Databases and Data Warehouses Chapter 3 Databases and Data Warehouses

STUDENT LEARNING OUTCOMES Describe business intelligence and its role in an organization. Differentiate between databases and data warehouses with respect to their focus on OLTP and OLAP. List and describe the key characteristics of a relational database.

STUDENT LEARNING OUTCOMES Define the five software components of a database management system. List and describe the key characteristics of a data warehouse. Define the four major types of data-mining tools in a data warehouse environment. List key considerations in information ownership in an organization.

Can Companies Keep Your Personal Information Secure and Private? Databases and data warehouses are organizational repositories of information Much of the information is personal It must be secure If hackers get your personal information, you can suffer from identity theft

Can Companies Keep Your Personal Information Secure and Private? Top-10 incidents of personal information loss by organizations Could affect over 53 million people CardSystems lost information on 40 million customers Many others

Can Companies Keep Your Personal Information Secure and Private? Have you been a victim of identity theft? What happened? What did you do to recover? How long did it take?

INTRODUCTION Businesses need business intelligence (BI) Business intelligence – knowledge about your customers, competitors, business partners, environment, and internal operations Enables effective decision making Information on steroids

INTRODUCTION IT tools help process information to create business intelligence according to… OLTP (online transaction processing) OLAP (online analytical processing)

INTRODUCTION OLTP – gathering and processing transaction information and updating existing information to reflect transaction Databases support OLTP Operational database – database that supports OLTP

INTRODUCTION OLAP – manipulation of information to support decision making Databases can help some Data warehouses support only OLAP, not OLTP Data warehouses – special forms of databases that support decision making

INTRODUCTION

INTRODUCTION This chapter – database and data warehouse concepts Along with some privacy and security considerations

RELATIONAL DATABASE MODEL Database – logical collection of information you organize and access according to the logical structure of the information Relational database – uses a series of two-dimensional tables or files to store information in the form of a database

Databases Are… Collections of information Created with logical structures With logical ties within the information With built-in integrity constraints

Databases – Collections of Information Databases have many tables Solomon Enterprises as a concrete provider. Tables include: Order Customer Concrete Type Employee Truck

Databases – Collections of Information

Databases – Created with Logical Structures In databases, row numbers are irrelevant In databases, columns have logical names such as Order Date and Customer Name Data dictionary – contains the logical structure of the information in a database

Databases – Logical Ties within the Information Logical ties must exist between the tables Logical ties are created with primary and foreign keys Primary key – field (or group of fields in some cases) that uniquely describe each record

Databases – Logical Ties within the Information Foreign key – primary key of one file that appears in another file Foreign keys help create relationships among tables Table = file = relation (don’t confuse yourself)

Databases – Logical Ties within the Information

Databases – Built-in Integrity Constraints Integrity constraint – rule that helps ensure the quality of information Examples Primary keys must be unique Foreign keys cannot be blank Sales price cannot be negative Phone numbers must have an area code

DBMS TOOLS Database management system (DBMS) – helps you specify the logical organization for a database and access and use the information within a database Word processing software = document Spreadsheet software = workbook DBMS software = database

DBMS TOOLS 5 software components DBMS engine Data definition subsystem Data manipulation subsystem Application generation subsystem Data administration subsystem

DBMS TOOLS

DBMS Engine DBMS engine – accepts logical requests, converts them into their physical equivalent, and accesses the database and data dictionary DBMS engine separates the logical from the physical

DBMS Engine Physical view – how information is arranged, stored, and accessed on a storage device Logical view – how you (knowledge worker) need to arrange and access information Databases – you work only with logical views

Data Definition Subsystem Data definition subsystem – helps you create and maintain the data dictionary and define the structure of the files in a database Must create data dictionary for a database before entering any information

Data Manipulation Subsystem Data manipulation subsystem – helps you add, change, and delete information Primary interface between you and a database Views Report generators QBE tools SQL

Views View – allows you to see the contents of a database file Similar to a spreadsheet view Make changes Sort Query

Views

Report Generators Report generator – helps you quickly define formats of reports and what information you want to see in a report Save report formats to use later Uses a wizard interface

Report Generators Specify the fields you want in a report Specify the layout of the report

Report Generators

QBE Tools Query-by-example (QBE) tool – helps you graphically design the answer to a question “What driver most often delivers concrete to Triple A Homes?”

QBE Tools

SQL Structured query language (SQL) – standardized fourth-generation language found in most DBMSs Performs same task as QBE Uses sentence structure instead Mostly used by IT people

Application Generation Subsystem Application generation subsystem – contains facilities to help you develop transaction-intensive applications Data entry screens (called forms in Access) Programming languages Mostly used by IT people

Data Administration Subsystem Data administration subsystem – helps you manage the overall database environment Backup and recovery Security management Query optimization Concurrency control Change management

Data Administration Subsystem Backup and recovery Periodically back up information Recover a database after a failure Security management Who has access to what information Who can perform CRUD tasks on information

Data Administration Subsystem Query optimization Restructure physical view to optimize response times to queries Concurrency control What happens if two people simultaneously try to change the same information?

Data Administration Subsystem Change management What is the effect of structural changes to a database? What if you add a new column? What happens if you delete a column? What happens if you change a column’s attributes?

DATA WAREHOUSES & DATA MINING Data warehouses support OLAP and decision making Data warehouses do not support OLTP Data-mining tools are tools for working with data warehouse information DBMS software = database Data-mining tools = data warehouse

What Is a Data Warehouse? Data warehouse – logical collection of information – gathered from operational databases – used to create business intelligence that supports business analysis activities and decision-making tasks

What Is a Data Warehouse?

What Is a Data Warehouse? Multidimensional Rows and columns Also layers Many times called hypercubes What are the dimensions in Figure 3.8 on page 97?

What Are Data-Mining Tools? Data-mining tools – software tools that you use to query information in a data warehouse Query-and-reporting tools Intelligent agents Multidimensional analysis tools Statistical tools

What Are Data-Mining Tools?

Query-and-Reporting Tools Query-and-reporting tools – similar to QBE tools, SQL, and report generators in the typical database environment Also similar to pivot tables in Excel

Intelligent Agents Use various AI tools such as neural networks and fuzzy logic to form the basis for “information discovery” and building BI Help you find hidden patterns in information Chapter 4 focuses on these

Multidimensional Analysis Tools Multidimensional analysis (MDA) tools – slice-and-dice techniques that allow you to view multidimensional information from different perspectives Bring new layers to the front Reorganize rows and columns

Statistical Tools Help you apply various mathematical models to the information stored in a data warehouse to discover new information Regression Analysis of variance And so on

Data Marts Data warehouses are organizationwide Data marts have subsets of an organizationwide data warehouse Data mart – subset of a data warehouse in which only a focused portion of the data warehouse information is kept

Data Marts

Data Mining as a Career Opportunity Knowledge of data mining can be a substantial career opportunity for you Business Objects SAS Cognos Informatica Many others

Considerations in Using a Data Warehouse Do you need a data warehouse? DBMS may offer all you need Do all employees need the entire data warehouse? Consider a data mart How up-to-date must information be? “Snapshot” concept What data-mining tools do you need? Training can be expensive

INFORMATION OWNERSHIP Strategic management support The sharing of information with responsibility Information cleanliness

Strategic Management Support Chief privacy officer (CPO) – ensuring that information is used in an ethical way Chief security officer (CSO) – ensuring security of information (e.g., firewalls) Chief information officer (CIO) – oversees every aspect of an organization’s information resource

Strategic Management Support Data administration – plans for, oversees the development of, and monitors the information resource Database administration – responsible for the more technical aspects and operational aspects of managing information Both often report to the CIO

The Sharing of Information with Responsibility If you create it, you “own” it You will also share it with others Because you “own” it, you are responsible for its quality

Information Cleanliness Database and data warehouse information must be “clean” No errors No duplicates

Information Cleanliness Extraction, transformation, and loading (ETL) – what information you want from each database, how the information is associated, and what rules to follow in consolidating the information to ensure its cleanliness in a data warehouse

CAN YOU… Describe business intelligence and its role in an organization. Differentiate between databases and data warehouses with respect to their focus on OLTP and OLAP. List and describe the key characteristics of a relational database.

CAN YOU… Define the five software components of a database management system. List and describe the key characteristics of a data warehouse. Define the four major types of data-mining tools in a data warehouse environment. List key considerations in information ownership in an organization.