ifsm 300 disc resp 2

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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. 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

Learning Resource

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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

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

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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, 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

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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 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 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.

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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.

3 Archery Club 6 To compete in

archery.

Sample data on specific clubs

Students Database Table

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ID First Name Last Name Email Year of BirthID 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

1 1

1 2

1 14

2 1

2 3

Sample data on memberships

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Club ID Student ID

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

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

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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 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.

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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 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.

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

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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

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

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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, 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.

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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

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.

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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.

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.

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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 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

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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.

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

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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:

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 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

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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 businesses use databases, data warehouses, and data-mining techniques in

order to produce business intelligence and gain a competitive advantage.

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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

Chapter 4: Data and Databases

(https://www.saylor.org/site/textbooks/Information%20Systems%20for%20Business%20

and%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.

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