1. 1 Chapter 7 Enterprise Databases, Data Warehouses, and Business Intelligence

2. 2 Objectives  Advantages of shared databases.  Compare relational vs. object oriented databases.  Describe the differences between schemas, views, and indexes.  Shared vs. distributed databases.  Data warehouses and Business Intelligence.

3. 3 Enterprise Data – Scaling Up  Database: A collection of data and information describing items of interest to an organization.  Enterprise Database: A collection of data designed to be shared by many users within an organization.

4. 4 Both Actual Data and Schema are Shared

5. 5 Database Mangement  The Functions of Database Management: Integrating Databases Reducing Redundancy Sharing Information Maintaining Integrity Enabling Database Evolution

6. 6 DBMS in Systems

7. 7 Enterprise Data Model  Enterprise Data Model/Entity Relationship: A graphical representation of the items (the entities) of interest about which data is captured and stored in the database.

8. 8 Schema  Schema: The structure of a database.  Schema for Relational Database Relational Database: A database in which the data are structured in a table format consisting of rows and columns.

9. 9 Relational Schema

10. 10 Object Orientation  Schema for Object-Oriented Database Object-oriented Database: A database that stores data and information about objects. Object: A component that contains data about itself and how it is to be processed. Action/Method: An instruction that tells a database how to process an object to produce specific information.

11. 11 Object Oriented Schema

12. 12 User views  View: A subset of one or more databases, created either by extracting copies of records from a database or by merging copies of records from multiple databases.

13. 13 Enterprise Database Structures Views (Continued)

14. 14 Indexing  Index: A data file that contains identifying information about each record and its location in storage.  Record Key: In a database, a designated field used to distinguish one record from another.

15. 15 Enterprise Database Structures Indexes (Continued)

16. 16 Integration  Web-based Integration: Makes data from enterprise databases available to users connecting through the Internet (including enterprise intranets and extranets).

17. 17 Databases and the Internet

18. 18 Distributed Databases  Shared Database: A database shared among many users and applications.  Distributed Database: A database that resides in more than one system in a distributed network. Each component of the database can be retrieved from any node in the network.

19. 19 Partitioning and Replication  Partitioning: A method of database distribution in which different portions of the database reside at different nodes in the network. Vertical Horizontal  Replication: A method of database distribution in which one database contains data that are included in another database. Real time Cascade Batch

20. 20 Distribution Strategies  Geographic Distribution Strategy: A database distribution strategy in which the database is located in a region where the data and information are used most frequently.  Functional Distribution Strategy: A database distribution strategy in which the database is distributed according to business functions.

21. 21 Designing a Distributed Database  Database Directory: The component of a shared database that keeps track of data and information.  Other Design Factors Storage Costs Processing Costs Communication Costs Retrieval and Processing Reliability Frequency of Updates and Queries

22. 22 Data Warehouses and OLAP  Data Warehouse: A large data store, designed from inquiries, that combines details of both current and historical operations, usually drawn from a number of sources.  Online Analytical Processing (OLAP): Database processing that selectively extracts data from different points of view.

23. 23 Comparison of Enterprise Databases and Data Warehouses

24. 24 Data Warehouse

25. 25 Data Warehouses and OLAP Definition  Data Mining: Uses software designed to detect information hidden in the data.  Data Marts: Processed to focus on a specific area of activities or isolated scientific or commercial processes.

26. Business Intelligence: Supporting Managerial Decision Making

27. Issues  MIS: Reporting  Data-Driven DSS: Business Intelligence  Model- -Driven DSS: Models and Modeling  GDSS and ESS  Case Study: MasterCard

28. 28 Decision Levels and Application Systems Business Operations Tactical Management Strategic Mgt. DSS MIS Transaction Processing From R.N. Anthony, Planning and Control Systems: A Framework for Analysis. Harvard University (1965)

29. 29 MIS vs. DSS (Data Driven and Model Driven)  MIS: Provides reports based on routine flow of data. Assists in general control of the organization. Exception reports used to reduce volume and focus on items that require management attention.

30. 30 MIS Reports  Paper or online  Can includes text, graphs, or both.  Batch vs. Real-time  Fixed vs. Ad Hoc (a continuum)  Summary vs. Detail  Types include: Exception Trend Validation (such as Trial Balance)

31. 31 Data-Driven DSS (a.k.a. Business Intelligence)  Also known as. Query/inquiry, Data Mining, and OLAP (Online Analytical Processing).  Goal is to determine where we are or where we’ve been.  “Business Intelligence” has emerged as common term.  Sometimes also called Datamining, though this generally implies using statistical techniques such as correlation analysis and clustering to find patterns and relationships in large databases.

32. 32 Goals of BI  Enables users to identify and understand the key trends and events driving their businesses.  Allows employees to sift through and analyze large amounts of data that the company makes available for them.  Helps business managers at all levels make better decisions quicker.

33. 33 What is BI Used For?  To perform trend analyses on product, sales, event (i.e. promotions and advertising campaigns) and financial information. Sales per office or region and then drill down to lower level details to uncover what is driving the trends.  It is also used for exception-reporting and for budgeting, planning, and forecasting.

34. 34 BI Tool Capabilities  Support large volumes of data and an unlimited number of dimensions  Can aggregate data Sums, averages, maximums, minimums, percentage of total, and user-defined functions or rules.  Can contain analytical engines that perform computations. Rankings, ratios, or variances (i.e., This-year-to-last- year or actual-versus-budget comparisons), Revenue or expense allocations, Currency conversions, etc.

35. Most BI Tools also include graphics capabilities

36. 36 Data Sources for BI Include  Relational Data Bases (including Data Warehouses)  Data Marts Star Schemas Facts and Dimensions  Cubes (Facts and Dimensions)

37. 37 Data Warehouse OLTP Database 3NF tables Operations data Predefined reports Data warehouse Star configuration Daily data transfer Interactive data analysis Flat files

38. 38 Data Warehouses Contain Data from Many Sources (a.k.a. Domains)

39. 39 Cube Example: Sales Information Sales information can be represented in the cube below. You will be able to derive many measures based on the dimensions below Region Department Time

40. 40 Some Leading BI Vendors  Enterprise Query/Reporting (RDBMS Based): Actuate Crystal Reports Information Builders / WebFocus  OLAP (Data Mart and Cube Based): MicroStrategy Hyperion Oracle Business Objects (also includes reporting tools) Cognos (also includes reporting tools)

41. 41 Demo Sites  Cognos PowerPlay: http://naade02.msfc.nasa.gov/workforce/ind ex.html http://naade02.msfc.nasa.gov/workforce/ind ex.html http://www.cognosdemo.com/temple/  Information Builders Web FOCUS: www.informationbuilders.com/test_drive/inde x.html www.informationbuilders.com/test_drive/inde x.html www.nyc.gov/html/doh/html/rii/index.html

42. 42 For more information...  Bill Inmon: http://www.billinmon.com/  Ralph Kimball: http://www.rkimball.com/  Data Management Review: http://www.dmreview.com/  Data Warehouse: http://www.datawarehouse.com

43. DSS: Decision Support System Models salesrevenueprofitprior 154204.545.3235.72 163217.853.2437.23 161220.457.1732.78 173268.361.9347.68 143195.232.3841.25 181294.783.1967.52 Sales and Revenue 1994 JanFebMarAprMayJun 0 50 100 150 200 250 300 Legend Sales Revenue Profit Prior Database Model Output data to analyze results

44. Optimization Maximum Model: defined by the data points or equation Control variables Goal or output variables Why Build Models?  Understanding the Process  Optimization  Prediction  Simulation or "What If" Scenarios

45. Prediction 0 5 10 15 20 25 Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2 Time/quarters Output Moving Average Trend/Forecast Economic/ regression Forecast

46. 46 Marketing Sales Forecast forecast Note the fourth quarter sales jump. The forecast should pick up this cycle.

47. 47 Time Series Components time sales Dec 1. Trend 2. Seasonal 3. Cycle 4. Random Trend Seasonal A cycle is similar to the seasonal pattern, but covers a time period longer than a year.  Collect data over time  Identify trends  Identify seasonal effects  Forecast based on patterns

48. 48 Forecasting Uses  Marketing Future sales Consumer preferences/trends Sales strategies  Finance Interest rates Cash flows Financial market conditions  HRM Labor costs Absenteeism Turnover  Strategy Rivals’ actions Technological change Market conditions

49. Simulation Goal or output variables Results from altering internal rules

50. 50 Group Decision Support Systems (GDSS)  Interactive computer-based system.  Facilitates solution to unstructured problems.  Set of decision makers working together as a group.

51. EIS: Enterprise Information System (aka Executive Information System and Executive Support System)  Easy access to data  Graphical interface  Non-intrusive  Drill-down capabilities EIS Software from Lightship highlights ease- of-use GUI for data look- up.

52. 52 Digital Dashboard http://www.microsoft.com/business/casestudies/dd/honeywell.asp Stock market Exceptions Plant or management variables Equipment details Products Quality control Plant schedule