Business Intelligence
Definition of Business Intelligence <ul><li>“ BI is the cornerstone of a learning organization, one that uses facts to val...
Business Intelligence <ul><li>What it is </li></ul><ul><ul><li>Process for gathering, processing and disseminating decisio...
Evolution of Business Intelligence Running Canned Reports Directly Against Operational DB Running Reports Against Nightly ...
BI Infrastructure Is About Data Data Quality Business Rules ETL Processes Analyzing Data Sources User Training BI Tools an...
DW Foundation
Data Warehouse Lifecycle
DW Bus Matrix
Dimensional Modeling
Demo <ul><li>OLTP </li></ul><ul><li>Physical DW Model </li></ul><ul><li>Facts & Dimensions </li></ul>
Multidimensional Databases
Drilling into Detail
Demo <ul><li>Cubes </li></ul><ul><li>KPIs & Metrics </li></ul>
Data Mining & Predictive Analytics <ul><li>Classification : The act of distributing objects into predefined classes or cat...
Demo <ul><li>Data Mining </li></ul>
Who’s Who <ul><li>Bill Inmon </li></ul><ul><ul><li>“ Father” of Data Warehousing </li></ul></ul><ul><ul><li>Corporate Info...
Vendors <ul><li>RDBMS </li></ul><ul><ul><li>Microsoft SQL Server </li></ul></ul><ul><ul><li>Oracle 10g </li></ul></ul><ul>...
Vendors (Continued) <ul><li>Profiling & Data Quality </li></ul><ul><ul><li>ProfileStage (IBM) </li></ul></ul><ul><ul><li>T...
Presenter Information <ul><li>Karl Lacher </li></ul><ul><ul><li>(612) 998 - 1590 </li></ul></ul><ul><ul><li>[email_address...
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business intelligence

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  • Umbrella term that embodies many different things. It certainly includes static reporting which is the mainstay of many organizations. Beyond that it also includes free form or ad hoc analysis, data mining, and predictive modeling.
  • This graphic depicts the evolution (no pun intended) of information delivery and consumption by organizations. Out to the right would be an extra-terrestrial who would represent the latest developments in business intelligence. These are data mining and predictive analytics. There is momentum to move to more real-time business performance management using scorecards and dashboards. Predictive models help identify significant variables that have a bearing on outcomes.
  • Information infrastructure to enable business intelligence relies on a solid integrated data infrastructure. There are many ways to realize this, from Operational Data Stores to Enterprise Data Warehouses to Federated Data Marts. The users only see what is presented to them which belies the complexity of the processes and infrastructure that enable the presentation layer. Another analogy that Ralph Kimball frequently uses is that of a restaurant. Data Quality is essential to the acceptance and use of a DW.
  • A data warehouse serves as a solid foundation on which to build business intelligence capabilities. An ERP provides some interesting integration advantages and some complexities.
  • Speak to the elements of the various phases of the DW lifecycle.
  • The DW Bus Matrix is built with attention to the business processes that must be measured. Fact tables contain the process measures and the conforming dimensions represent the business participants in the process.
  • Dimensions Types 1, 2, and 3 Facts Transaction Periodic Snapshot Accumulating Snapshot
  • A single rolled up representation of the business process metrics. The overall measure of this business process could be “42”.
  • Add column headers for drill down purposes. These are additional “records” in the pre-aggregated cube. Stress the fact that dimensions are unlimited
  • business intelligence

    1. 1. Business Intelligence
    2. 2. Definition of Business Intelligence <ul><li>“ BI is the cornerstone of a learning organization, one that uses facts to validate intuitions and make steady progress towards achieving strategic objectives.” </li></ul><ul><li>— Wayne W. Eckerson, Director of Research and Services, TDWI </li></ul>
    3. 3. Business Intelligence <ul><li>What it is </li></ul><ul><ul><li>Process for gathering, processing and disseminating decision-making information to stakeholders </li></ul></ul><ul><ul><li>Turning data into information </li></ul></ul><ul><ul><li>Analytics </li></ul></ul><ul><li>What it isn’t </li></ul><ul><ul><li>Only reporting </li></ul></ul><ul><ul><li>Clandestine, Business Espionage </li></ul></ul><ul><ul><li>Oxymoron ??? </li></ul></ul>
    4. 4. Evolution of Business Intelligence Running Canned Reports Directly Against Operational DB Running Reports Against Nightly Copy of Operational DB (Reporting Server) Running Reports Against Real-time Copy of Operational DB (ODS) Composing and Running Ad hoc Reports Against Dimensionally Integrated Data (Relational Data Warehouse) Free Form Analysis Using Dimensionally Integrated and Pre-Aggregated Data (OLAP Data Mart)
    5. 5. BI Infrastructure Is About Data Data Quality Business Rules ETL Processes Analyzing Data Sources User Training BI Tools and Rollout DW Schema
    6. 6. DW Foundation
    7. 7. Data Warehouse Lifecycle
    8. 8. DW Bus Matrix
    9. 9. Dimensional Modeling
    10. 10. Demo <ul><li>OLTP </li></ul><ul><li>Physical DW Model </li></ul><ul><li>Facts & Dimensions </li></ul>
    11. 11. Multidimensional Databases
    12. 12. Drilling into Detail
    13. 13. Demo <ul><li>Cubes </li></ul><ul><li>KPIs & Metrics </li></ul>
    14. 14. Data Mining & Predictive Analytics <ul><li>Classification : The act of distributing objects into predefined classes or categories. </li></ul><ul><li>Estimation : A prediction of the value of an unknown, continuous variable. </li></ul><ul><li>Clustering : Identifying logical groups in which to place similar objects. </li></ul><ul><li>Prediction : Classification, estimation or clustering about a value or behavior which has yet to occur. </li></ul><ul><li>Affinity Analysis : Determine which objects can be expected to co-occur with other objects. </li></ul>
    15. 15. Demo <ul><li>Data Mining </li></ul>
    16. 16. Who’s Who <ul><li>Bill Inmon </li></ul><ul><ul><li>“ Father” of Data Warehousing </li></ul></ul><ul><ul><li>Corporate Information Factory </li></ul></ul><ul><li>Ralph Kimball </li></ul><ul><ul><li>Dimensional Modeling </li></ul></ul><ul><ul><li>www.kimballgroup.com </li></ul></ul>
    17. 17. Vendors <ul><li>RDBMS </li></ul><ul><ul><li>Microsoft SQL Server </li></ul></ul><ul><ul><li>Oracle 10g </li></ul></ul><ul><ul><li>IBM DB2 </li></ul></ul><ul><li>ETL (Extract, Transform, & Load) </li></ul><ul><ul><li>Integration Services (Microsoft) </li></ul></ul><ul><ul><li>Warehouse Builder (Oracle) </li></ul></ul><ul><ul><li>DataStage (IBM) </li></ul></ul><ul><ul><li>Informatica </li></ul></ul>
    18. 18. Vendors (Continued) <ul><li>Profiling & Data Quality </li></ul><ul><ul><li>ProfileStage (IBM) </li></ul></ul><ul><ul><li>Trillium </li></ul></ul><ul><ul><li>DataFlux (SAS) </li></ul></ul><ul><ul><li>First Logic (Business Objects) </li></ul></ul><ul><li>Reporting & Analytics </li></ul><ul><ul><li>Reporting Services & Analysis Services (Microsoft) & ProClarity or Panorama </li></ul></ul><ul><ul><li>Cognos </li></ul></ul><ul><ul><li>Business Objects </li></ul></ul><ul><ul><li>Hyperion </li></ul></ul>
    19. 19. Presenter Information <ul><li>Karl Lacher </li></ul><ul><ul><li>(612) 998 - 1590 </li></ul></ul><ul><ul><li>[email_address] </li></ul></ul><ul><li>Michael Dalton </li></ul><ul><ul><li>(612) 203 – 8548 </li></ul></ul><ul><ul><li>[email_address] </li></ul></ul>

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