VMBICH2.ppt

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VMBICH2.ppt

  1. 1. Chapter 2. Bridging the Analysis Gap 商業智慧 資訊管理系助理教授 電子商務研究中心主任 創新育成中心主任 閔庭祥
  2. 2. 概述 <ul><li>問題的產生 </li></ul><ul><ul><li>企業所需的資訊往往與所蒐集的資料有所落差( GAP ) </li></ul></ul><ul><li>解決方案 </li></ul><ul><ul><li>組織何種資訊將有助於企業分析 </li></ul></ul><ul><ul><li>瞭解電腦系統如何將原始資料轉換為資訊 </li></ul></ul>
  3. 3. 概述 <ul><li>Multidimensional analysis( 多面向分析 ) </li></ul><ul><li>Difining the differences between operational systems and BI systems </li></ul><ul><ul><li>Operation systems : Collect company’s raw data </li></ul></ul><ul><ul><li>BI systems : transform raw data into useful information </li></ul></ul>
  4. 4. Multidimensional Analysis <ul><li>A useful approach for viewing information that allow you to perform flexible and powerful business analysis </li></ul><ul><li>Almost always reveals new and interesting information compared to isolated, single-dimension data lists </li></ul><ul><li>Dimensions:distinct categorizations </li></ul><ul><li>EX:P30 </li></ul><ul><ul><li>Fruit by time,market,product…… </li></ul></ul>
  5. 5. Operation Systems <ul><li>Operation Database : support the day-to-day operations of the company(EX:customer order) </li></ul><ul><li>The data is not necessarily readily available for business analysis </li></ul><ul><li>These databases are structured for the purposes of running the day-to-day business by processing transactions ,not for effective business analysis </li></ul><ul><li>Basic functionality :gather,update store,retrieve and archive data </li></ul><ul><li>Database structure called a relational database management system(RDBMS) </li></ul>
  6. 6. OLTP Systems <ul><li>Example :ATM </li></ul><ul><li>Characteristics: </li></ul><ul><ul><li>It processes a transaction </li></ul></ul><ul><ul><li>It performs all the elements of the transaction in real time </li></ul></ul><ul><ul><li>It processes many transactions on a continuous basis </li></ul></ul>
  7. 7. OLTP Systems <ul><li>OLTP is designed for managing the raw data of business </li></ul><ul><li>The data can be served up quickly ,it is not very useful for an analysis of the overall business </li></ul><ul><li>OLTP systems are lousy for analysis </li></ul><ul><li>The data resides within multiple,disparately organized,and often old technology systems </li></ul>
  8. 8. Operational Reporting <ul><li>These applications typically include meaningful reporting capabilities,which have value for performing business analysis and rightly part of an overall BI strategy </li></ul><ul><li>Two basic limitations </li></ul><ul><ul><li>They report on only their own internally gathered information without the ability to combine data from other systems </li></ul></ul><ul><ul><li>Operational reporting does not effectively support multidimensional analysis at the speed of thought </li></ul></ul>
  9. 9. BI systems <ul><li>A place where data form many oprational systems(and outside data sources) is pulled together for the purpose of analysis </li></ul><ul><li>BI system that enable delivery of fast and efficient multidimensional analysis </li></ul><ul><li>A specific BI paradigm : online analytical processing(OLAP) </li></ul>
  10. 10. Why OLAP <ul><li>OLAP provides conceptual and intuitive data model(multidimensional analysis) that users who are not necessarily trained as analysts can understand and quickly relate to </li></ul><ul><ul><li>OLAP systems organize the data directly as multidimensinal structures, including easy-to use tools for users to get information </li></ul></ul>
  11. 11. Why OLAP <ul><li>OLAP is also fast for the user </li></ul><ul><ul><li>It is the quick response for getting information </li></ul></ul><ul><ul><li>Fast retrieval times let managers and analysts ask and answer more questions in a concentrated period of time </li></ul></ul>
  12. 12. Why OLAP <ul><li>OLAP systems have robust calculation engines for handing the specialized calculation requirements that a multidimensional structure imposes </li></ul><ul><ul><li>OLAP calculation engines structure the data in a way that allows the business analyst to write simple,straightforward formulas that perform across multiple dimensions with only a few lines of code </li></ul></ul>
  13. 13. OLAP systems structures <ul><li>Dimensions for slice and dice </li></ul><ul><ul><li>Dimension is a categorically consistent view of data </li></ul></ul><ul><ul><li>Data about the members can be compared </li></ul></ul><ul><ul><li>Data from the members can be aggregated to summary members </li></ul></ul><ul><ul><li>Multidimensional data in an OLAP system is typically visualized as a cube storage structure with lots of mini-cubes or cells P39 </li></ul></ul>
  14. 14. OLAP systems structures <ul><li>Hierarchies for Drill Down </li></ul><ul><ul><li>Allow you to organize the data into hierarchies that aggregate the detail to higher and higher levels </li></ul></ul><ul><li>Measures </li></ul>
  15. 15. Hierarchies for drill down <ul><li>Dimension </li></ul><ul><li>Hierarchy </li></ul><ul><ul><li>A ragged hierarchy </li></ul></ul><ul><ul><li>drill down levels are not parallel </li></ul></ul><ul><ul><li>An alternate hierarchy </li></ul></ul><ul><ul><li>The second organization of aggregation levels that Use the same source of bottom-level data </li></ul></ul><ul><li>Member </li></ul><ul><ul><li>The name or label for any member at any level in a hierarchy </li></ul></ul><ul><ul><li>Leaf members </li></ul></ul><ul><li>Family Relationships </li></ul>time 2001 Q1 Q2 Q3 Q4 1 2 3 4
  16. 16. Family Relationships <ul><li>Child </li></ul><ul><ul><li>A member directly subordinate </li></ul></ul><ul><ul><li>to another member( 1  Q1) </li></ul></ul><ul><li>Parent </li></ul><ul><ul><li>A member directly above another member(Q1  1) </li></ul></ul><ul><li>Sibling:the same lever’s member(1--2) </li></ul><ul><li>Descendant </li></ul><ul><ul><li>Any member at any lower level in relation to another(Q and mouth  2001) </li></ul></ul><ul><li>Ancetor(2001 and Q1  1) </li></ul>time 2001 Q1 Q2 Q3 Q4 1 2 3 4
  17. 17. Measure <ul><li>The data in most BI applications and all OLAPsystems is called a measure </li></ul><ul><li>A measure is any quantitative expression </li></ul><ul><li>A measure is what is being analyzed across multiple dimensions </li></ul><ul><li>Four important parameters </li></ul><ul><ul><li>Always a quantity or an expression that yields a quantity </li></ul></ul><ul><ul><li>Take any quantitative format EX:Value Ratio </li></ul></ul><ul><ul><li>Can be derived from any original data source or calculation </li></ul></ul><ul><ul><li>Must have at least one measure to do any type of OLAP </li></ul></ul>
  18. 18. Measure <ul><li>Mesaures in business intelligence are gererally known by different name </li></ul><ul><ul><li>Metric </li></ul></ul><ul><ul><li>Key performance indicator(KPI) </li></ul></ul><ul><ul><li>Benchmark </li></ul></ul><ul><ul><li>Ratio </li></ul></ul>
  19. 19. OLAP Storage Modes <ul><li>Most OLAP systems utilize one or may of the following three storage paradidms to support multidimensional analysis </li></ul><ul><li>Desktop files(DOLAP) </li></ul><ul><ul><li>Data sotred on individyal desktop machines </li></ul></ul><ul><li>Relational databases servers(ROLAP) </li></ul><ul><ul><li>Storing data in a relational database </li></ul></ul><ul><li>Multidimensional databases servers (MOLAP) </li></ul><ul><ul><li>Data is placed into special structures that stored on a central server(s) </li></ul></ul><ul><li>HOLAP </li></ul>
  20. 20. 問題與討論

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