Cybersecurity vs. Data Analytics: Similarities and Differences

 




Infrastructure was once the primary focus for information technology (IT) professionals, but as technology has become more deeply ingrained into society, additional disciplines have emerged. Two of these, cybersecurity and data analytics, are very different in many ways, but they intersect in several others. Examining these two fields, and their similarities and differences, is essential for choosing the right career path.

An online graduate program in cybersecurity can be a good choice for aspiring IT professionals who are considering a career in cybersecurity vs. data analytics and are interested in protecting the systems that drive the modern globalized world.
What Are Cybersecurity and Data Analytics?
Cybersecurity and data analytics are two different disciplines that fall under the IT umbrella. Data analytics is a branch of data science that focuses on extracting actionable insights from data, and cybersecurity focuses on safeguarding data.
Understanding Data Analytics
Data analytics focuses on the collection, analysis, and interpretation of often vast pools of data in an effort to discover actionable trends or insights that can positively impact business decisions. More than just a process, data analysis is a powerful tool that makes strategic planning possible, and it occurs in six steps:

 	
Defining Objectives: Every analysis seeks to meet an objective or answer a question, and defining those objectives early on is important for efficiency. This step also involves defining the metrics required to measure the outcomes of the analysis.
 	
Collecting Data: The next step involves collecting data that’s relevant to the defined objectives. Data collection can use various methods, including surveys or observations. It may even come from existing databases.
 	
Cleaning Data. Cleaning (or cleansing) data involves carefully checking for errors or irrelevancies and then removing that information from the data pool. When the data is more reliable, so are the insights it provides.
 	
Analyzing Data. Analysis can only occur once the data required for the objective has been collected and cleansed. Data analysts use one or more mathematical techniques or sophisticated software to find trends or patterns.
 	
Interpreting and Visualizing Data. Data visualization may involve translating the output of data analysis into visual formats, such as charts, graphs, and reports that others can easily share and interpret.
 	
Telling a Story. Finally, data analysis involves telling a story or creating a narrative that makes the findings easy to understand. The narrative often accompanies the visual aids and is geared toward nontechnical decision-makers within the company.

Understanding Cybersecurity
Cybersecurity protects data. This involves safeguarding against data leaks and preventing unauthorized personnel — including those within an organization and potential cybercriminals — from accessing secure networks containing sensitive or confidential information. Every modern business uses some sort of IT, and while some depend more heavily on the digital realm than others, protecting the data is essential to any organization’s success. There are several types of cybersecurity, including the following:

 	
Critical Infrastructure Security: Critical infrastructure security protects computers and networks that keep organizations operational. It also encompasses the technologies, processes, and protections companies have established to keep their critical infrastructure secure.
 	
Network Security: Network security is more specific and focuses primarily on firewalls, virtual private networks (VPNs), access control, antivirus and antimalware technology, and other safeguards that keep networks safe.
 	
Endpoint Security: An endpoint is any device connected to an organization’s network. This may include desktop computers and printers, and in contemporary settings, it may also include TVs and mobile devices.
 	
Application Security: Application security is a set of processes that keeps individual applications safe from cyber attacks. Examples are fixing bugs in an application’s code or writing new code to implement two-factor authentication.
 	
Information Security: Information security protects data. It applies to electronic health records (EHRs) in doctors’ offices and hospitals, customer and client information in large companies, and other forms of proprietary or personally identifiable information (PII).
 	
Cloud Security: Cloud-based data passes from different endpoints and applications regularly, exposing it to security risks. Cloud security keeps the data and applications safe from attacks.**
 	
Mobile Security: Mobile security is specific to smartphones; smartwatches; tablets; and other devices that contain, send, and receive personal information regularly.
 	
Internet of Things Security: The IoT connects high-tech devices, such as smart TVs; heating, ventilating, and air-conditioning (HVAC) systems; lights and controls; and refrigerators. Even these seemingly simple devices can provide an entry point for cybercrime.

Where Do Cybersecurity and Data Analytics Intersect?
Cybersecurity and data analytics are two very different disciplines, but they both play distinct and substantial roles in managing and protecting digital data. Understanding how the two fields work together in data privacy and compliance, behavior analytics, and risk management gives a clearer picture of the similarities.
Analyzing User Behavior
Data analysts can create baseline models to simulate how different user groups, including a target market, may behave in given scenarios. Cybersecurity professionals also use behavior analysis to predict the likelihood of a security breach.
Managing Risk
While data analysts quantify and assess risks by looking at historical data, cybersecurity professionals use risk assessments that come directly from data analysts. They do this to ensure the most useful allocation of security resources and the proper prioritization of responses and measures. For example, a clinical data analyst may look through health records for ways to improve patient outcomes while a cyber threat analyst works to protect those records from breaches.
Achieving Data Privacy and Compliance
Data analytics can measure and monitor network usage, including access to specific data pools, to provide insights into regulation and protocol compliance. Cybersecurity professionals use encryption and access control measures to further improve compliance.
Differences Between Cybersecurity and Data Analytics
Cybersecurity and data analytics have many differences. From their specific purposes to the techniques used to achieve the desired outcomes, five key considerations highlight these differences.
1. Purpose of Each Discipline
The purpose of cybersecurity is to protect data, networks, and associated systems from unauthorized access, including cyber attacks and data breaches. The purpose of data analytics, on the other hand, is to use data to uncover insights that can help organizations make better business decisions.
2. Key Activities
Cybersecurity professionals are responsible for designing, implementing, and maintaining the security measures that protect networks. They also monitor for threats, check for vulnerabilities in networks and systems, and respond to incidents when they occur. Data analysts oversee the collection, processing, and analysis of various data pools. They then analyze that data to find patterns and trends to use for actionable insights.
3. Techniques and Tools
Cybersecurity specialists are well versed in using tools such as firewalls, security protocols, and various systems to detect unauthorized network access and data breaches. Data analysts rely on statistical methods and models; machine learning; algorithms; and a wide range of visualization tools, such as graphs and charts, for data analysis and visualization.
4. Examples and Applications
Cybersecurity is essential for any organization that collects, stores, or uses sensitive online data, such as addresses, credit card numbers, or health records. It prevents unwanted access to these and other forms of data. Conversely, data analysis can be used to analyze customer behaviors or markets, providing critical intelligence that can help businesses grow.
5. Desired Outcomes
Cybersecurity keeps data and networks confidential, reducing downtime and potential data loss. Data analysis allows organizations to leverage data in new ways, which can be useful for product development and marketing purposes.
Education, Skills, and Experience Requirements
The education, skills, and experience required to enter the IT field as a data analyst or cybersecurity specialist are quite similar, but a few important differences should be kept in mind. Degree programs, certain key skills, and the level or type of experience may vary between the two fields.
Education and Experience Requirements
A bachelor’s degree is required to pursue an entry-level career as a data analyst or cybersecurity specialist, but as technology evolves and becomes more complex, employers may seek candidates with graduate-level education.

For data analysts, a master’s degree in data analytics, applied analytics, or data science provides in-depth education and can prepare professionals for high-paying, high-demand roles. For cybersecurity professionals, such as cybersecurity engineers , a master’s degree in cybersecurity teaches the relevant skills required to help organizations safeguard their data and systems.