Chapter 3: Data and Databases • Database schemas redesigned • Data types added • SQL examples include output • NoSQL described • Data Dictionary re-ordered to column name • New section on “Why database technology?” • Differentiation of data, information, and knowledge • Section on Data models • Changed illustrative example of database tables and relationships.• Updated section on Business Intelligence to focus on the rise of analytics and data science. Includes a new “What is Data Science?” sidebar. 

Chapter 3: Data and Databases 

3.1 Introduction

You have already been introduced to the first two components of information systems: hardware and software. However, those two components 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 is the third component of an information system.

3.2 Data, Information, and Knowledge 

There have been many definitions and theories about data, information, and knowledge. The three terms are often used interchangeably, although they are distinct in nature. We define and illustrate the three terms from the perspective of information systems.

Information is processed data that possess context, relevance, and purpose. For example, monthly sales calculated from the collected daily sales data for the past year are information. Information typically involves the manipulation of raw data to obtain an indication of magnitude, trends, in patterns in the data for a purpose. Knowledge in a certain area is human beliefs or perceptions about relationships among facts or concepts relevant to that area. For example, the conceived relationship between the quality of goods and the sales is knowledge. Knowledge can be viewed as information that facilitates action. 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. Explicit knowledge typically refers to knowledge that can be expressed into words or numbers. In contrast, tacit knowledge includes insights and intuitions, and is difficult to transfer to another person by means of simple communications. Evidently, when information or explicit knowledge is captured and stored in computer, it would become data if the context or intent is devoid. 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. 

3.3 Big 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, a social media post, and an e-book. Recently, big data has been capturing the attention of all types of organizations. The term refers to such massively large data sets that conventional data processing technologies do not have sufficient power to analyze them. For example, Walmart must process millions customer transactions every hour across the world. Storing and analyzing that much data is beyond the power of traditional data management tools. Understanding and developing the best tools and techniques to manage and analyze these large data sets are a problem that governments and businesses alike are trying to solve. 

3.4 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, allow the user to put the data into context, and provide tools for aggregation and analysis. A database is designed for just such a purpose. 

3.5 Why Databases? 

As you can see from the two spreadsheets, this data management system has problems. The fact that “Student 4567 is Mary Brown, and her major is Finance” is stored more than once. Such occurrences are called data redundancy. Redundant data often make data access convenient, but can be harmful. For example, if Mary Brown changes her name or her major, then all her names and major stored in the system must be changed altogether. For small data systems, such a problem looks trivial. However, when the data system is huge, making changes to all redundant data is difficult if not impossible. As a result of data redundancy, the entire data set can be corrupted. 

(2) Violation of data integrity Data integrity means consistency among the stored data. We use the above illustrative example to explain the concept of data integrity and how data integrity can be violated if the data system is flawed. You can find that Alex Wilson received a grade in MKT211; however, you can’t find Alex Wilson in the student roster. That is, the two rosters are not consistent. Suppose we have a data integrity control to enforce the rules, say, “no student can receive a grade unless she/he has registered and paid tuition”, then such a violation of data integrity can never happen. (3) Relying on human memory to store and to search needed data. The third common mistake in data resource management is the over use of human memory for data search. A human can remember what data are stored and where the data are stored, but can also make mistakes. If a piece of data is stored in an un-remembered place, it has actually been lost. As a result of relying on human memory to store and to search needed data, the entire data set eventually becomes disorganized. To avoid the above common flaws in data resource management, database technology must be applied. A database is an organized collection of related data. It is an organized collection, because in a database, all data is described and associated with other data. For the purposes of this text, we will only consider computerized databases. 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. 

3.6 Data Models and Relational Databases 

3.7 Designing a Database 

3.8 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 data redundancy; and 2) ensure data integrity. In the School Database design, the design team worked to achieve these objectives. For example, to track grades, a simple (and wrong) solution might have been to create a Student field in the COURSE table and then just list the names of all of the students there. However, this design would mean that if a student takes two or more courses, then his or her data would have to be entered twice or more times. This means the data are redundant. Instead, the designers solved this problem by introducing the GRADE table. In this design, when a student registers into the school system before taking a course, we first must add the student to the STUDENT table, where their ID, name, major, and e-mail address are entered. Now we will add a new entry to denote that the student takes a specific course. This is accomplished by adding a record with the StudentID and the CourseID in the GRADE table. If this student takes a second course, we do not have to duplicate the entry of the student’s name, major, and e-mail; instead, we only need to make another entry in the GRADE table of the second course’s ID and the student’s ID. The design of the School database also makes it simple to change the design without major modifications to the existing structure. For example, if the design team were asked to add functionality to the system to track instructors who teach the courses, we could easily accomplish this by adding a PROFESSOR table (similar to the STUDENT table) and then adding a new field to the COURSE table to hold the professors’ ID. 

3.9 Data Types 

When defining the fields in a database table, we must give each field a data type. For example, the field StudentName is text string, while Enrollment Capacity is number. 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. • Boolean: a data type with only two possible values, such as 0 or 1, “true” or “false”, “yes” or “no”. • 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: this data type allows for text longer than 256 characters. • Object: this data type 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. For example, we can subtract the course capacity from the classroom capacity to find out the number of extra seats available. 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 StudentName field is defined as a Text (50) data type, this means 50 characters are allocated for each name we want to store. If a student’s name is longer than 50 characters, the database will truncate it. 

To the computer, a database looks like one or more files. In order for the data in the database to be stored, 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. 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. In enterprises the relational DBMS are built and supported by companies such as Oracle, Microsoft SQL Server, and IBM Db2. The open-source MySQL is also an enterprise database. 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. Apache OpenOffice.org Base (see screen shot) can be used to create, modify, and analyze databases in open-database (ODB) format. Microsoft’s Access DBMS is used to work 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. 

3.11 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 School database: The following query will retrieve the major of student John Smith from the STUDENT table: 

SELECT StudentMajor 

FROM STUDENT 

WHERE StudentName = ‘John Smith’; 

The following query will list the total number of students in the STUDENT table: 

SELECT COUNT (*) 

FROM STUDENT;

SQL can be embedded in many computer languages that are used to develop platform-independent web-based applications. An indepth 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 databases. Many DBMS, such as Microsoft Access, allow you to use QBE (Query-by- Example), a graphical query tool, to retrieve data though visualized commands. QBE generates SQL for you, and is easy to use. In comparison with SQL, QBE has limited functionalities and is unable to work without the DBMS environment. 

3.12 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. 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.

3.13 Sidebar: What Is Metadata? 

The term metadata can be understood as “data about data.” Examples of metadata of database are: 

• number of records 

• data type of field 

• size of field 

• description of field 

• default value of field 

• rules of use. 

When a database is being designed, a “data dictionary” is created to hold the metadata, defining the fields and structure of the database. 

3.14 Finding Value in Data: Business Intelligence 

With the rise of Big Data and a myriad of new tools and techniques 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 their own data, stored in data warehouses (see below), firms often purchase information from data brokers to get a big- picture understanding of their industries and the economy. The results of these analyses can drive organizational strategies and provide competitive advantage. 

3.14.1 Data Visualization 

Data visualization is the graphical representation of information and data. These graphical representations (such as charts, graphs, and maps) can quickly summarize data in a way that is more intuitive and can lead to new insights and understandings. Just as a picture of a landscape can convey much more than a paragraph of text attempting to describe it, graphical representation of data can quickly make meaning of large amounts of data. Many times, visualizing data is the first step towards a deeper analysis and understanding of the data collected by an organization. Examples of data visualization software include Tableau and Google Data Studio. 

3.14.2 Data Warehouses 

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. 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 non-operational 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, each database uses its own format for dates (e.g., mm/dd/yy, or dd/mm/yy, or yy/mm/dd, etc.). 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 topdown 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. 

3.14.3 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. 

3.14.4 Data Mining and Machine Learning 

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

One data mining method that an organization can use to do these analyses is called machine learning. Machine learning is used to analyze data and build models without being explicitly programmed to do so. Two primary branches of machine learning exist: supervised learning and unsupervised learning. Supervised learning occurs when an organization has data about past activity that has occurred and wants to replicate it. For example, if they want to create a new marketing campaign for a particular product line, they may look at data from past marketing campaigns to see which of their consumers responded most favorably. Once the analysis is done, a machine learning model is created that can be used to identify these new customers. It is called “supervised” learning because we are directing (supervising) the analysis towards a result (in our example: consumers who respond favorably). Supervised learning techniques include analyses such as decision trees, neural networks, classifiers, and logistic regression. Unsupervised learning occurs when an organization has data and wants to understand the relationship(s) between different data points. For example, if a retailer wants to understand purchasing patterns of its customers, an unsupervised learning model can be developed to find out which products are most often purchased together or how to group their customers by purchase history. Is it called “unsupervised” learning because no specific outcome is expected? Unsupervised learning techniques include clustering and association rules. 

3.14.5 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. This subject will be covered in much more detail in a later chapter – the chapter on the ethical concerns of information systems. 

3.15 Sidebar: What is data science? What is data analytics? 

The term “data science” is a popular term meant to describe the analysis of large data sets to find new knowledge. For the past several years, it has been considered one of the best career fields to get into due to its explosive growth and high salaries. While a data scientist does many different things, their focus is generally on analyzing large data sets using various programming methods and software tools to create new knowledge for their organization. Data scientists are skilled in machine learning and data visualization techniques. The field of data science is constantly changing, and data scientists are on the cutting edge of work in areas such as artificial intelligence and neural networks. 

3.16 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 creating, formalizing the capture, indexing, storing, and sharing of the company’s knowledge in order to benefit from the experiences and insights that the company has captured during its existence.