1. McGraw-Hill/Irwin ©2008 The McGraw-Hill Companies, All Rights Reserved DATABASES AND DATA WAREHOUSES CHAPTER 6

2. 6-2 UNDERSTANDING INFORMATION Information is everywhere in an organization –Data are raw facts that describe the characteristics of an event Sales event – date, item number, item description, quantity ordered, customer name, shipping details –Information is data converted into a menaingful and useful context Sales event – best/worst selling item, best/worst customer Employees must be able to obtain and analyze the many different levels, formats, and granularities of organizational information to make decisions Successfully collecting, compiling, sorting, and analyzing information can provide tremendous insight into how an organization is performing

3. 6-3 UNDERSTANDING INFORMATION Information granularity – refers to the extent of detail within the information (fine and detailed or coarse and abstract) –Levels –Formats –Granularities

4. 6-4 UNDERSTANDING INFORMATION Information TypesRangeExamples Information LevelsIndividualIndividual knowledge, goals and strategies DepartmentDepartmental goals, revenues, expenses, processes and strategies EnterpriseEnterprise-wide revenues, expenses, processes and strategies Information FormatsDocumentLetters, memos, faxes, e-mails, reports, marketing materials PresentationProduct, strategy, process, financial, customer and competitor presentations SpreadsheetSales, marketing, industry, financial, competitor, customer, and order spreadsheets DatabaseCustomer, employee, sales, order, supplier and manufacturer Information GranularitiesDetail (Fine)Reports for each salesperson, product and part SummaryReports for all sales personnel, all products and all parts Aggregate (Coarse)Reports across departments, organizations and companies

5. 6-5 Information Quality Business decisions are only as good as the quality of the information used to make the decisions Characteristics of high quality information include: –Accuracy Are all the values correct? Is the name spelled correctly? Is the dollar amount recorded properly? –Completeness Are any of the values missing? Is the address complete including street, city, state, and zip code? –Consistency Is aggregate or summary information in agreement with detailed information? Do all total fields equal the true total of the individual fields? –Uniqueness Is each transaction, entity, and event represented only once in the information? Are there any duplicate customers? –Timeliness Is the information current with respect to the business requirements? Is information updated weekly, daily, or hourly?

6. 6-6 Information Quality Low quality information example

7. 6-7 Information Quality Issue 1: Without a first name it would be impossible to correlate this customer with customers in other databases (Sales, Marketing, Billing, Customer Service) to gain a compete customer view (CRM) Issue 2: Without a complete street address there is no possible way to communicate with this customer via mail or deliveries. An order might be sitting in a warehouse waiting for the complete address before shipping. The company has spent time and money processing an order that might never be completed Issue 3: If this is the same customer, the company will waste money sending out two sets of promotions and advertisements to the same customers. It might also send two identical orders and have to incur the expense of one order being returned Issue 4: This is a good example of where cleaning data is difficult because this may or may not be an error. There are many times when a phone and a fax have the same number. Since the phone number is also in the e-mail address field, chances are that the number is inaccurate Issue 5: The business would have no way of communicating with this customer via e-mail Issue 6: The company could determine the area code based on the customer’s address. This takes time, which costs the company money. This is a good reason to ensure that information is entered correctly the first time. All incorrect information needs to be fixed, which costs time and money

8. 6-8 Understanding the Costs of Poor Information The four primary sources of low quality information include: 1.Online customers intentionally enter inaccurate information to protect their privacy 2.Information from different systems have different entry standards and formats 3.Call center operators enter abbreviated or erroneous information by accident or to save time 4.Third party and external information contains inconsistencies, inaccuracies, and errors

9. 6-9 Understanding the Costs of Poor Information Potential business effects resulting from low quality information include: –Inability to accurately track customers –Difficulty identifying valuable customers –Inability to identify selling opportunities –Marketing to nonexistent customers –Difficulty tracking revenue due to inaccurate invoices –Inability to build strong customer relationships

10. 6-10 Understanding the Costs of Poor Information Poor information could cause the SCM system to order too much inventory from a supplier based on inaccurate orders Poor information could cause a CRM system to send an expensive promotional item (such as a fruit basket) to the wrong address of one of its best customers What occurs when you have the inability to build strong customer relationships? –Decreased seller power

11. 6-11 Understanding the Benefits of Good Information High quality information can significantly improve the chances of making a good decision Good decisions can directly impact an organization's bottom line

12. 6-12 DATABASE FUNDAMENTALS Information is everywhere in an organization Almost every business decision is based on information Information is stored in databases –Database – maintains information about various types of objects (inventory), events (transactions), people (employees), and places (warehouses)

13. 6-13 DATABASE FUNDAMENTALS Database models include: –Hierarchical database model – information is organized into a tree-like structure (using parent/child relationships) in such a way that it cannot have too many relationships –Network database model – a flexible way of representing objects and their relationships –Relational database model – stores information in the form of logically related two-dimensional tables

14. 6-14 DATABASE ADVANTAGES Database advantages from a business perspective include –Increased flexibility –Increased scalability and performance –Reduced information redundancy –Increased information integrity (quality) –Increased information security –Spreadsheet limitations Limited number of rows and columns (Excel - 65,536 rows by 256 columns) Once you use more than 65,536 rows you have outgrown your spreadsheet Only one users can access the spreadsheet Users can view all information in the spreadsheet Users can change all information in the spreadsheet

15. 6-15 Increased Flexibility A well-designed database should: –Handle changes quickly and easily –Provide users with different views –Have only one physical view Physical view – deals with the physical storage of information on a storage device –Have multiple logical views Logical view – focuses on how users logically access information

16. 6-16 Increased Scalability and Performance A database must scale to meet increased demand, while maintaining acceptable performance levels –Scalability – refers to how well a system can adapt to increased demands –Performance – measures how quickly a system performs a certain process or transaction

17. 6-17 Reduced Redundancy Databases reduce information redundancy by recording each piece of information in only one place –Redundancy – the duplication of information or storing the same information in multiple places; can lead to low quality information Inconsistency is one of the primary problems with redundant information

18. 6-18 Increased Integrity (Quality) Information integrity – measures the quality of information Integrity constraint – rules that help ensure the quality of information –Relational integrity constraint – rule that enforces basic and fundamental information-based constraints Users cannot create an order for a nonexistent customer An order cannot be shipped without an address –Business-critical integrity constraint – rule that enforce business rules vital to an organization’s success and often require more insight and knowledge than relational integrity constraints Product returns are not accepted for fresh product 15 days after purchase A discount maximum of 20 percent

19. 6-19 Increased Security Information is an organizational asset and must be protected Databases offer several security features including: –Password – provides authentication of the user –Access level – determines who has access to the different types of information –Access control – determines types of user access, such as read-only access

20. 6-20 Increased Security Why you would want to define access level security? –Access levels will typically mimic the hierarchical structure of the organization and protect organizational information from being viewed and manipulated by individuals who should not have access to the sensitive or confidential information Low level employees typically have the lowest levels of access High level employees typically have access to all types of database information

21. 6-21 Increased Security –For example: You would not want analysts viewing all salary information for the entire company - in general: Analysts can usually only view their own salary Managers have higher access and can view the salaries of all their team members, but cannot view other managers’ salaries Directors can view all of their managers’ and analysts’ salaries, but not other directors’ salaries The CFO and CEO can view every employee’s salary

22. 6-22 Entity – a person, place, thing, transaction, or event about which information is stored –The rows in each table contain the entities –In Figure 6.5 CUSTOMER includes Dave’s Sub Shop and Pizza Palace entities Entity class (table) – a collection of similar entities –In Figure 6.5 CUSTOMER, ORDER, ORDER LINE, DISTRIBUTOR, and PRODUCT entity classes RELATIONAL DATABASE FUNDAMENTALS

23. 6-23 Attributes (fields, columns) – characteristics or properties of an entity class –The columns in each table contain the attributes –In Figure 6.5 attributes for CUSTOMER include: Customer ID Customer Name Contact Name Phone –Possible other attributes: Address Fax E-mail Cell phone RELATIONAL DATABASE FUNDAMENTALS

24. 6-24 Primary keys and foreign keys identify the various entity classes (tables) in the database –Primary key – a field (or group of fields) that uniquely identifies a given entity in a table –Foreign key – a primary key of one table that appears an attribute in another table and acts to provide a logical relationship among the two tables –Example Hawkins Shipping in the DISTRIBUTOR table has a primary key called Distributor ID – DEN8001 Hawkins Shipping (Distributor ID DEN8001) is responsible for delivering orders 34561 and 345652 Therefore, Distributor ID in the ORDER table creates a logical relationship (who shipped what order) between ORDER and DISTRIBUTOR RELATIONAL DATABASE FUNDAMENTALS

25. 6-25 Potential relational database for Coca- Cola

26. 6-26 How many orders have been placed for T’s Fun Zone? –Ans: 1 Order IT 34563 How many orders have been placed for Pizza Palace? –Ans: None How many items are included in Dave’s Sub Shop’s two orders? –Ans: Order 34561 has 3 items and order 34562 has one item for a total of 4 items in both orders. Who is responsible for distributing Dave’s Sub Shop’s orders? –Ans: Hawkins Shipping Which products are included in Order 34562? –Ans: 300 Vanilla Coke RELATIONAL DATABASE FUNDAMENTALS

27. 6-27 DATABASE MANAGEMENT SYSTEMS Database management systems (DBMS) – software through which users and application programs interact with a database

28. 6-28 DATABASE MANAGEMENT SYSTEMS Direct interaction – –The user interacts directly with the DBMS –The DBMS obtains the information from the database Indirect interaction –User interacts with an application (i.e., payroll application, manufacturing application, sales application) –The application interacts with the DBMS –The DBMS obtains the information from the database

29. 6-29 DATABASE MANAGEMENT SYSTEMS Four components of a DBMS

30. 6-30 Data Definition Component Data definition component – creates and maintains the data dictionary and the structure of the database The data definition component includes the data dictionary –Data dictionary – a file that stores definitions of information types, identifies the primary and foreign keys, and maintains the relationships among the tables –The data dictionary is an important part of the DBMS because users can consult the dictionary to determine the different types of database information

31. 6-31 Data Definition Component Data dictionary essentially defines the logical properties of the information that the database contains Business integrity constraint Relational integrity constraint

32. 6-32 Data Manipulation Component Data manipulation component – allows users to create, read, update, and delete information in a database A DBMS contains several data manipulation tools: –View – allows users to see, change, sort, and query the database content –Report generator – users can define report formats –Query-by-example (QBE) – users can graphically design the answers to specific questions –Structured query language (SQL) – query language

33. 6-33 Data Manipulation Component Sample report using Microsoft Access Report Generator

34. 6-34 Data Manipulation Component Sample report using Access Query-By-Example (QBE) tool

35. 6-35 Data Manipulation Component Results from the query in previous QBE

36. 6-36 Data Manipulation Component SQL version of the QBE Query in Figure 6.10

37. 6-37 Application Generation and Data Administration Components Application generation component – includes tools for creating visually appealing and easy-to- use applications Data administration component – provides tools for managing the overall database environment by providing faculties for backup, recovery, security, and performance IT specialists primarily use these components

38. 6-38 INTEGRATING DATA AMONG MULTIPLE DATABASES Integration – allows separate systems to communicate directly with each other –Forward integration – takes information entered into a given system and sends it automatically to all downstream systems and processes –Backward integration – takes information entered into a given system and sends it automatically to all upstream systems and processes

39. 6-39 INTEGRATING DATA AMONG MULTIPLE DATABASES One of the biggest benefits of integration is that organizations only have to enter information into the systems once and it is automatically sent to all of the other systems throughout the organization This feature alone creates huge advantages for organizations because it reduces information redundancy and ensures accuracy and completeness Without integrations an organization would have to enter information into every single system that requires the information from marketing and sales to billing and customer service –Entering the same customer information into multiple systems is redundant, and chances of making a mistake in one of the systems is high –For example, customer information would have to be manually entered into the marketing, sales, ordering, inventory, billing, and shipping databases. (Each of these systems are separate and would have their own database – if the company doesn’t have a complete ERP installed.)

40. 6-40 INTEGRATING DATA AMONG MULTIPLE DATABASES Forward and backward integration

41. 6-41 INTEGRATING DATA AMONG MULTIPLE DATABASES Sales enters the information when it is negotiating the sale (looking for opportunities) The information is then passed to the order entry system when the order is actually placed The order fulfillment system picks the products from the warehouse, packs the products, labels boxes, etc Once the order is filled and shipped, the customer is billed What would happen if users could enter order information directly into the billing system? –The systems would quickly become out-of-sync. There might be bills for nonexistent orders, or orders that do not have any bills (if someone deleted a bill) –For this reason organizations typically place a business-critical integrity constraint on integrated systems: With a forward integration the information must be entered in the sales system, you could not enter information directly into the billing system

42. 6-42 INTEGRATING DATA AMONG MULTIPLE DATABASES Integrations are expensive to build and maintain and difficult to implement For these reasons many organizations only build forward integrations and use business-critical integrity constraints to ensure all information is always entered only at the start of the integration (one source of record) Why would an organization want to build both forward and backward integrations? –This allows users to enter information at any point in the business process and the information is automatically sent upstream and downstream to all other systems –For example, if order fulfillment determined that they could not fulfill an order (the product had been discontinued), they could simply enter this information into the database and it would be sent automatically upstream to the sales representative who could contact the customer and downstream to billing to remove the item from the bill

43. 6-43 INTEGRATING DATA AMONG MULTIPLE DATABASES Building a central repository specifically for integrated information

44. 6-44 INTEGRATING DATA AMONG MULTIPLE DATABASES Users can create, read, update, and delete in the main customer repository, and it is automatically sent to all of the other databases Business-critical integrity constraints still need to be built to ensure information is only ever entered into the customer repository, otherwise the information will become out-of-sync

45. 6-45 HISTORY OF DATA WAREHOUSING Bill Inmon, is recognized as the "father of the data warehouse" and co-creator of the "Corporate Information Factory." Data warehouses extend the transformation of data into information In the 1990’s executives became less concerned with the day-to-day business operations and more concerned with overall business functions The data warehouse provided the ability to support decision making without disrupting the day-to-day operations

46. 6-46 DATA WAREHOUSE FUNDAMENTALS Data warehouse – a logical collection of information – gathered from many different operational databases – that supports business analysis activities and decision-making tasks The primary purpose of a data warehouse is to aggregate information throughout an organization into a single repository for decision-making purposes –Database store information for a single application whereas a data warehouse stores information from multiple databases, or multiple applications, and external information such as industry information This enables cross-functional analysis, industry analysis, market analysis, etc., all from a single repository –Data warehouses support online analytical processing (OLAP)

47. 6-47 DATA WAREHOUSE FUNDAMENTALS Extraction, transformation, and loading (ETL) – a process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse –ETL process also gathers data from the data warehouse and passes it to the data marts Data mart – contains a subset of data warehouse information –A data warehouse has an enterprise-wide organizational focus, while a data mart focuses on a subset of information for a given business unit such as finance

48. 6-48 DATA WAREHOUSE FUNDAMENTALS

49. 6-49 Multidimensional Analysis Databases contain information in a series of two-dimensional tables In a data warehouse and data mart, information is multidimensional, it contains layers of columns and rows –Dimension – a particular attribute of information – such as Products, Promotions, Stores, Category, Region, Stock price, Date, Time, Weather The ability to look at information from different dimensions can add tremendous business insight –By slicing-and-dicing the information a business can uncover great unexpected insights

50. 6-50 Multidimensional Analysis Cube – common term for the representation of multidimensional information

51. 6-51 Multidimensional Analysis Users can slice and dice the cube to drill down into the information –Cube A represents store information (the layers), product information (the rows), and promotion information (the columns) –Cube B represents a slice of information displaying promotion II for all products at all stores –Cube C represents a slice of information displaying promotion III for product B at store 2

52. 6-52 Multidimensional Analysis Data mining – the process of analyzing data to extract information not offered by the raw data alone –Data mining can begin at a summary information level (coarse granularity) and progress through increasing levels of detail (drilling down), or the reverse (drilling up) To perform data mining users need data-mining tools –Data-mining tool – uses a variety of techniques to find patterns and relationships in large volumes of information and infers rules that predict future behavior and guide decision making Data-mining tools include query tools, reporting tools, multidimensional analysis tools, statistical tools, and intelligent agents

53. 6-53 Multidimensional Analysis What might an accountant discover through the use of data-mining tools to drill down into the details of all of the expense and revenue? –Which employees are spending the most amount of money on long-distance phone calls –Which customers are returning the most products

54. 6-54 Information Cleansing or Scrubbing An organization must maintain high-quality data in the data warehouse What would happen if the information contained in the data warehouse was only about 70 percent accurate? –Would you use this information to make business decisions? –Is it realistic to assume that an organization could get to a 100% accuracy level on information contained in its data warehouse? Information cleansing or scrubbing – a process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information

55. 6-55 Information Cleansing or Scrubbing Customer information exists in several operational systems with different detail information –Determining which contact information is accurate and correct for this customer depends on the business process that is being executed

56. 6-56 Information Cleansing or Scrubbing Standardizing Customer name from Operational Systems

57. 6-57 Information Cleansing or Scrubbing Typical events that occur during information cleansing

58. 6-58 Information Cleansing or Scrubbing Accurate and complete information

59. 6-59 Information Cleansing or Scrubbing Why do you think most businesses cannot achieve 100% accurate and complete information? –Some companies are willing to go as low as 20% complete just to find business intelligence –Few organizations will go below 50% accurate – the information is useless if it is not accurate Achieving perfect information is almost impossible –The more complete and accurate an organization wants to get its information, the more it costs –The tradeoff between perfect information lies in accuracy verses completeness –Accurate information means it is correct, while complete information means there are no blanks –Most organizations determine a percentage high enough to make good decisions at a reasonable cost, such as 85% accurate and 65% complete

60. 6-60 BUSINESS INTELLIGENCE Business intelligence – information that people use to support their decision- making efforts Principle BI enablers include: –Technology –People –Culture

61. 6-61 BUSINESS INTELLIGENCE Technology –Even the smallest company with BI software can do sophisticated analyses today that were unavailable to the largest organizations a generation ago. The largest companies today can create enterprise-wide BI systems that compute and monitor metrics on virtually every variable important for managing the company. –Technology is the most significant enabler of business intelligence. People –Understanding the role of people in BI allows organizations to systematically create insight and turn these insights into actions. Organizations can improve their decision making by having the right people making the decisions. This usually means a manager who is in the field and close to the customer rather than an analyst rich in data but poor in experience. –In recent years “business intelligence for the masses” has been an important trend, and many organizations have made great strides in providing sophisticated yet simple analytical tools and information to a much larger user population than previously possible.

62. 6-62 BUSINESS INTELLIGENCE Culture –A key responsibility of executives is to shape and manage corporate culture. The extent to which the BI attitude flourishes in an organization depends in large part on the organization’s culture. –Perhaps the most important step an organization can take to encourage BI is to measure the performance of the organization against a set of key indicators. The actions of publishing what the organization thinks are the most important indicators, measuring these indicators, and analyzing the results to guide improvement display a strong commitment to BI throughout the organization

63. 6-63 DATA MINING Data-mining software includes many forms of AI such as neural networks and expert systems

64. 6-64 DATA MINING Data-mining tools apply algorithms to information sets to uncover inherent trends and patterns in the information Analysts use this information to develop new business strategies and business solutions Common forms of data-mining analysis capabilities include: –Cluster analysis –Association detection –Statistical analysis

65. 6-65 Cluster Analysis Cluster analysis – a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible –Consumer goods by content, brand loyalty or similarity –Product market typology for tailoring sales strategies –Retail store layouts and sales performances –Corporate decision strategies using social preferences CRM systems depend on cluster analysis to segment customer information and identify behavioral traits

66. 6-66 Association Detection Association detection – reveals the degree to which variables are related and the nature and frequency of these relationships in the information –Maytag uses association detection to ensure that each generation of appliances is better than the previous generation –Maytag’s warranty analysis tool automatically detects potential issues, provides quick and easy access to reports, and performs multidimensional analysis on all warranty information –Market basket analysis – analyzes such items as Web sites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services

67. 6-67 Statistical Analysis Statistical analysis – performs such functions as information correlations, distributions, calculations, and variance analysis –Forecast – predictions made on the basis of time-series information –Time-series information – time-stamped information collected at a particular frequency

68. 6-68 Statistical Analysis Kraft uses statistical analysis to assure consistent flavor, color, aroma, texture, and appearance for all of its lines of foods Kraft evaluates every manufacturing procedure, from recipe instructions to cookie dough shapes and sizes to ensure that the billions of Kraft products that reach consumers each year taste great (and the same) with every bite Nestle Italiana uses data mining and statistical analysis to determine production forecasts for seasonal confectionery products The company’s data-mining solution gathers, organizes, and analyzes massive volumes of information to produce powerful models that identify trends and predict confectionery sales

69. 6-69 Mining the Data Warehouse Ben & Jerry’s tracks the ingredients and life of each pint in a data warehouse. If a consumer calls in with a complaint, the consumer affairs staff matches up the pint with which supplier’s mile, eggs, or cherries, etc. did not meet the organization’s near-obsession with quality.

70. 6-70 BI at Harrah’s The Total Rewards program allows Harrah’s to give every single customer the appropriate amount of personal attention, whether it’s leaving sweets in the hotel room or offering free meals. –Total Rewards works by providing each customer with an account and a corresponding card that the player swipes each time he or she plays a casino game. The program collects information, via a database, on the amount of time the customers gamble, their total winnings and losses, and their betting strategies. Customers earn points based on the amount of time they spend gambling, which they can then exchange for comps such as free dinners, hotel rooms, tickets to shows, and even cash. –Without database integration among its hotels and casinos, Harrah’s would be unable to determine what a customer’s true value is to the company. For example, a customer that spend $500,000 dollars at one casino might be treated like royalty. This same customer could visit another Harrah’s location, but since the information is not integrated, the new location would have no idea that they had a high-rolling customer on the premises and they might not treat the customer accordingly.