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  • Moving away from spreadsheets to come with the single version of the “truth” Dimensional Modeling: foundation for understanding the data and how they all fit together; used to build the data warehouses Data Warehouse: includes data marts and are created based on the models created above. Gather all the data together so that it can be shared by all and stored in a consistent manner. OLAP: create cubes used for reporting the information. Instead of 2 dimensions, can include regions, sales, products, and time. Used for ad hoc reports and to provide decision support interfaces. Data Visualization: presenting the data in an insightful, easy to read format, along with KPIs and other indicators to further the analysis. Data Mining: A discovery process, improve goals by using predictions, forecasting trends, based on the stored data. Our list of courses cover these topics: Dimensional modeling Data warehousing I and II Multi-dimensional analysis I and II Data visualization Data mining Mostly theory with some hands on applications
  • Here is an Imports cube, which contains two measures, Packages and Last , and three related dimensions, Route, Source, and Time. Last is the last ship date The smaller alphanumeric values around the cube are the members of the dimensions. Example members are ground, Africa, and 1st quarter. The values within the cube represent the two measures, Packages and Last. The Packages measure represents the number of imported packages, and it aggregates by the Sum function. The Last measure represents the date of receipt, and it aggregates by the Max function. The Route dimension represents the means by which the imports reach their destination. The Source dimension represents the locations where the imports are produced. The Time dimension represents the quarters and halves of a single year. Business users of a cube can determine its measures' values for each member of every dimension. This is possible because the members aggregate measure values. For example, the measure values shown in the preceding illustration aggregate within a standard calendar hierarchy in the Time dimension as follows. In addition to aggregating within a single dimension, measures aggregate for all combinations of members from different dimensions. This allows business users to evaluate measures by members in multiple dimensions simultaneously. For example, if an business user wants to analyze quarterly imports that arrived by air from the Eastern Hemisphere and Western Hemisphere, the business user can issue the appropriate query on the cube to retrieve the following dataset.
  • Mention compressed storage
  • Textbook: The Data Warehouse ETL Toolkit Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data, Kimball, Caserta, Wiley Technology Publishing, ISBN: 0764567578 Topics: Decision Support Systems: history, give examples of how they work without data warehouse Requirements Gathering: who wants to see what, when, how often, from where. Is the data available, must it be calculated? Data Analysis: see the big picture, various types of source data: flat file, relational db, different hardware, different software, owners of data, data definitions ETL Processes & Deliverables Cleaning & Conforming: what does Good data look like? Dimensional schemas Dimension Tables: characteristics of data Fact Tables: measurements required for reports We do not go into implementation
  • BI Analyst: Certificate of Accomplishment BI Developer: Certificate of Achievement
  • Relational DB Analyst: Certificate of Accomplishment Relational DB Developer: Certificate of Achievement
  • To work with the students who are in the program.
  • Transcript

    • 1. Business Intelligence: Effective Decision Making Bellevue College Linda Rumans IT Instructor, Business Division Bellevue College [email_address]
    • 2. Current Status Mountains of Data What do I do??? How do I increase sales???? How do I make my product better??? Business Users
    • 3. Mountains of Data
      • From Operational Systems
          • ERP (Enterprise Resource Planning)
            • Sales/Order
            • Inventory
          • Customer Relationship Management (CRM)
          • Web Sites
            • Orders
            • Click-stream
    • 4. Mountains of Data
      • Organizations have lots of data
      • Data is not in a form that is useful to decision-makers
        • Not easy to review
        • Not informative nor insightful
    • 5. Today’s Information Flow
      • Business in 90’s invested in transactional systems:
        • Supply Chain Management (SCM)
        • Customer Relationship Management (CRM)
        • Enterprise Resource Planning (ERP)
        • Manufacturing Resource Planning (MRP)
        • Finance (budget, forecasting and reporting)
    • 6. Proliferation of Data MRP SCM CRM Finance Operations Sales Finance Procure- ment Silos of data by functional area Transaction Layer Reporting Layer
    • 7. Data from Disparate Sources Region: A Region: B Div 2 Div 1 Sales Sales Sales Sales Silos of data within large organizations Transaction Layer Reporting Layer
    • 8. Business Intelligence
      • Business is now investing in Business Intelligence
      • Business Intelligence is about making effective business decisions
    • 9. 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.
    • 10. 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.
    • 11. BI
      • Involves PEOPLE and Technology
      • Involves using a rational approach to management
      • Involves a continuous cycle of measurement, adjustment & re-measurement
    • 12. The BI Cycle Analysis Insight Action Measurement BI start
    • 13. 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.
    • 14. 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
    • 15. Benefits of BI
      • “ Single Version of the truth”
      • Accurate, timely data available to all levels of the organization
    • 16. 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 …
    • 17. BI Activities
        • BI applications include the activities of:
        • decision support,
        • query and reporting,
        • online analytical processing (OLAP),
        • statistical analysis,
        • forecasting, and
        • data mining.
    • 18. BI Users
      • There are many different users who can benefit from business intelligence
        • Executives
        • Business Decision Makers
        • Information Workers
        • Line Workers
        • Analysts
    • 19. BI Solutions- How to make it happen
      • Two main components:
        • Data Consolidation and Storage
        • Data Retrieval, Analysis and Presentation
    • 20. BI Curriculum
      • Multi-Dimensional Analysis
      • Data Warehousing
      • Data Mining
      • Dimensional Modeling
      • Data Visualization
    • 21. The Problem Mountains of Data Business People GAP How do I increase sales???? How do I make my product better??? How do I retain customers?
    • 22. 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
    • 23. Data Storage Structures
      • Multi-Dimensional Databases
          • Cubes
    • 24. 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: Product/Customer/Region/Time
        • These are the “ By’s
        • “ What were our Customer Sales by Product Line by Region by Quarter for the past two years ?”.
    • 25. Logical Structure
    • 26. Multi-Dimensional Databases (Cubes) ODS ODS ODS Data Warehouse Multi-Dimensional Database (Cube) * ODS = Operational Data Store Relational Database Programs Business Intelligence Programs
    • 27. Multi-Dimensional Databases Multi-Dimensional Database (Cube)
    • 28. Software Applications Multi-Dimensional Database (Cube) Business Person Business Person Business Person Reporting Applications Analytic Applications Score Cards Dashboards
    • 29. Analytics
      • 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
    • 30.  
    • 31.  
    • 32. Analytic Application
    • 33. Summary
      • Students learn to:
        • Create multi-dimensional databases
        • Create professional quality reports
        • Use analytics to provide in-depth data analysis
    • 34. Data Warehousing Designing a Data Warehouse
    • 35. Data Warehouse Topics
      • Decision Support Systems
        • history
      • Requirements Gathering
        • Where data located, owners, definition, how often updated
      • Data Analysis
        • Determine for table structures
    • 36. Data Warehouse
      • ETL Processes & Deliverables
        • Cleaning & Conforming
          • Valid, missing
          • Address, gender
        • Schemas
          • Dimension Tables
          • Fact Tables
    • 37. Data Consolidation & Storage
      • Operations and financial information is shared across the organization from same core data
      MRP CRM SCM Finance Transaction Layer Shared Data Layer Data Warehouse Customers Sales Procurement Suppliers Operations Finance Shared Reporting
    • 38. Data Warehouses ODS* ODS ODS Data Warehouse Multi-Dimensional Database (Cube) *ODS = Operational Data Store
    • 39. 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
    • 40. Transforming Data
      • Data must be transformed for consistency and meaning
        • 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)
    • 41. 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
    • 42. 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 cost!!!!
    • 43. Data Mining
    • 44. Data Mining
      • The process of identifying patterns in data
      • Goes beyond simple querying of the database
      • Goes beyond multi-dimensional database queries as well
    • 45. 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.
    • 46. 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.
    • 47. Data Mining Applications
      • Fraud detection
      • Targeted Marketing
      • Risk Management
      • Business Analysis
    • 48. Origins of Data Mining
      • Mathematics
        • Statistics
        • Numerical Analysis
      • Artificial Intelligence/Machine Learning
      • Computer Science
        • Data Storage and Manipulation
    • 49. 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.
    • 50. How does Data Mining work? What-Cha-Ma-Call-Its NOT What-Cha-Ma-Call-Its
    • 51. How does Data Mining work? Which of these are What-Cha-Ma-Call-Its?
    • 52. Data Mining Process List of Customers: -some bicycle buyers -some not Data Mining Software Model List of Prospective Buyers Model List of Likely Buyers
    • 53. Overview of Mining Strategies Note: This representation is over-simplified and data mining strategies are continually being invented.
    • 54. More on our Curriculum
    • 55. Skills
      • Written communication
      • Problem Solving
        • Analytical
        • Troubleshooting
      • Software
        • Microsoft SQL Server Management Studio
        • SQL Server BI Development Studio
        • SQL Server Reporting Services
        • Pro Clarity
    • 56. Delivery Methods
      • Online: Distance Education, reaches wider market
      • 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
    • 57. Delivery Methods
      • Use of Camtasia for
        • Software demonstrations
        • PowerPoint lectures
      • Pod casting
    • 58. Certificates
      • 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 classes)
        • Dimensional modeling, data visualization, multi-dimensional II, data warehousing II (more programming with SQL Server)
      • Web site:
    • 59. Certificates
      • Relational Database Analyst (6 classes)
        • SA & D, programming, reporting, spreadsheets, db theory
        • 2 quarters full-time/ 3 quarters part-time
      • Relational Database Developer (3 additional classes)
        • Programming, SQL, group processes
      • Web site:
    • 60. Jobs
      • Business Analyst
      • Data Analyst
      • Functional Analyst
      • Marketing Analyst
    • 61. Jobs
      • Report Developer
      • Data Modeler
      • ETL Developer
      • Data Architect
      • Data Warehouse Designer
      • Data Warehouse Developer
      • Data Warehouse Administrator
      • Database Administrator
    • 62. Jobs
      • Business Intelligence Consultant
      • Business Intelligence Developer
      • Business Intelligence Analyst
      • Business Intelligence Project Team Member
    • 63. Jobs
      • 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
    • 64. Questions?