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Business Intelligence - PowerPoint

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									  Business Intelligence:
Effective Decision Making

                Bellevue College

           Linda Rumans
           IT Instructor, Business Division
           Bellevue College
   Current Status

                            What do I
                            How do I increase

                              How do I make my
                               product better???

                    Business Users

Mountains of Data
Mountains of Data

           From Operational Systems
                  ERP (Enterprise Resource Planning)
                    – Sales/Order
                    – Inventory
                  Customer Relationship Management
                  Web Sites
                    – Orders
                    – Click-stream
                  …
Mountains of Data

   Organizations have lots of data
   Data is not in a form that is useful to
    –   Not easy to review
    –   Not informative nor insightful
Today’s Information Flow

      Business in 90‟s invested in transactional
       –   Supply Chain Management (SCM)
       –   Customer Relationship Management (CRM)
       –   Enterprise Resource Planning (ERP)
       –   Manufacturing Resource Planning (MRP)
       –   Finance (budget, forecasting and reporting)
Proliferation of Data

                Sales   Procure-   Operations   Finance

               CRM        SCM        MRP        Finance

                  Silos of data by functional area
Data from Disparate Sources

                 Sales   Sales       Sales       Sales



                 Div 2   Region: B   Region: A    Div 1

                   Silos of data within large organizations
Business Intelligence

   Business is now investing in Business
   Business Intelligence is about making
    effective business decisions
What is BI?

The process by which an organization
 manages large amounts of data, extracting
  pertinent information, and turning that
  information into knowledge upon which
  actions can be taken.
What is BI?

Business intelligence (BI) is a broad category
  of application programs and technologies for
  gathering, storing, analyzing, and providing
  access to data to help enterprise users make
  better business decisions.

   Involves PEOPLE and Technology
   Involves using a rational approach to
   Involves a continuous cycle of measurement,
    adjustment & re-measurement
The BI Cycle

Reasons for BI

    BI enables organizations to make well
     informed business decisions and gain
     competitive advantage.
    BI enables organizations to use information to
     quickly and constantly respond to changes.
Benefits of BI

   Improved performance based upon timely
    and accurate information
    Elimination of guesswork
    Expedited decision making
    Early visibility of changes:
    –   Customer buying patterns
    –   Supply chain activity
    –   Financial arrangements
Benefits of BI

   “Single Version of the truth”
   Accurate, timely data available to all levels of
    the organization
To Note:

Although we call it Business Intelligence, the
  concepts and techniques are applicable to
  almost any organization including those in
  health care, biotech, education,
  government …
BI Activities

  BI applications include the activities of:
  • decision support,
  • query and reporting,
  • online analytical processing (OLAP),
  • statistical analysis,
  • forecasting, and
  • data mining.
BI Users

   There are many different users who can
    benefit from business intelligence
    –   Executives
    –   Business Decision Makers
    –   Information Workers
    –   Line Workers
    –   Analysts
BI Solutions-
How to make it happen

   Two main components:
    –   Data Consolidation and Storage
    –   Data Retrieval, Analysis and Presentation
BI Curriculum

   Multi-Dimensional Analysis
   Data Warehousing
   Data Mining
   Dimensional Modeling
   Data Visualization
The Problem

How do I retain

How do I increase

How do I make my
 product better???

     Business People
                             Mountains of Data
Bridging the Gap

   Need data storage structures to facilitate fast
    analysis of huge volumes of data

   Need software to provide access to the data,
    allow flexible manipulation, and provide
    meaningful presentation
Data Storage Structures

   Multi-Dimensional Databases
       Cubes
Multi-Dimensional Databases

   Measures
    –   Any quantitative expression
    –   Some are designated as Key Performance Indicators (KPI)
    –   Appropriate to the business process.
   Dimensions
    –   How we describe the measures:
    –   These are the “By‟s
    –   “What were our Customer Sales by Product Line by
        Region by Quarter for the past two years?”.
Logical Structure
Multi-Dimensional Databases (Cubes)

           Multi-Dimensional            Business
           Database (Cube)              Intelligence

            Data Warehouse

     ODS            ODS           ODS    Relational
 * ODS = Operational Data Store
Multi-Dimensional Databases

                      Database (Cube)
Software Applications

Business   Reporting

Business                  Database
Person                    (Cube)

           Score Cards
Person     Dashboards

   Reporting Applications
    –   Limited user interaction
    –   Fulfill a significant portion of an organization‟s
        information needs
   Analytic Applications
    –   Allow users to visualize and explore data
        following their train of thought
    –   Extensive interactivity
Analytic Application

   Students learn to:
    –   Create multi-dimensional databases
    –   Create professional quality reports
    –   Use analytics to provide in-depth data analysis
Data Warehousing

Designing a Data Warehouse
Data Warehouse Topics

   Decision Support Systems
    –   history
   Requirements Gathering
    –   Where data located, owners, definition, how often
   Data Analysis
    –   Determine for table structures
Data Warehouse

   ETL Processes &
    –   Cleaning & Conforming
            Valid, missing
            Address, gender
    –   Schemas
            Dimension Tables
            Fact Tables
Data Consolidation & Storage

Customers Sales Procurement Suppliers Operations Finance


 Shared Data
                       Data Warehouse

            SCM        CRM        MRP       Finance

    Operations and financial information is shared
     across the organization from same core data
Data Warehouses

             Database (Cube)

          Data Warehouse

   ODS*           ODS            ODS

          *ODS = Operational Data Store
How is data consolidated?

   This is difficult!!!!!
    –   Data is often spread across multiple systems,
        stored in different formats, and may even be
        localized for different countries
Transforming Data

   Data must be transformed for consistency and
    –   Transformations may be as simple as copying columns or
        may be incredibly complex
    –   Common transformations include:
            Hard-coded changes („T‟ to 1)
            Looking up values in a table (mapping a customer number
             across disparate systems)
            Inserting dummy records and mapping them to unknowns
             (inserting an „Unknown‟ customer)
Cleansing Data

   Data must be cleansed to be meaningful
    –   All companies have “bad” data in their systems
    –   Data may be missing
    –   Data may be inconsistent
    –   Data may be wrong
Data Warehouses

   ETL (extract, transform and load) processes
    are needed to create data warehouses
    –   This is an arduous and technical process that can
        account for a large percentage of a BI project
Data Mining
Data Mining

   The process of identifying patterns in data

   Goes beyond simple querying of the database

   Goes beyond multi-dimensional database
    queries as well
Data Mining

    Data Mining works for problems like:
     –   Develop a general profile for credit card customers
     –   Differentiate individuals who are poor credit risks
     –   Determine what characteristics differentiate male
         & female investors.
Data Mining vs. Data Query

   Use data query if you already almost know
    what you are looking for.
    Use data mining to find regularities in data
    that are not obvious.
Data Mining Applications

    Fraud detection
    Targeted Marketing
    Risk Management
    Business Analysis
Origins of Data Mining

   Mathematics
    –   Statistics
    –   Numerical Analysis
   Artificial Intelligence/Machine Learning
   Computer Science
    –   Data Storage and Manipulation
How does Data Mining work?

   Uses induction-based learning:

The process of forming general concept
  definitions by observing specific examples of
  concepts to be learned.
 How does Data Mining work?

What-Cha-Ma-Call-Its   NOT What-Cha-Ma-Call-Its
How does Data Mining work?

   Which of these are What-Cha-Ma-Call-Its?
      Data Mining Process

  List of Customers:         Data Mining
   -some bicycle buyers      Software          Model

   -some not

                                           List of Likely Buyers
List of Prospective Buyers   Model
 Overview of Mining Strategies

                                               Data Mining

                         Learning                  Unsupervised

Note: This representation is over-simplified and data mining strategies
are continually being invented.
More on our Curriculum

   Written communication
   Problem Solving
    –   Analytical
    –   Troubleshooting
   Software
    –   Microsoft SQL Server Management Studio
    –   SQL Server BI Development Studio
    –   SQL Server Reporting Services
    –   Pro Clarity
Delivery Methods

   Online: Distance Education, reaches wider
   Telecourse: tremendous effort to create, but
    once created easy to deliver
    –   Televised, DVDs, online for homework, exams
   Hybrid: Meet once a week, the rest online
   On campus: evenings only
Delivery Methods

   Use of Camtasia for
    –   Software demonstrations
    –   PowerPoint lectures
   Pod casting

   Business Intelligence Analyst (5 classes)
    –   Multi-dimensional analysis, data warehousing, data
        mining, statistics, general business
    –   2 quarters full-time/ 3 quarters part-time
   Business Intelligence Developer (4 additional
    –   Dimensional modeling, data visualization, multi-
        dimensional II, data warehousing II (more
        programming with SQL Server)
   Web site:

   Relational Database Analyst (6 classes)
    –   SA & D, programming, reporting, spreadsheets, db
    –   2 quarters full-time/ 3 quarters part-time
   Relational Database Developer (3 additional
    –   Programming, SQL, group processes
   Web site:

   Business Analyst
   Data Analyst
   Functional Analyst
   Marketing Analyst

   Report Developer
   Data Modeler
   ETL Developer
   Data Architect
   Data Warehouse Designer
   Data Warehouse Developer
   Data Warehouse Administrator
   Database Administrator

   Business Intelligence Consultant
   Business Intelligence Developer
   Business Intelligence Analyst
   Business Intelligence Project Team Member

   One of the fastest growing segments of IT

   Less likely to be outsourced

   May exist in business units rather than IT

   Knowledge/understanding of the organization
    is key

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