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					    ISQS 6339, Data Management & Business Intelligence
                           Introduction

                                    Zhangxi Lin
                                Texas Tech University




1   ISQS 6339, Data Mgmt & BI
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2   ISQS 6339, Data Mgmt & BI
    Outline
     Definitions of BI
     Categorizations of BI
     BI Trend
     BI tools




3   ISQS 6339, Data Mgmt & BI
    What is Business Intelligence
     A Simple Definition: The applications and technologies transforming
      Business Data into Action

        Business intelligence (BI) is a business management term
           refers to applications and technologies which are used to gather, provide
            access to, and analyze data and information about their company
            operations.
        Business intelligence systems can help companies gain more comprehensive
         knowledge of the factors affecting their business, and help companies to make
         better business decisions.

     YouTube:
        What is BI? – A, 10’
        What is BI? – B, 2’
        Microsoft Business Intelligence Surface Demo 6’34”



4   ISQS 6339, Data Mgmt & BI
    Data, information, and knowledge
     Data – a collection of raw value elements or facts used for calculating,
      reasoning, or measuring.
     Information – the result of collecting and organizing data in a way that
      establishes relationship between data items, which thereby provides context
      and meaning
     Knowledge – the concept of understanding information based on
      recognized patterns in a way that provides insight to information.




5   ISQS 6339, Data Mgmt & BI
    The process of BI
     Data -> information -> knowledge -> actionable plans
     Data -> information: the process of determining what data is to be
      collected and managed and in what context
     Information -> knowledge: The process involving the analytical
      components, such as data warehousing, online analytical processing, data
      quality, data profiling, business rule analysis, and data mining
     Knowledge -> actionable plans: The most important aspect in a BI
      process




6   ISQS 6339, Data Mgmt & BI
    Actionable Knowledge
     An information asset retains its value on if the converted
      knowledge is actionable.
        Need some methods for extracting value from knowledge
        This is not a technical issue but an organizational one – need empowered
         individuals in the organization to take the action
        There is an issue of Return on Investment (ROI)




7   ISQS 6339, Data Mgmt & BI
    BI Problems
     Structured
          Detecting Credit card fraud
          Setting Loan parameters
          Market segmentation/Mass customization
          Deciding Marketing mix
          Customer Churn
          Reducing employee turnover
          Improving Quality/Efficiency
           …
     Unstructured
        Data exploration
        Utilization of resources (stored knowledge) to maximum effectiveness
        …




8   ISQS 6339, Data Mgmt & BI
    BI Applications
     Customer Analytics
       Customer profiling
       Targeted marketing
       Personalization
       Collaborative filtering
       Customer satisfaction
       Customer lifetime value
       Customer loyalty
     Sales Channel Analytics
       Marketing
       Sales performance and pipeline




9   ISQS 6339, Data Mgmt & BI
     BI Applications (2)
      Supply Chain Analytics
        Supplier and vendor management
        Shipping
        Inventory control
        Distribution analysis
      Behavior Analysis
        Purchasing trends
        Web activity
        Fraud and abuse detection
        Customer attrition
        Social network analysis




10   ISQS 6339, Data Mgmt & BI
     Why is BI getting hot?
      Demands from processing explosive information
         MIS/ERP
         Internet
      Gartner Says Business Intelligence Software Market to Reach $3
       Billion in 2009 Gartner's CIO Survey ranked BI as number one technology
       priority for 2006
       London, UK, 7 February 2006 - New license revenue in the worldwide
       business intelligence (BI) software market is poised for constant growth
       through 2009, when the market is projected to reach $3 billion in 2009,
       according to the latest forecasts by Gartner Inc. In 2006, the market is
       estimated to reach 2.5 billion, a six percent increase from 2005.




11   ISQS 6339, Data Mgmt & BI
Explosion of digitally born data
How much is 12 Exabytes?                                   Emerging data sources
                                                                    Medical images: potential 1 EB/year
   1,200,000 Libraries of Congress                                 Video monitors: potential 100 EB/year




                                                        55% in personal PCs
                                                        16% in corporate data warehouses
                                                        Internet only 21 TB
                                                        Email 500x more than Internet / year
Sources:
• http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/execsum.htm,
• The Expanding Digital Universe, IDC white paper, March 2007
          BI Job Description - BI Analyst (1)
        Description: Looking for professionals in Microsoft Business Intelligence and Data Warehousing
         who have a proven track record of success within industry. The position requires a broad range of
         skills and the ability to step in to different roles depending on the size and scope of an engagement
         both internally and at client sites. The qualified candidate would have proven experience developing
         successful Microsoft-based Business Intelligence and Data Warehouse solutions.
        Requirements: * 10+ years of experience developing Business Intelligence solutions with
         Microsoft database, ETL and OLAP technologies (SQL Server, SSIS, Analysis Services)
         * Demonstrated understanding of multi-dimensional database design and architecture.
         * Ability to develop business requirements and translate them into a data warehouse dimensional
         model.
         * Demonstrated ability to develop front-end reporting and analytical solutions that meet the
         business needs.
         * Microsoft SQL Server data modeling and development (10 years)
         * Microsoft SQL Server Analysis Services design and development (5 years)
         * Microsoft SQL Server Integration Services (2 years)
         * Microsoft SQL Server Reporting Services design and development
         * Understanding of Data Warehouse Methodologies, preferably using Kimball Methodology
         * Demonstrated leadership aptitude and ability to work effectively within a team environment


13   ISQS 6339, Data Mgmt & BI
     BI Analyst (2)
      Microsoft SQL Server (BI) Business Intelligence


      SetFocus is seeking professionals with Analyst and/or Data Warehousing backgrounds for
        Business Intelligence consulting positions across the country. Apply Today:
        www.setfocus.com/Apply/defaultbi.aspx
      Successful candidates have had backgrounds as:
        Business Intelligence Analyst, Database Developer, SQL Programmer, Financial Analyst,
        Business Analyst, System Analyst, Software Developer, Dir. of IT, VP of IT and / or
        experience with Cognos, Siebel, SAP, Business Objects, SAS, PeopleSoft, Oracle,
        Microstrategy, Information Builders, ProClarity, CA, or Actuate.




14   ISQS 6339, Data Mgmt & BI
     The Evolution of Business Intelligence
      1st Generation – Traditional analytics (query and reporting)
      2nd Generation – Traditional generation (OLAP, data
       warehousing)
      2.5nd Generation – New traditional generation
      3rd Generation - Advanced analytics
         Rules, predictive analytics and realtime data mining
         Stream analytics




15   ISQS 6339, Data Mgmt & BI
       Business Intelligence Classifications
                                             Stream Analytics*
                                  Real-time, continuous, sequential analysis
                                  (ranging from basic to advanced analytics)
                       * In lieu of stream analytics, “embedded analytics,” although architecturally
                       different, could potentially play the same role
3rd-Generation BI

                           Advanced Analytics/Optimization
                                                      Rules
                                              Predictive Analytics
                                      Real-time and traditional Data Mining


                                  “New Traditional” Analytics
                         “2.5-Gen” Analytics (In-Memory OLAP, Search-Based)



   Source:                                Traditional Analytics
   Bill O’Connell              1st Generation Analytics (Query & Reporting)                            Legacy BI
   IBM, Aug 2007
                           2nd Generation Analytics (OLAP, Data Warehousing)

  16
           ISQS 6339, Data Mgmt & BI
                                                                                                                Example Target Solutions:
                                                                                                                Fraud Detection / Risk

Business Intelligence Use Cases                                                                                 CRM Analytic
                                                                                                                Supply Chain Optimization
                                                                                                                RFID / Spatial Data
 Focus on what is                                     Stream Analytics*                                         Other High-Volume
 happening RIGHT NOW                       Real-time, continuous, sequential analysis
                                           (ranging from basic to advanced analytics)
                                * In lieu of stream analytics, “embedded analytics,” although architecturally
                                different, could potentially play the same role
                                                                                                                Focus on what will
                                                                                                                happen
                                    Advanced Analytics/Optimization
                                                                                                                Analytic applications that
                                                                Rules
                                                                                                                apply statistical
                                                        Predictive Analytics                                    relationships in the form
   Real-Time Threshold                          Real-time and traditional Data Mining                           of RULES


                                                                                                                 Data mining to determine
                                           “New Traditional” Analytics                                           why something
Focus on what did                 “2.5-Gen” Analytics (In-Memory OLAP, Search-Based)                             happened by unearthing
happen                                                                                                           relationships that the
                                                                                                                 end-user may not have
Turning data into                                                                                                known existed.
information is limited by the
relationships which the                            Traditional Analytics
end-user already knows to                 1st
                                            Generation Analytics (Query & Reporting)                                  Source:
look for.                           2nd   Generation Analytics (OLAP, Data Warehousing)                               Bill O’Connell
                                                                                                                      IBM, Aug 2007


  17         ISQS 6339, Data Mgmt & BI
     3rd Generation Business Intelligence

      Raises Traditional Warehousing to new levels  Dynamic Warehousing
      Injects analytical insight into the day to day process of an organization when
         activity is occurring in real time
        Broad, real time, leverage of insight to achieve business optimization
        Moves beyond “what happened” to “why and what should happen next”.
        Requires the marriage of analytical insight with real time business
         processing.
        3rd Gen BI by nature requires a Data Warehouse Platform and MDM system
         to consume analytical insight, not just source data for BI.




18   ISQS 6339, Data Mgmt & BI
 “3rd Generation BI” Attributes
 … from data management perspective
      Near-real time (streaming, change data control, memory resident, etc.)
      Off-line capable
      In-context
      Actionable through predictive/prescriptive stats, optimization and business
         rules
        Search User Interface (UI) as the front end of BI
        Structured + unstructured
        Visual
        For the masses
        Horizontal platform with verticalized solutions
        Can be delivered via a hosted model

19   ISQS 6339, Data Mgmt & BI

				
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