Assignment 87

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Essentials of Management Information Systems

Thirteenth Edition

Chapter 6

Foundations of Business Intelligence: Databases and Information Management

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Learning Objectives

6.1 What is a database, and how does a relational database organize data?

6.2 What are the principles of a database management system?

6.3 What are the principal tools and technologies for accessing information from databases to improve business performance and decision making?

6.4 Why are information policy, data administration, and data quality assurance essential for managing the firm’s data resources?

6.5 How will M I S help my career?

Ask students to make a list with you of all the databases they use or interact with in their lives. The idea here is to make students aware of the ubiquity and importance of record keeping systems (databases). Databases are at the very heart of the MIS profession.

Ask students if they have had any experience with errors in a database? A mistaken identity, a wrong address, an incorrect balance on a statement. Why are data errors important for a business? For individuals?

Video Cases

Case 1: Dubuque Uses Cloud Computing and Sensors to Build a Smarter City

Case 2: Brooks Brothers Closes in on Omnichannel Retail

Case 3: Maruti Suzuki Business Intelligence and Enterprise Databases

What is a Database?

Database:

Collection of related files containing records on people, places, or things

Entity:

Generalized category representing person, place, thing

E.g., SUPPLIER, PART

Attributes:

Specific characteristics of each entity:

SUPPLIER name, address

PART description, unit price, supplier

Ask students if databases are always digital? Can they think of a non-digital database? How about a telephone book. What are the entities? The attributes? Or, a library card catalog.

Relational Databases

Organize data into two-dimensional tables (relations) with columns and rows

One table for each entity:

E.g., (CUSTOMER, SUPPLIER, PART, SALES)

Fields (columns) store data representing an attribute

Rows store data for separate records, or tuples

Key field: uniquely identifies each record

Primary key

Figure 6.2 A Relational Database Table

Figure 6.2, Page 199.

A relational database organizes data in the form of two-dimensional tables. Illustrated here is a table for the entity SUPPLIER showing how it represents the entity and its attributes. Supplier_Number is the key field.

The graphic on this slide and the next illustrates two tables in a relational DBMS. Ask students what the entity on this slide and the next are.

The key field in the Supplier table is the Supplier number. What is the purpose of the key field?

Figure 6.3 The PART Table

Figure 6.3, Page 199.

Data for the entity PART have their own separate table. Part_Number is the primary key and Supplier_Number is the foreign key, enabling users to find related information from the SUPPLIER table about the supplier for each part.

This slide shows the second part of the graphic on the previous slide. Notice that the foreign key in this table is the primary key in the Suppliers table. What is the purpose of the foreign key. Can multiple records have the same foreign key?

Establishing Relationships (1 of 2)

Entity-relationship diagram

Used to clarify table relationships in a relational database

Relational database tables may have:

One-to-one relationship

One-to-many relationship

Many-to-many relationship

Requires “join table” or intersection relation that links the two tables to join information

Figure 6.4 A Simple Entity-Relationship Diagram

Figure 6.4, Page 200.

This diagram shows the relationship between the entities SUPPLIER and PART.

Figure 6.5 Sample Order Report

Figure 6.5, Page 201.

The shaded areas show which data came from the ORDER, SUPPLIER, and LINE_ITEM tables. The database does not maintain data on extended price or order total because they can be derived from other data in the tables.

Figure 6.6 The Final Database Design with Sample Records

Figure 6.6, Page 202.

The final design of the database for suppliers, parts, and orders has four tables. The LINE_ITEM table is a join table that eliminates the many-to-many relationship between ORDER and PART.

Figure 6.7 Entity-Relationship Diagram for the Database with Four Tables

Figure 6.7, Page 203.

This diagram shows the relationship between the SUPPLIER, PART, LINE_ITEM, and ORDER entities.

This graphic shows an example of an entity relationship diagram. It shows that one ORDER can contain many LINE_ITEMs. (A PART can be ordered many times and appear many times as a line item in a single order.) Each LINE ITEM can contain only one PART. Each PART can have only one SUPPLIER, but many PARTs can be provided by the same SUPPLIER.

Database Management Systems (D B M S)

Software for creating, storing, organizing, and accessing data from a database

Separates the logical and physical views of the data

Logical view: how end users view data

Physical view: how data are actually structured and organized

Examples: Microsoft Access, D B 2, Oracle Database, Microsoft S Q L Server, My S Q L

One of the most popular open source databases (purchased by SUN Computer but now owned by Oracle) is MySQL. Go to http://www.mysql.com/customers/ in class for some illustrations of companies that use MySQL. This database management system is not as sophisticated as high end enterprise database systems but still quite sufficient for many businesses.

Figure 6.8 Human Resources Database with Multiple Views

Figure 6.8, Page 204.

A single human resources database provides many views of data, depending on the information requirements of the user. Illustrated here are two possible views, one of interest to a benefits specialist and one of interest to a member of the company’s payroll department.

This graphic illustrates what is meant by providing different logical views of data. The orange rectangles represent two different views in an HR database, one for reviewing employee benefits, the other for accessing payroll records. The students can think of the green cylinder as the physical view, which shows how the data are actually organized and stored on the physical media. The physical data do not change, but a DBMS can create many different logical views to suit different needs of users.

Operations of a Relational D B M S

Select:

Creates a subset of all records meeting stated criteria

Join:

Combines relational tables to present the server with more information than is available from individual tables

Project:

Creates a subset consisting of columns in a table

Permits user to create new tables containing only desired information

Figure 6.9 The Three Basic Operations of a Relational D B M S

Figure 6.9, Page 205.

The select, join, and project operations enable data from two tables to be combined and only selected attributes to be displayed.

This graphic illustrates the result from combining the select, join, and project operations to create a subset of data. The SELECT operation retrieves just those parts in the PART table whose part number is 137 or 150. The JOIN operation uses the foreign key of the Supplier_Number provided by the PART table to locate supplier data from the Supplier Table for just those records selected in the SELECT operation. Finally, the PROJECT operation limits the columns to be shown to be simply the part number, part name, supplier number, and supplier name (orange rectangle).

Capabilities of Database Management Systems

Data definition capabilities:

Specify structure of content of database

Data dictionary:

Automated or manual file storing definitions of data elements and their characteristics

Querying and reporting:

Data manipulation language

Structured query language (S Q L)

Microsoft Access query-building tools

Report generation, e.g., Crystal Reports

One important function of databases is to bring about common definitions of entities and attributes, like what is a fiscal year, how to express date of hire, and defining “business location.” In pre-database environments, and even in global companies, these definitions vary from one location to another.

Figure 6.10 Access Data Dictionary Features

Figure 6.10, Page 206.

Microsoft Access has a rudimentary data dictionary capability that displays information about the size, format, and other characteristics of each field in a database. Displayed here is the information maintained in the SUPPLIER table. The small key icon to the left of Supplier_Number indicates that it is a key field.

This graphic shows the data dictionary capability of Microsoft Access. For the field “Supplier Name” selected in the top pane, definitions can be configured in the General tab in the bottom pane. These General characteristics are Fields Size, Format, Input Mask, Caption, Default Value, Validation Rule, Validation Text, Required, Allow Zero Length, Indexed, Unicode Compression, IME mode, IME Sentence Mode, and Smart Tags.

Figure 6.11 Example of an S Q L Query

SELECT PART.Part_Number, PART.Part_Name, SUPPLIER.Supplier_Number,

SUPPLIER.Supplier_Name

FROM PART, SUPPLIER

WHERE PART.Suplier_Number = SUPPLIER.Supplier_Number AND

Part_Number = 137 OR Part_Number = 150;

Figure 6.11, Page 206.

Illustrated here are the SQL statements for a query to select suppliers for parts 137 or 150. They produce a list with the same results as Figure 6.9.

This graphic shows an example SQL statement that would be used to retrieve data from a database. In this case, the SQL statement is retrieving records from the PART table illustrated on Slide 20 (Figure 6-8) whose Part Number is either 137 or 150.

Ask students to relate what each phrase of this statement is doing. (For example, the statement says to take the following columns: Part_Number, Part_Name, Supplier Number, Supplier Name, from the two tables Part and Supplier, when the following two conditions are true…)

Figure 6.12 An Access Query

Figure 6.12, Page 207.

Illustrated here is how the query in Figure 6.11 would be constructed using Microsoft Access query-building tools. It shows the tables, fields, and selection criteria used for the query.

This graphic illustrates a Microsoft Access query that performs the same operation as the SQL query in the last slide. The query pane at the bottom shows the fields that are requested (Fields), the relevant Tables (Table), the fields that will be displayed in the results (Show), and the criteria limiting the results to Part numbers 137 and 150 (Criteria).

Non-Relational Databases

“No S Q L”

Handle large data sets of data that are not easily organized into tables, columns, and rows

Use more flexible data model

Don’t require extensive structuring

Can manage unstructured data, such as social media and graphics

E.g. Amazon’s Simple D B, MetLife’s Mongo D B

Cloud Databases and Distributed Databases

Relational database engines provided by cloud computing services

Pricing based on usage

Appeal to small or medium-sized businesses

Amazon Relational Database Service

Offers My S Q L, Microsoft S Q L Server, Oracle Database engines

Distributed databases

Stored in multiple physical locations

Google’s Spanner cloud service

The Challenge of Big Data

Massive quantities of unstructured and semi-structured data from Internet and more

3V s: Volume, variety, velocity

Petabytes and exabytes

Big datasets offer more patterns and insights than smaller datasets, e.g.

Customer behavior

Weather patterns

Requires new technologies and tools

Business Intelligence Infrastructure

Array of tools for obtaining useful information from internal and external systems and big data

Data warehouses

Data marts

Hadoop

In-memory computing

Analytical platforms

Modern databases can store enormous amounts of information. Consider that PhotoBucket has 80 billion photos on tap! Yet making sense out of all this data is a challenge for managers. What’s needed are tools to organize the data, analyze, and describe what’s happening in the real world based on the data.

Data Warehouses

Data warehouse:

Database that stores current and historical data that may be of interest to decision makers

Consolidates and standardizes data from many systems, operational and transactional databases

Data can be accessed but not altered

Data mart:

Subset of data warehouses that is highly focused and isolated for a specific population of users

Data warehouses and data marts are two tools that bring data together and move it offline to storage areas where it can be analyzed without interfering with the transaction processing systems that produce the data. Data analysis, business intelligence applications, would slow down transaction processing.

Hadoop

Open-source software framework for big data

Breaks data task into sub-problems and distributes the processing to many inexpensive computer processing nodes

Combines result into smaller data set that is easier to analyze

Key services

Hadoop Distributed File System (H D F S)

MapReduce

In-Memory Computing

Relies on computer’s main memory (RAM) for data storage

Eliminates bottlenecks in retrieving and reading data

Dramatically shortens query response times

Enabled by high-speed processors, multicore processing

Lowers processing costs

Analytic Platforms

Preconfigured hardware-software systems

Designed for query processing and analytics

Use both relational and non-relational technology to analyze large data sets

Include in-memory systems, No S Q L D B M S

E.g. I B M Pure Data System for Analytics

Integrated database, server, storage components

Data lakes

Figure 6.13 Business Intelligence Technology Infrastructure

Figure 6.13, Page 213.

A contemporary business intelligence technology infrastructure features capabilities and tools to manage and analyze large quantities and different types of data from multiple sources. Easy-to-use query and reporting tools for casual business users and more sophisticated analytical toolsets for power users are included.

Analytical Tools: Relationships, Patterns, Trends

Once data is gathered, tools are required for consolidating, analyzing, to use insights to improve decision making

Software for database querying and reporting

Multidimensional data analysis (O L A P)

Data mining

Online Analytical Processing (O L A P)

Supports multidimensional data analysis, enabling users to view the same data in different ways using multiple dimensions

Each aspect of information—product, pricing, cost, region, or time period—represents a different dimension

E.g., comparing sales in East in June versus May and July

Enables users to obtain online answers to ad hoc questions such as these in a fairly rapid amount of time

Figure 6.14 Multidimensional Data Model

Figure 6.14, Page 214.

This view shows product versus region. If you rotate the cube 90 degrees, the face that will show is product versus actual and projected sales. If you rotate the cube 90 degrees again, you will see region versus actual and projected sales. Other views are possible.

This graphic illustrates a data cube composed of three dimensions: actual/projected sales, product, and region. Obviously, the point is to try and understand differences between actual and projected sales by looking at region and product. This is an ideal problem for pivot tables in Excel because two of the variables are categorical (product and region), one is interval.

Data Mining

Finds hidden patterns and relationships in large databases and infers rules from them to predict future behavior

Types of information obtainable from data mining

Associations: occurrences linked to single event

Sequences: events linked over time

Classifications: patterns describing a group an item belongs to

Clustering: discovering as yet unclassified groupings

Forecasting: uses series of values to forecast future values

With terrorism so much in the news, you might ask students how they imagine federal officials use data mining to identify potential or actual terrorists. For instance, if one was looking for terrorists, what kinds of associations, sequences, classifications, and clusters would you look for.

Text Mining

Unstructured data (mostly text files) accounts for 80 percent of an organization’s useful information.

Text mining allows businesses to extract key elements from, discover patterns in, and summarize large unstructured data sets.

Sentiment analysis

Mines online text comments online or in email to measure customer sentiment

Web Mining

Discovery and analysis of useful patterns and information from the web

E.g. to understand customer behavior, evaluate website, quantify success of marketing

Content mining – mines content of websites

Structure mining – mines website structural elements, such as links

Usage mining – mines user interaction data gathered by web servers

Databases and the Web

Firms use the web to make information from their internal databases available to customers and partners.

Middleware and other software make this possible

Web server

Application servers or C G I

Database server

Web interfaces provide familiarity to users and savings over redesigning legacy systems.

Figure 6.15 Linking Internal Databases to the Web

Figure 6.15, Page 216.

Users access an organization’s internal database through the web, using their desktop PCs or mobile devices and web browser software.

This graphic illustrates the way data is passed from a database through to a user with a Web browser. Ask students to describe what types of data transformation occur between the various appliances; for example, what happens to data between the database and the database server?

Establishing an Information Policy

Information policy

States organization’s rules for organizing, managing, storing, sharing information

Data administration

Responsible for specific policies and procedures through which data can be managed as a resource

Database administration

Database design and management group responsible for defining and organizing the structure and content of the database, and maintaining the database.

Databases are much more than hardware and software. Indeed the most difficult parts involve organizational and people considerations. Sometimes the careers of people, and the fate of entire departments, are involved with the data they collect. When you install a DBMS you potentially are changing who collects what information about whom, where, when, and how.

Ensuring Data Quality

Poor data quality: major obstacle to successful customer relationship management

Data quality problems caused by:

Redundant and inconsistent data produced by multiple systems

Data input errors

Data quality audit

Data cleansing

Databases are often collections of dirty data, data that is ambiguous, inaccurate, or incomplete. Before a modern DBMSD system is installed, one large budget item is usually cleaning up the old data in old systems. The size of data quality problems varies from one database to another, but credit record databases usually exhibit a 25 percent rate of quality problems, with 10 percent of the records actually being wrong. Ask students if they are aware of instances where a database was wrong about some person or entity.

How Will M I S Help My Career?

The Business: Mega Midwest Power

Position Description

Job Requirements

Interview Questions

A good opportunity for a class discussion of the new Section on careers. Would any in the class be interested in a job like this? What do they think are the most important skills the employer is looking for? How would they answer the interviewer questions?