is a set of tables comprised of a single, central fact table surrounded by de-normalized dimensions. Each dimension is represented in a single table. Star schema implement dimensional data structures with de- normalized dimensions.
Snowflake schema : A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake
A snowflake schema is a set of tables comprised of a single, central fact table surrounded by normalized dimension hierarchies. Each dimension level is represented in a table. Snowflake schema implement dimensional data structures with fully normalized dimensions.
Star Schema – Example 1 OrderNo SalespersonID CustomerNo ProdNo DateKey CityName Quantity TotalPrice Fact Table CityName State Country City DateKey Date Month Year Date ProdNo ProdName ProdDescr Category CategoryDescr UnitPrice QOH Product OrderNo OrderDate Order SalespersonID SalespersonName City Quota Salesperson CustomerNo CustomerName CustomerAddress City Customer
Star Schema – Example 2 Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures time_key day day_of_the_week month quarter year time location_key street city province_or_street country location item_key item_name brand type supplier_type item branch_key branch_name branch_type branch
Snowflake Schema –Example Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures time_key day day_of_the_week month quarter year time location_key street city_key location item_key item_name brand type supplier_key item branch_key branch_name branch_type branch supplier_key supplier_type supplier city_key city province_or_street country city
DW and Data Mart: some other issues Characteristics Data Mart Enterprise DW Time to build Months Years Complexity to build Low to medium High Shared Shared in Dept. Shared across Update Frequently Less Frequently Type of user Subject focused Corporate and top executives