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Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
Business Intelligenc..
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Business Intelligenc..

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  • 1. Bridging the Analysis Gap Reporter : Yuan - Fang Lin , Huel - Ling Jheng Authors : Elizabeth Vitt , Michael Luckevich , Stacia Misner 【 Business Intelligence 】
  • 2. Outline
    • Analysis Gap
    • Bridging analysis gap
    • Multidimensional Analysis
    • Operational Systems
    • OLTP Systems
    • Operational Reporting
    • Business Intelligence System
    • Why OLAP ?
    • OLAP System Structures
  • 3. Analysis Gap
    • To make better decisions faster , Executives and business managers require useful information that is readily available and flexible to analyze .
    • The gap exists between the information that business people need and the mountains of raw data that companies collect .
  • 4. Bridging analysis gap
    • How companies to bridge this gap :
      • To recognize how information can be used for business analysis .
      • To understand how computer systems convert raw data into useful information .
  • 5. Multidimensional Analysis
    • Example : A fruit wholesaler who purchases fruit from farmers and transports and distributes the fruit in four markets.
    • This categorizations are called dimensions .
  • 6. Multidimensional Analysis (Cont.)
    • This process of interacting with multidimensional views of the data - Slicing and Dicing .
  • 7. Operational Systems
    • Operational databases
      • These are structured for the purposes of running the day to day business by processing transactions , not for effective business analysis.
      • Basic functionality : To gather, store, update, retrieve, and archive data.
      • RDBMS (Relational Database Management System)
  • 8. OLTP Systems
    • OLTP (Online Transaction Processing) is designed for managing the raw data of business.
    • Example - Withdrawing cash from ATM.
    • Three characteristics :
      • It processes a transaction .
      • It performs all the elements of the transaction in real time .
      • It processes many transactions on a continuous basis.
  • 9. Operational Reporting
    • Two basic limitations :
      • They report on only their own internally gathered information without the ability to combine data from other systems.
      • Operational reporting does not effectively support multidimensional analysis at the speed of thought.
  • 10. Business Intelligence System
    • A place where data from many operational systems (and outside data sources) is pulled together for the purpose of analysis.
    • BI system that enable delivery of fast and efficient multidimensional analysis .
    • A specific BI paradigm : online analytical processing ( OLAP )
  • 11. Why OLAP ?
    • Multidimensional analysis
      • OLAP provides a conceptual and intuitive data model.
      • Analysts can understand easily and quickly relate to.
      • Being able to see data through multiple dimensions as we call them.
  • 12. Why OLAP ? (Cont.)
    • OLAP is the quick response for getting information back from the OLAP system.
    • OLAP systems have robust calculation engines for handling the specialized calculation requirements.
  • 13. OLAP System Structures
    • Dimensions
    • Hierarchies
    • Measures
  • 14. Dimension for Slice and Dice
    • Dimension is a categorically consistent view of data
    • Data about the members can be compared
    • Data from the members can be aggregated to summary members
    • Multidimensional data in an OLAP system is typically visualized as a cube storage structure with lots of mini-cubes or cells
  • 15. Hierarchies for Drill Down
    • Allow you to organize the data into hierarchies that aggregate the detail to higher and higher levels
  • 16. Family Relationships
    • Child
      • A member directly subordinate to another member( 1  Q1)
    • Parent
      • A member directly above another member(Q1  1)
    • Sibling
      • The same level’s member(1--2)
    • Descendant
      • Any member at any lower level in relation to another(Q and month  2001)
    • Ancetor(2001 and Q1  1)
    time 2001 Q1 Q2 Q3 Q4 1 2 3 4
  • 17. Measure
    • The data in most BI applications and all OLAP systems is called a measure
    • A measure is any quantitative expression
      • EX. amount sales
    • A measure is what is being analyzed across multiple dimensions
      • EX. Unit sales by month,by product
  • 18. Measure
    • Four important parameters
      • Always a quantity or an expression that yields a quantity
      • Take any quantitative format
        • EX: currency value(amount sales)
      • Can be derived from any original data source or calculation
        • EX : aggregation(sum of unit sales)
      • Must have at least one measure to do any type of OLAP
  • 19. Measure
    • Measures in business intelligence are generally known by different name
      • Metric
      • Key performance indicator(KPI)
      • Benchmark
      • Ratio
  • 20. OLAP Storage Modes
    • Desktop files(DOLAP)
      • Data is stored on individual desktop machines
    • Relational databases servers(ROLAP)
      • Storing data in a relational database
    • Multidimensional databases servers(MOLAP)
      • Data is placed into special structures that stored on a central server(s)
    • HOLAP
      • Spread data across both relational and multidimensional databases

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