What Is Data?

What is data? Can there be a single comprehensive definition for it? Or is there a way that many definitions can possibly be summarized?

In very informal terms, data enables a business or an individual to achieve desirable outcomes by knowing what we know and uncovering what we do not know, yet.

In more formal terms, data is known facts that have implicit meaning. In other words, data is a collection of facts, such as:

• Numbers or numerical values

• Quantities or measurements

• Recorded observations about objects

• Descriptions of objects

For example, details about employees such as last name, first name, age, number of years of experience, and current pay are data about the employee. For a type a of car produced, quantity, colors, and variations are data about the vehicle.

The Importance of Data

Basically, data is information. Information can be described as everything around us—everything we see, hear, or can sense by way of speech, touch, smell, taste, and so on. When we collect information and record it, it becomes data.

Data is one of the most valuable assets in many facets of life. Data has become the single most precious resource and has been leveraged very well by organizations of all sizes to their advantage—both in terms of monetization and in getting an edge over the competition. Data comes in various formats and forms, and you will get to know more about them in Chapter 3, “Data Types and Types of Data.” Moreover, where and how data is stored and utilized are important aspects of the data life cycle, which is covered in Chapter 1, “Understanding Databases and Data Warehouses,” and Chapter 2, “Understanding Database Schemas and Dimensions.”

Think about medicine, science, engineering, economics, and many streams of our daily life where data is being collected on an ongoing basis. The transactions you make with your bank using their (or a third-party) payment gateway and the purchases you make online reveal a lot about you and your persona to interested parties. Organizations want to know what products you browsed and bought, your spending capacity, what brands you like most, and many other facts that become apparent through the way you go about a purchase. Banks and e-commerce merchants would like to leverage this type of information in order to post advertisements that capture your interest.

In another realm, the information captured by performing medical experiments in a lab is vital to the success of new life-saving vaccines and drugs. Unless researchers know genetic information about a pathogen, they are not adequately empowered to perform research on the pathogen.

In addition, space exploration has given us a lot of data to work with, and today humans understand more than ever before about the vast space, galaxies, stars in our solar system, neighboring solar systems, and much more. Space probes such as Voyager I have provided immensely helpful insights about the vast space beyond our reach.

Not all data is created or acquired equally. Data often includes noise (unwanted information), gaps (missing information), and duplication (repeated or redundant information)—in other words, inconsistencies. Further, data can be structured, semi-structured, or unstructured in nature.

What Is the Importance of Data?

If some businesses did not have data at their disposal, they would not be able to function properly. For example, without the right data around demand and supply, a retail organization would not know how much stock to have at each store to meet demand.

For other businesses, data is a way of monetization and without appropriate data, they would be less effective. For example, a Facebook influencer would not be much of an influencer without the right data around the things they want to influence about. Subscribers would only follow and subscribe when they saw value in the information being given.

In some organizations, data actually is the business. For example, big entertainment houses run on metrics about what people like to see (drama, action, romance, comedy, and so on). Unless they know what their audience is aching for, they cannot deliver, and if they do not deliver, they lose business. For these organizations, data is essential, and without access to the right data insights, they will crumble.

These examples should give you an idea of the importance of data in today’s world. Many case studies and TV series have been created about data, and you can browse Google to find them.

What Are the Sources of Data?

Where is data generated? That is, what are the sources of data? The answer, surprisingly, is very straightforward: Data is generated by almost everything around us. Every single electrical and electronic system is capable of generating data. For example, data is generated by computers, vehicles, household appliances, fitness devices, communication devices, electrical grids, POS machines, cloud instances, RFID systems, and HVAC systems, just to name a few. Any analog or digital system is capable of producing data.

What data is useful to you? Is the data being generated by an electric grid of any use to you, or is the data from your own house’s smart power meters more important to you? Is the data being generated by your car’s tire pressure sensor more important than the data being transmitted by the radio station about the weather in the upcoming week? Getting to know crucial information by way of data is not just for commercial purposes but can very well be lifesaving.

The following are some of the potential sources of data for individuals and organizations:

• Personal electronic gadgets, such as phones, smart devices, and wearables

• Smart home electrical appliances

• Smart vehicles

• Smart meters

• Health devices

• E-commerce or banking transactions

• Website transactions

• Cloud data storage

• Clinical research

• Online and in-person surveys

• Protected health information (PHI)

• Personally identifiable information (PII)

• Data Expansion over the Past Few Decades

The digital footprint of data has grown incredibly in the past couple of decades. Popular search engines have made data much more accessible. Advancements in technology such as mobility and the advent of the cloud have increased the demand for data in individuals’ lives and in organizations’ decision making.

In the past, data sources were many, data was siloed, and not a lot happened without cooperative efforts of various groups working together. Now, with online and cloud-hosted databases and data warehouses, the availability of meaningful data has increased dramatically. As storage costs have come down over the past few decades—especially with the advent of the cloud in the early 2010s—the amount of data being generated and stored has grown exponentially.

Over the past few years, the flexibility and varied offerings of cloud platform providers have enabled organizations to build and leverage complex databases and data warehouses where data from numerous data sources can coexist. Private and public cloud architectures offer a lot more than could previously be accomplished from both data generation and consumption viewpoints. Your wearables can transmit directly to a cloud server leveraging wireless or mobile connectivity (LTE/4G/5G), and the data from hundreds of thousands of transmissions can be processed in the cloud, leading to insights into health metrics! This is just one application of generating viable data and making sense of it using some form of visualization. We will cover these topics in more detail later in the book.

Data Terminology

This section covers the basic terminology pertinent to data across a vast range of topics, including data analysis, data analytics, data mining, and data warehousing. The purpose is to make you comfortable with some key terms and their meaning in the context of real-life data collection, (pre)processing, storage, analysis, visualization, and many other aspects. Again, the topics covered here are introductory and are covered in more depth throughout this book.

To keep the examples in this section streamlined, we leverage a fictitious mining organization called Mining The World (MTW) to describe these terms:

• Dataset: A dataset is a group or structured collection of related data that shares the same set of attributes or properties as other data in the same dataset. For example, MTW can leverage geospatial locations stored in a comma-separated values (CSV) file for undersea mining operations.

• Data analysis: Data analysis is the process of examining available data artifacts (or datasets) to discover facts, relationships, insights, trends, or patterns in order to support better decision making. For example, MTW can leverage data analysis to analyze locations for future mining operations.

• Data analytics: Data analytics encompasses data life cycle management across different phases, such as data collection, cleansing, normalization, organization, analysis, storage, and governance. For example, MTW can run analytics on datasets available from multiple locations and derive meaningful information about the specific locations for mining the precious gems.

• Data governance: Data governance includes people, processes, and technologies to ensure the integrity of data and leading practices for data management. For example, MTW can appoint a chief data officer (CDO) to ensure that its data initiatives are driven strategically and that only relevant employees have access to raw or processed data.

• Data mining: Data mining is the process of analyzing massive volumes of data (or datasets) to detect patterns and relevant points that can be leveraged by organizations to drive unbiased and intelligent decision making. For example, MTW could leverage data mining to focus on proactive maintenance of field machinery based on the number of hours of usage and prevent loss of revenue due to breakdowns.

• Data model: A data model focuses on the relationships among different data types and the various ways in which data can be grouped and organized as well as its formats and attributes. For example, MTW could process multiple data models across oil and gas mining as well as precious gem mining to ascertain that the geographic areas of maximum impact in terms of mining capacity are explored.

• Data structure: A data structure is a format for organizing, processing, storing, and retrieving data. A common example is arrays where one or more items that have similar data type are stored.

• Data visualization: Data visualization is the process whereby data is represented in a graphical format to provide insights about key findings or data points. Common examples are pie charts, graphs, and maps generated based on data analytics. For example, MTW can generate a report summary with pie charts on successful efforts and funding for finding and digging new resources in mountains.

• Data warehouse: A data warehouse enables organizations to collate data sources and leverage the collected data repository to make informed business decisions by performing data analytics. For example, MTW can leverage an on-premises or cloud-based data warehouse to get insights into areas of investment where technology for mining can be improved with minimal disruption to ongoing mining operations. This would lead to massive savings based on reducing the time to mine and ship products to end consumers.

• Database: A database is an organized collection of information that can be queried against to yield results. MTW can have one or more (relational or non-relational) databases to store information where the queries can be run to extract relevant information, such as customer or employee records. Databases are an important source of information on customer or employee records and transactions.

Target Audience

The CompTIA Data+ exam assesses whether candidates have the competencies of an entry-level data professional with the knowledge equivalent of at least 18–24 months of experience in a report/business analyst job role, exposure to databases and analytical tools, a basic understanding of statistics, and data visualization experience.

This book is for professionals who have experience working with data across reports, dashboards, and visualizations; data processing; data manipulation; data analysis; databases, data warehouses, and data lakes; and more. This book does not cover everything in the data world, however. How could anyone do so in such a concise package? Despite its brevity, this book offers a lot of insights and a whole lot of test preparation.

Essentially, this book is for three types of people:

• Those who have been working with data analytics, visualization, and other aspects and want to achieve a vendor-neutral certification, and validate their knowledge

• Those who want to sharpen their understanding of data analytics and are fairly new to the data world

• Those who simply want a basic knowledge of what data is all about and how organizations leverage data to create more meaningful offerings for customers

For those of you in the first group, the CompTIA Data+ certification can have a positive career impact, increasing the chances of getting to the next level or securing a higher-paying job. It also acts as a steppingstone to more advanced certifications. For those in the second group, preparing for the exam serves to keep your skills sharp and your knowledge up to date, helping you remain a sought-after technician. For those of you in the third group, the knowledge in this book can be helpful in any career path you decide to take and can be beneficial to just about any organization you might work for—because almost every organization leverages data for driving decision making.

Regardless of your situation, one thing to keep in mind is that this book is written not just to help you pass the CompTIA Data+ exam but to teach you how to be a well-rounded data professional. While the main goal of this book is to help you become Data+ certified, we also want to share our experience with you so that you can grow as an individual.

About the CompTIA Data+ Certification

This book covers the material tested on the CompTIA Data+ DA0-001 exam, which you must pass to obtain the CompTIA Data+ certification. This exam is administered by Pearson Vue and can be taken at a local test center or online.

Passing the certification exam proves that you are an experienced problem solver and can support today’s data-first approach to solving some of the most complex analytical problems that organizations face.

Before doing anything else, we recommend that you download the official CompTIA Data+ objectives from CompTIA’s website. The objectives are a comprehensive bulleted list of the concepts you should know for the exam. This book directly aligns with those objectives, and each chapter specifies the objective(s) it covers.

For more information about how the Data+ certification can help you in your career or to download the latest objectives, access CompTIA’s Data+ web page at https://www.comptia.org/certifications/data#examdetails.

About This Book

This book covers what you need to know to pass the CompTIA Data+ exam. It does so in a concise way that allows you to learn the facts quickly and efficiently.

The book is composed of 18 chapters, each of which pertains to one or more objectives covered on the exam. At the beginning of each chapter you will find a list of exam topics that the chapter covers so you know what topics to focus on as you prepare for the exams. Chapter 18 discusses how to get ready for the Data+ exam and gives some tips and techniques for passing it.

The organization of this book is based on the order of the official CompTIA Data+ objectives. Typically, you will find one objective covered in each chapter. However, a chapter may cover two objectives, and in a couple of instances, an objective stretches across two chapters. The CompTIA objective or objectives covered are listed verbatim at the beginning of each chapter and in the subsequent major headings. This organization allows you to easily locate whatever objective you want to learn more about. In addition, you can use the index to quickly find the concepts you are after.

Regardless of your experience level, we don’t recommend skipping content. This book is designed to be read completely. The best way to study for the Data+ exam is to read the entire book.

Chapter Format and Conventions

Every Exam Cram chapter follows a standard structure and contains graphical clues about important information. The structure of each chapter includes the following:

• Opening topics list: The chapter begins with the CompTIA Data+ objective(s) covered in the chapter.

• Topical coverage: Each chapter explains its topics in a hands-on and a theory-based way. The book includes in-depth descriptions, tables, and figures geared toward building your knowledge so that you can pass the exam.

• Cram Quiz questions: At the end of each topic is a quiz. The quizzes and ensuing explanations are meant to help you gauge your knowledge of the subjects you have just studied. If the answers to the questions don’t come readily to you, consider reviewing individual topics or the entire chapter. You can also find the Cram Quiz questions on the book’s companion web page at www.pearsonitcertification.com.

• Exam Alerts, Fun Facts, and Notes: These are interspersed throughout the book. Watch out for them!

Additional Elements

Beyond the chapters, there are a few more elements that are helpful in your journey preparing for the CompTIA Data+ exam. They include:

• Practice Exams: Practice Exams are available as part of the custom practice test engine at the companion web page for this book. They are designed to prepare you for the multiple-choice questions that you will find on the real CompTIA Data+ exam.

• Cram Sheet: The tear-out Cram Sheet is located at the beginning of the book. It is designed to provide some of the most important facts you need to know for the exam onto one small sheet, allowing for easy memorization. It is also available in PDF format on the companion web page. If you have an e-book version, the Cram Sheet might be located elsewhere in the e-book; run a search for the term “cram sheet,” and you should be able to find it.

The Hands-on Approach

It is incredibly important that you apply what you are learning to real-world application of data analytics, processes, visualization, and more. This is the leading practice that we have recommended for years. It works! Practice as much as you can on databases, data warehouses, visualization dashboards, and whatever you can get your hands on.

In this book we give as many use cases and actual technology examples as possible, including SQL queries, screenshots of data system/software navigation, and so on. By referencing real-world applications of data processing, visualization, and analytics tools technology in actual scenarios, we infuse some real-world knowledge to solidify the concepts you need to learn for the exam. This hands-on approach can help you to visualize concepts better.

This book frequently refers to various websites that provide sample datasets, tools, and other materials to help you with your exam preparation.

Goals for This Book

We have three main goals in mind for preparing you for the CompTIA Data+ exam.

The first goal is to help you understand Data+ topics and concepts quickly and efficiently. To do this, we have tried getting right to the facts necessary for the exam. To drive these facts home, the book incorporates figures, tables, real-world scenarios, and simple, to-the-point explanations.

The second goal for this book is to provide you with an abundance of unique questions to prepare you for the exams. Between the Cram Quizzes and the practice exams, that goal has been met, and we think it will benefit you greatly. Because CompTIA reserves the right to change test questions at any time, it is difficult to foresee exactly what you will be asked on the exams. However, to become a good data-focused professional, you must know the basics and concepts; you can’t just memorize questions. Therefore, each question has an explanation and maps back to the chapter covered in the text.

The third goal is to give you real-world examples and to ensure that you get a broader understanding of topics than just getting prepared for the exam. This will be immensely useful in activities in your real life at your job.

Good luck in your certification endeavors. We hope you benefit from this book. Enjoy!

Sincerely,

Akhil Behl

Siva G. Subramanian