IFSM 300 Stage 1-Assignment 1

Data and Databases

Introduction

You have been introduced to the five key components of information

systems. However, two components, hardware and software, by

themselves do not make a computer useful. Imagine if you turned on a

computer, started the word processor, but could not save a document.

Imagine if you opened a music player, but there was no music to play.

Imagine opening a web browser, but there were no web pages. Without

data, hardware and software are not very useful.

Data, Information, and Knowledge

Data is the raw bits and pieces of information with no context. If I told

you, “15, 23, 14, 85,” you would not have learned anything. But I would

have given you data.

Data can be quantitative or qualitative. Quantitative data is numeric, the

result of a measurement, count, or some other mathematical calculation.

Qualitative data is descriptive. “Ruby Red,” the color of a 2013 Ford

Focus, is an example of qualitative data. A number can be qualitative too:

If I tell you my favorite number is 5, that is qualitative data because it is

descriptive, not the result of a measurement or mathematical calculation.

By itself, data is not that useful. To be useful, it needs to be given context.

Learning Resource

Data and Databases

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Returning to the example above, if I told you that “15, 23, 14, and 85″ are

the numbers of students that had registered for upcoming classes, that

would be information. By adding the context—that the numbers represent

the count of students registering for specific classes—I have converted

data into information.

Once we have put our data into context, aggregated and analyzed it, we

can use it to make decisions for our organization. We can say that this

consumption of information produces knowledge. This knowledge can be

used to make decisions, set policies, and even spark innovation.

The final step up the information ladder is the step from knowledge

(knowing a lot about a topic) to wisdom. We can say that someone has

wisdom when they can combine their knowledge and experience to

produce a deeper understanding of a topic. It often takes many years to

develop wisdom on a particular topic, and requires patience.

Information Ladder

Progression of the

usefulness of data with

information, knowledge,

and wisdom

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Examples of Data

Almost all software programs require data to do anything useful. For

example, if you are editing a document in a word processor such as

Microsoft Word, the document you are working on is the data. The word‐

processing software can manipulate the data: create a new document,

duplicate a document, or modify a document. Some other examples of

data are an MP3 music file, a video file, a spreadsheet, a web page, and an

e‐book. In some cases, such as with an e‐book, you may only have the

ability to read the data.

Databases

The goal of many information systems is to transform data into

information in order to generate knowledge that can be used for decision

making. In order to do this, the system must be able to take data, put the

data into context, and provide tools for aggregation and analysis. A

database is designed for just such a purpose.

A database is an organized collection of related information. It is an

organized collection, because in a database, all data is described and

associated with other data. All information in a database should be

related as well; separate databases should be created to manage

unrelated information. For example, a database that contains information

about students should not also hold information about company stock

prices. Databases are not always digital—a filing cabinet, for instance,

might be considered a form of database. For the purposes of this text, we

will only consider digital databases.

Relational Databases

Databases can be organized in many different ways, and thus take many

forms. The most popular form of database today is the relational

database. Popular examples of relational databases are Microsoft Access,

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MySQL, and Oracle. A relational database is one in which data is

organized into one or more tables. Each table has a set of fields, which

define the nature of the data stored in the table. A record is one instance

of a set of fields in a table. To visualize this, think of the records as the

rows of the table and the fields as the columns of the table. In the

example below, we have a table of student information, with each row

representing a student and each column representing one piece of

information about the student.

Relational Database

Students’ names and information about the students

In a relational database, all the tables are related by one or more fields, so

that it is possible to connect all the tables in the database through the

field(s) they have in common. For each table, one of the fields is identified

as a primary key. This key is the unique identifier for each record in the

table. To help you understand these terms further, let’s walk through the

process of designing a database.

Designing a Database

Suppose a university wants to create an information system to track

participation in student clubs. After interviewing several people, the

design team learns that the goal of implementing the system is to give

better insight into how the university funds clubs. This will be

accomplished by tracking how many members each club has and how

active the clubs are. From this, the team decides that the system must

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keep track of the clubs, their members, and their events. Using this

information, the design team determines that the following tables need to

be created:

• Clubs: This table will track the club name, the club president, and a

short description of the club.

• Students: The table will contain student name, email, and year of

birth.

• Memberships: This table will correlate students with clubs, allowing

us to have any given student join multiple clubs.

• Events: This table will track when the clubs meet and how many

students showed up.

Now that the design team has determined which tables to create, they

need to define the specific information that each table will hold. This

requires identifying the fields that will be in each table. For example, Club

Name would be one of the fields in the Clubs table. First Name and Last

Name would be fields in the Students table. Finally, since this will be a

relational database, every table should have a field in common with at

least one other table (in other words: They should have a relationship

with each other).

In order to properly create this relationship, a primary key must be

selected for each table. This key is a unique identifier for each record in

the table. For example, in the Students table, it might be possible to use

students’ last name as a way to uniquely identify them. However, it is

more than likely that some students will share a last name (like Rodriguez,

Smith, or Lee), so a different field should be selected. A student’s email

address might be a good choice for a primary key, since email addresses

are unique. However, a primary key cannot change, so this would mean

that if students changed their email address, we would have to remove

them from the database and then re‐insert them—not an attractive

proposition. Our solution is to create a value for each student—a user

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ID—that will act as a primary key. We will also do this for each of the

student clubs. This solution is quite common and is the reason you have

so many user IDs.

You can see the final database design in the figure below:

Student Clubs Database

A user ID is the primary key that relates one table to another

With this design, not only do we have a way to organize all of the

information we need to meet the requirements, but we have also

successfully related all the tables together. Here’s what the database

tables might look like with some sample data. Note that the Memberships

table has the sole purpose of allowing us to relate multiple students to

multiple clubs.

Clubs Database Table

Club ID Club Name President Short desc

1 Cheese Club 14 To talk about our

love of cheese.

2 Chess Club 1 To learn how to

become better

chess players.

Sample data on specific clubs

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Club ID Club Name President Short desc

Sample data on specific clubs

3 Archery Club 6 To compete in

archery.

Students Database Table

ID First Name Last Name Email

Year of Birth

1 Peter Lee [email protected]

2 Jonathan Edwards [email protected]

3 Marilyn Johnson [email protected]

6 Joe Kim [email protected]

12 Haley Martinez [email protected]

14 John Mfume [email protected]

15 David Letty [email protected]

Sample data on students

Memberships Database Table

Club ID Student ID

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Club ID Student ID

1 1

1 2

1 14

2 1

2 3

2 5

2 6

3 1

3 6

3 12

3 14

3 15

Sample data on memberships

Events Database Table

Club ID Event name Date Attendance

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Club ID Event name Date Attendance

1 Cheese promo 1/10/2013 6

2 MLK Tournament 1/21/2013 17

3 January meeting 1/22/2013 12

2 January meeting 1/28/2013 10

Sample data on events

Normalization

When designing a database, one important concept to understand is

normalization. In simple terms, to normalize a database means to design

it in a way that: 1) reduces duplication of data between tables and 2)

gives the table as much flexibility as possible.

In the Student Clubs database design, the design team worked to achieve

these objectives. For example, to track memberships, a simple solution

might have been to create a Members field in the Clubs table and then

just list the names of all of the members there. However, this design

would mean that if a student joined two clubs, then his or her information

would have to be entered a second time. Instead, the designers solved

this problem by using two tables: Students and Memberships.

In this design, when a student joins their first club, we first must add the

student to the Students table, where their first name, last name, email

address, and birth year are entered. This addition to the Students table

will generate a student ID. Now we will add a new entry to denote that

the student is a member of a specific club. This is accomplished by adding

a record with the student ID and the club ID in the Memberships table. If

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this student joins a second club, we do not have to duplicate the entry of

the student’s name, email, and birth year; instead, we only need to make

another entry in the Memberships table of the second club’s ID and the

student’s ID.

The design of the Student Clubs database also makes it simple to change

the design without major modifications to the existing structure. For

example, if the design team was asked to add functionality to the system

to track faculty advisors to the clubs, we could easily accomplish this by

adding a Faculty Advisors table (similar to the Students table) and then

adding a new field to the Clubs table to hold the Faculty Advisor ID.

Data Types

When defining the fields in a database table, we must give each field a

data type. For example, the field Birth Year is a year, so it will be a

number, while First Name will be text. Most modern databases allow for

several different data types to be stored. Some of the more common data

types are listed here:

• Text: for storing non‐numeric data that is brief, generally under 256

characters. The database designer can identify the maximum length

of the text.

• Number: for storing numbers. There are usually a few different

number types that can be selected, depending on how large the

largest number will be.

• Yes/No: a special form of the number data type that is (usually) one

byte long, with a 0 for “No” or “False” and a 1 for “Yes” or “True.”

• Date/Time: a special form of the number data type that can be

interpreted as a number or a time.

• Currency: a special form of the number data type that formats all

values with a currency indicator and two decimal places.

• Paragraph Text: data type that allows for text longer than 256

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characters.

• Object: data type that allows for the storage of data that cannot be

entered via keyboard, such as an image or a music file.

There are two important reasons that we must properly define the data

type of a field. First, a data type tells the database what functions can be

performed with the data. For example, if we wish to perform

mathematical functions with one of the fields, we must be sure to tell the

database that the field is a number data type. So if we have, say, a field

storing birth year, we can subtract the number stored in that field from

the current year to get age.

The second important reason to define data type is so that the proper

amount of storage space is allocated for our data. For example, if the First

Name field is defined as a text(50) data type, this means 50 characters are

allocated for each first name we want to store. However, even if the first

name is only 5 characters long, 50 characters (bytes) will be allocated.

While this may not seem like a big deal, if our table ends up holding

50,000 names, we are allocating 50 * 50,000 = 2,500,000 bytes for

storage of these values. It may be prudent to reduce the size of the field

so we do not waste storage space.

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The Difference Between a Database and a Spreadsheet

Many times, when introducing the concept of databases to students,

they quickly decide that a database is pretty much the same as a

spreadsheet. After all, a spreadsheet stores data in an organized

fashion, using rows and columns, and looks very similar to a database

table. This misunderstanding extends beyond the classroom:

spreadsheets are used as a substitute for databases in all types of

situations every day, all over the world.

To be fair, for simple uses, a spreadsheet can substitute for a

database quite well. If a simple listing of rows and columns (a single

table) is all that is needed, then creating a database is probably

overkill. In our Student Clubs example, if we only needed to track a

listing of clubs, the number of members, and the contact information

for the president, we could get away with a single spreadsheet.

However, the need to include a listing of events and the names of

members would be problematic if tracked with a spreadsheet.

When several types of data must be mixed together, or when the

relationships between these types of data are complex, then a

spreadsheet is not the best solution. A database allows data from

several entities (such as students, clubs, memberships, and events) to

all be related together into one whole. While a spreadsheet does

allow you to define what kinds of values can be entered into its cells,

a database provides more intuitive and powerful ways to define the

types of data that go into each field, reducing possible errors and

allowing for easier analysis.

Though not good for replacing databases, spreadsheets can be ideal

tools for analyzing the data stored in a database. A spreadsheet

package can be connected to a specific table or query in a database

and used to create charts or perform analysis on that data.

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Structured Query Language

Once you have a database designed and loaded with data, how will you

do something useful with it? The primary way to work with a relational

database is to use Structured Query Language, SQL (pronounced

“sequel,” or simply stated as S‐Q‐L). Almost all applications that work with

databases (such as database management systems, discussed below)

make use of SQL as a way to analyze and manipulate relational data. As

its name implies, SQL is a language that can be used to work with a

relational database. From a simple request for data to a complex update

operation, SQL is a mainstay of programmers and database

administrators. To give you a taste of what SQL might look like, here are a

couple of examples using our Student Clubs database.

• The following query will retrieve a list of the first and last names of

the club presidents:

SELECT "First Name", "Last Name" FROM "Students" WHERE

"Students.ID" = "Clubs.President"

• The following query will create a list of the number of students in

each club, listing the club name and then the number of members:

SELECT "Clubs.Club Name", COUNT("Memberships.Student ID")

FROM "Clubs" LEFT JOIN "Memberships" ON "Clubs.Club ID" =

"Memberships.Club ID"

An in‐depth description of how SQL works is beyond the scope of this

introductory text, but these examples should give you an idea of the

power of using SQL to manipulate relational data. Many database

packages, such as Microsoft Access, allow you to visually create the query

you want to construct and then generate the SQL query for you.

Other Types of Databases

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The relational database model is the most used database model today.

However, many other database models exist that provide different

strengths than the relational model. The hierarchical database model,

popular in the 1960s and 1970s, connected data together in a hierarchy,

allowing for a parent/child relationship between data. The document‐

centric model allowed for a more unstructured data storage by placing

data into “documents” that could then be manipulated.

Perhaps the most interesting new development is the concept of NoSQL

(from the phrase “not only SQL”). NoSQL arose from the need to solve the

problem of large‐scale databases spread over several servers or even

across the world. For a relational database to work properly, it is

important that only one person be able to manipulate a piece of data at a

time, a concept known as record‐locking. But with today’s large‐scale

databases (think Google and Amazon), this is just not possible. A NoSQL

database can work with data in a looser way, allowing for a more

unstructured environment, communicating changes to the data over time

to all the servers that are part of the database.

Database Management Systems

To the computer, a database looks like one or more files. In order for the

data in the database to be read, changed, added, or removed, a software

program must access it. Many software applications have this ability:

iTunes can read its database to give you a listing of its songs (and play the

songs); your mobile‐phone software can interact with your list of

contacts. But what about applications to create or manage a database?

What software can you use to create a database, change a database’s

structure, or simply do analysis? That is the purpose of a category of

software applications called database management systems (DBMS).

DBMS packages generally provide an interface to view and change the

design of the database, create queries, and develop reports. Most of

these packages are designed to work with a specific type of database,

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but generally are compatible with a wide range of databases.

For example, Apache OpenOffice.org Base (see screenshot) can be used

to create, modify, and analyze databases in open‐database (ODB) format.

Microsoft’s Access DBMS is used to working with databases in its own

Microsoft Access Database format. Both Access and Base have the ability

to read and write to other database formats as well.

Apache OpenOffice.org Base

Database management system

Microsoft Access and Open Office Base are examples of personal

database‐management systems. These systems are primarily used to

develop and analyze single‐user databases. These databases are not

meant to be shared across a network or the internet, but are instead

installed on a particular device and work with a single user at a time.

Enterprise Databases

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A database that can only be used by a single user at a time is not going to

meet the needs of most organizations. As computers have become

networked and are now joined worldwide via the internet, a class of

database has emerged that can be accessed by two, ten, or even a million

people. These databases are sometimes installed on a single computer to

be accessed by a group of people at a single location. Other times, they

are installed over several servers worldwide, meant to be accessed by

millions. These relational enterprise database packages are built and

supported by companies such as Oracle, Microsoft, and IBM. The open‐

source MySQL is also an enterprise database.

As stated earlier, the relational database model does not scale well. The

term scale here refers to a database getting larger and larger, being

distributed on a larger number of computers connected via a network.

Some companies are looking to provide large‐scale database solutions by

moving away from the relational model to other, more flexible models.

For example, Google now offers the App Engine Datastore, which is

based on NoSQL. Developers can use the App Engine Datastore to

develop applications that access data from anywhere in the world.

Amazon.com offers several database services for enterprise use, including

Amazon RDS, which is a relational database service; and Amazon

DynamoDB, a NoSQL enterprise solution.

Big Data

A new buzzword that has been capturing the attention of businesses

lately is big data. The term refers to such massively large data sets that

conventional database tools do not have the processing power to analyze

them. For example, Walmart must process over one million customer

transactions every hour. Storing and analyzing that much data is beyond

the power of traditional database‐management tools. Understanding the

best tools and techniques to manage and analyze these large data sets is

a problem that governments and businesses alike are trying to solve.

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Data Warehouse

As organizations have begun to utilize databases as the centerpiece of

their operations, the need to fully understand and leverage the data they

are collecting has become more and more apparent. However, directly

analyzing the data that is needed for day‐to‐day operations is not a good

idea; we do not want to tax the operations of the company more than we

need to. Further, organizations also want to analyze data in a historical

sense: How does the data we have today compare with the same set of

data this time last month, or last year? From these needs arose the

concept of the data warehouse.

What Is Metadata?

The term metadata can be understood as "data about data." For

example, when looking at one of the values of Year of Birth in the

Students table, the data itself may be "1992." The metadata about

that value would be the field name Year of Birth, the time it was last

updated, and the data type (integer). Another example of metadata

could be for an MP3 music file; information such as the length of the

song, the artist, the album, the file size, and even the album cover

art, are classified as metadata. When a database is being designed, a

"data dictionary" is created to hold the metadata, defining the fields

and structure of the database.

The concept of the data warehouse is simple: extract data from one or

more of the organization’s databases and load it into the data warehouse

(which is itself another database) for storage and analysis. However, the

execution of this concept is not that simple. A data warehouse should be

designed so that it meets the following criteria:

• It uses nonoperational data. This means that the data warehouse is

using a copy of data from the active databases that the company

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uses in its day‐to‐day operations, so the data warehouse must pull

data from the existing databases on a regular, scheduled basis.

• The data is time‐variant. This means that whenever data is loaded

into the data warehouse, it receives a time stamp, which allows for

comparisons between different time periods.

• The data is standardized. Because the data in a data warehouse

usually comes from several different sources, it is possible that the

data does not use the same definitions or units. For example, our

Events table in our Student Clubs database lists the event dates

using the mm/dd/yyyy format (e.g., 01/10/2013). A table in another

database might use the format yy/mm/dd (e.g., 13/01/10) for dates.

In order for the data warehouse to match up dates, a standard date

format would have to be agreed upon and all data loaded into the

data warehouse would have to be converted to use this standard

format. This process is called extraction‐transformation‐load (ETL).

There are two primary schools of thought when designing a data

warehouse: bottom‐up and top‐down. The bottom‐up approach starts by

creating small data warehouses, called data marts, to solve specific

business problems. As these data marts are created, they can be

combined into a larger data warehouse. The top‐down approach suggests

that we should start by creating an enterprise‐wide data warehouse and

then, as specific business needs are identified, create smaller data marts

from the data warehouse.

Benefits of Data Warehouses

Organizations find data warehouses quite beneficial for a number of

reasons:

• The process of developing a data warehouse forces an organization

to better understand the data that it is currently collecting and,

equally important, what data is not being collected.

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• A data warehouse provides a centralized view of all data being

collected across the enterprise and provides a means for determining

data that is inconsistent.

• Once all data is identified as consistent, an organization can generate

one version of the truth. This is important when the company wants

to report consistent statistics about itself, such as revenue or number

of employees.

• By having a data warehouse, snapshots of data can be taken over

time. This creates a historical record of data, which allows for an

analysis of trends.

• A data warehouse provides tools to combine data, which can provide

new information and analysis.

Data warehouse process (top‐down)

Data Mining

Data mining is the process of analyzing data to find previously unknown

trends, patterns, and associations in order to make decisions. Generally,

data mining is accomplished through automated means against extremely

large data sets, such as a data warehouse. Some examples of data mining

include:

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• An analysis of sales from a large grocery chain might determine that

milk is purchased more frequently the day after it rains in cities with

a population of less than 50,000.

• A bank may find that loan applicants whose bank accounts show

particular deposit and withdrawal patterns are not good credit risks.

• A baseball team may find that collegiate baseball players with

specific statistics in hitting, pitching, and fielding make for more

successful major league players.

In some cases, a data‐mining project is begun with a hypothetical result in

mind. For example, a grocery chain may already have some idea that

buying patterns change after it rains and want to get a deeper

understanding of exactly what is happening. In other cases, there are no

presuppositions and a data‐mining program is run against large data sets

in order to find patterns and associations.

Privacy Concerns

The increasing power of data mining has caused concerns for many,

especially in the area of privacy. In today’s digital world, it is becoming

easier than ever to take data from disparate sources and combine them to

do new forms of analysis. In fact, a whole industry has sprung up around

this technology: data brokers. These firms combine publicly accessible

data with information obtained from the government and other sources

to create vast warehouses of data about people and companies that they

can then sell.

Business Intelligence and Business Analytics

With tools such as data warehousing and data mining at their disposal,

businesses are learning how to use information to their advantage. The

term business intelligence is used to describe the process that

organizations use to take data they are collecting and analyze it in the

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hopes of obtaining a competitive advantage. Besides using data from their

internal databases, firms often purchase information from data brokers to

get a big‐picture understanding of their industries. Business analytics is

the term used to describe the use of internal company data to improve

business processes and practices.

Knowledge Management

We end the chapter with a discussion on the concept of knowledge

management (KM). All companies accumulate knowledge over the course

of their existence. Some of this knowledge is written down or saved, but

not in an organized fashion. Much of this knowledge is not written down;

instead, it is stored inside the heads of its employees. Knowledge

management is the process of formalizing the capture, indexing, and

storing of the company’s knowledge in order to benefit from the

experiences and insights that the company has captured during its

existence.

Summary

We have learned about the role that data and databases play in the

context of information systems. Data is made up of small facts and

information without context. If you give data context, then you have

information. Knowledge is gained when information is consumed and

used for decision making. A database is an organized collection of related

information. Relational databases are the most widely used type of

database, where data is structured into tables, and all tables must be

related to each other through unique identifiers. A database management

system (DBMS) is a software application that is used to create and

manage databases and can take the form of a personal DBMS, used by

one person, or an enterprise DBMS that can be used by multiple users. A

data warehouse is a special form of database that takes data from other

databases in an enterprise and organizes it for analysis. Data mining is the

process of looking for patterns and relationships in large data sets. Many

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businesses use databases, data warehouses, and data‐mining techniques

in order to produce business intelligence and gain a competitive

advantage.

Study Questions

1. What is the difference between data, information, and

knowledge?

2. Explain in your own words how the data component relates to

the hardware and software components of information systems.

3. What is the difference between quantitative data and

qualitative data? In what situations could the number 42 be

considered qualitative data?

4. What are the characteristics of a relational database?

5. When would using a personal DBMS make sense?

6. What is the difference between a spreadsheet and a database?

List three differences between them.

7. Describe what the term normalization means.

8. Why is it important to define the data type of a field when

designing a relational database?

9. Name a database you interact with frequently. What would

some of the field names be?

10. What is metadata?

11. Name three advantages of using a data warehouse.

12. What is data mining?

Licenses and Attributions

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Chapter 4: Data and Databases (https://www.saylor.org/site/textbooks

/Information%20Systems%20for%20Business%20and%20Beyond.pdf)

from Information Systems for Business and Beyond by David T. Bourgeois

is available under a Creative Commons Attribution 3.0 Unported

(https://creativecommons.org/licenses/by/3.0/) license. © 2014, David

T. Bourgeois. UMGC has modified this work and it is available under the

original license.

© 2023 University of Maryland Global Campus

All links to external sites were verified at the time of publication. UMGC is not responsible for the

validity or integrity of information located at external sites.

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