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slide 1 bi introduction
- 2. . Learning Objectives • In this course, you will learn: • How business intelligence provides a comprehensive business decision support framework • About business intelligence architecture, its evolution, and reporting styles • About the relationship and differences between operational data and decision support data • What a data warehouse is and how to prepare data for one 2
- 3. . Learning Objectives • In this course, you will learn: • What star schemas are and how they are constructed • About data analytics • About online analytical processing (OLAP) • How SQL extensions are used to support OLAP-type data manipulations 3
- 7. . The Nature of Data • Data: a collection of facts • usually obtained as the result of experiences, observations, or experiments • Data may consist of numbers, words, images, … • Data is the lowest level of abstraction (from which information and knowledge are derived) • Data is the source for information and knowledge • Data quality and data integrity à critical to analytics
- 9. . Types of Databases • Single-user database: Supports one user at a time • Desktop database: Runs on PC • Multiuser database: Supports multiple users at the same time • Workgroup databases: Supports a small number of users or a specific department • Enterprise database: Supports many users across many departments 9
- 10. . Types of Databases • Centralized database: Data is located at a single site • Distributed database: Data is distributed across different sites • Cloud database: Created and maintained using cloud data services that provide defined performance measures for the database 10
- 11. . Types of Databases • General-purpose databases: Contains a wide variety of data used in multiple disciplines • Discipline-specific databases: Contains data focused on specific subject areas • Operational database: Designed to support a company’s day-to-day operations 11
- 12. . Types of Databases • Analytical database: Stores historical data and business metrics used exclusively for tactical or strategic decision making • Data warehouse: Stores data in a format optimized for decision support • Online analytical processing (OLAP) • Tools for retrieving, processing, and modeling data from the data warehouse • Business intelligence: Captures and processes business data to generate information that support decision making 12
- 13. . Types of Databases • Unstructured data: It exists in their original state • Structured data: It results from formatting • Structure is applied based on type of processing to be performed • Semistructured data: Processed to some extent • Extensible Markup Language (XML) • Represents data elements in textual format 13
- 14. . Metrics for Analytics Ready Data • Data source reliability • Data content accuracy • Data accessibility • Data security and data privacy • Data richness • Data consistency • Data currency/data timeliness • Data granularity • Data validity and data relevancy
- 15. . A Simple Taxonomy of Data • Data (datum—singular form of data): facts • Structured data • Targeted for computers to process • Numeric versus nominal • Unstructured/textual data • Targeted for humans to process/digest • Semi-structured data? • XML, HTML, Log files, etc. • Data taxonomy…
- 17. . Review Questions • 1. What is data? How does data differ from information and knowledge? • 2. What are the main categories of data? What types of data can we use for BI and • analytics? • 3. Can we use the same data representation for all analytics models? Why, or why not? 17
- 21. .
- 23. . Structural and Data Dependence • Structural dependence: Access to a file is dependent on its own structure • All file system programs are modified to conform to a new file structure • Structural independence: File structure is changed without affecting the application’s ability to access the data 23
- 24. . Structural and Data Dependence • Data dependence • Data access changes when data storage characteristics change • Data independence • Data storage characteristics is changed without affecting the program’s ability to access the data • Practical significance of data dependence is difference between logical and physical format 24
- 25. . Data Redundancy • Unnecessarily storing same data at different places • Islands of information: Scattered data locations • Increases the probability of having different versions of the same data 25
- 26. . Data Redundancy Implications • Poor data security • Data inconsistency • Increased likelihood of data-entry errors when complex entries are made in different files • Data anomaly: Develops when not all of the required changes in the redundant data are made successfully 26
- 29. . Case Study
- 30. . Questions Q1: Is this table normalized? Q2: If not, what anomalies can you discover on this table? Q3: How can you normalize this table?
- 31. . Things we learn today • Needs of data analysis • BI concept and framework • BI components and tools • BI in practices • BI benefits 31
- 32. . The Need for Data Analysis • Managers track daily transactions to evaluate how the business is performing • Strategies should be developed to meet organizational goals using operational databases • Data analysis provides information about short- term tactical evaluations and strategies 32
- 33. . Business Intelligence (BI) • Comprehensive, cohesive, integrated set of tools and processes • Captures, collects, integrates, stores, and analyzes data • Generates and presents information to support business decision making • Allows a business to transform: • Data into information • Information into knowledge • Knowledge into wisdom 33
- 34. . User cases 34
- 39. . Business Intelligence (BI) • Concepts, practices, tools and techniques to help business • Understand its core capabilities • Provide snapshots of the company situation • Identify key opportunities to create a competitive advantage • Provides a framework for • Collecting and storing operational data and aggregating it into decision support data • Analyzing decision support data and presenting generated information to end users to support business decisions • Making business decision which generates more data • Monitoring results to evaluate outcomes and predicting future outcomes with a high degree of accuracy 39
- 41. . 41
- 42. . A Multimedia Exercise in Business Intelligence Slide 1-42 • TUN (TeradataUniversityNetwork.com) • BSI Videos (Business Scenario Investigations) • Analogues to CSI (Crime Scene Investigation) • Go To • www.youtube.com/watch?v=NXEL5F4_aKA • See the • www.slideshare.net/teradata/bsi-how-we-did-it-the-case-of-the- misconnecting-passengers.slides • Discuss the case presented in the video and in the slides
- 46. . Questions of class today • What is business intelligence? Give some recent examples of BI usage, using the Internet for assistance. What BI benefits have companies found? • What components included in BI framework? Give some example of tools provided for BI framework by vendors. • Describe the BI framework. Illustrate the evolution of BI. 46
- 47. . Resource #2 Online Tutorial: http://www.studytonight.com/dbms/overview-of-dbms Reading Material
- 48. .