Big data is extremely large and complex sets of information collected from multiple sources, what you see online, the ads you get, the prices you’re offered, and even how services respond to you. While it helps companies improve products and personalize experiences, it also creates real risks: excessive data collection, profiling, data breaches, and the loss of control over your personal information.
The best way to protect your privacy is to build smarter habits and use the right tools consistently. Here’s what you need to do to protect your privacy online:
- Encrypt your internet traffic. Using a reliable VPN like NordVPN hides your IP address and keeps your online activity private, especially on public Wi-Fi.
- Use strong, unique passwords. A password manager such as 1Password makes it easy to generate and store complex passwords, reducing the risk of account takeovers.
- Limit data sharing. Review app permissions, avoid oversharing on social platforms, and think twice before filling in optional fields.
- Install privacy-focused browser extensions. Ad blockers and tracker blockers like Norton AV reduce how much data companies can collect about your browsing habits.
- Clear your digital footprint regularly. Delete cookies, cache, and browsing history to minimize tracking over time.
- Log out and clean up old accounts. Dormant accounts are easy targets for hackers, so delete what you no longer use.
- Be mindful of big data ecosystems. Many large platforms rely heavily on data collection, so diversifying your services can reduce exposure.
Eventually, you can control how much data you expose online. With the right awareness and practical steps, you can use modern technology to your advantage without compromising your privacy.
In this article, I’ll explore how big data and privacy intersect, the key risks involved, and practical steps you can take to better protect your personal information in an increasingly data-driven world.
What is big data?
The term big data describes the massive quantities of user data that are continuously being collected by different actors. An example would be all of the information Google collects from its users’ search queries.
Aided by technology, the phenomenon of big data is a recent development that began as (large) companies, such as Facebook, Google, and most government agencies, started to collect more data about their users, customers, and citizens than before.
Big data storages are often so vast that it’s impossible to analyze them using traditional data analytics. However, if one analyses big data properly, many interesting conclusions can be drawn.
For instance, big data is often used for large-scale market research, including tracking how users interact with software, websites, ads, and their behavior.
For a dataset to be considered big data, it should meet the following three criteria, also known as the three Vs:
- Volume: Big data is anything but a small sample. It involves vast data collection, resulting from long, continuous observation.
- Velocity: This refers to the impressive speeds at which big data is collected. Moreover, big data is often accessible in real time (as it is being gathered).
- Variety: Complex data sets often contain many different types of information. Data from large datasets could be combined to fill gaps and make the dataset even more complete.
Aside from these three V’s, big data has some other characteristics. For example, big data analytics is great for machine learning. This means it can be used to teach computers and machines certain tasks and patterns, such as recognizing objects in images, like people and trees, that can aid in autonomous driving.
Finally, big data reflects users’ digital fingerprints. This means it’s a by-product of people’s digital and online activities and can be used to build individual personal profiles.
Types of big data
Big data is often classified based on the type of data being collected. This method of organization is common and allows for data to be easily understood based on its characteristics and properties. There are three main categories of big data:
- Structured big data
- Unstructured big data
- Semi-structured big data
Structured big data
Structured big data refers to information organized in a clear, predefined format, making it easier to store, search, and analyze. This type of data typically fits into rows and columns, like in databases, spreadsheets, or tables, where each piece of information follows a consistent structure.
The organization of structured data makes it highly accessible and efficient to work with. For example, a company might store customer details, such as names, addresses, and contact information, in a neatly formatted table. This allows systems to quickly sort, filter, and analyze data, making it especially useful for tasks like customer management, reporting, and analytics.
Unstructured big data
Unstructured big data refers to information that doesn’t follow a predefined format or organization. Unlike structured data, it isn’t stored in neat rows and columns, which makes it harder to process, search, and analyze using traditional tools.
This type of data includes things like emails, videos, images, social media posts, audio files, and free-text documents, content that doesn’t fit into a fixed structure. Because of this, extracting useful insights from unstructured data often requires more advanced technologies, such as machine learning or natural language processing.
In reality, a large portion of enterprise data starts out unstructured. As a result, organizations invest heavily in tools and systems to organize and analyze it, turning raw information into something meaningful and actionable.
Semi-structured big data
Semi-structured big data is a hybrid of structured and unstructured data. It doesn’t follow a strict, table-like format, but it still contains some level of organization that makes it easier to analyze than completely unstructured data.
This type of data often includes elements like tags, labels, or metadata, small pieces of information that add context and structure. For example, a web page may contain unstructured text, but also includes metadata such as the author, publication date, or keywords. These elements help systems identify and categorize content.
Because of this partial structure, semi-structured data is more flexible than structured data while still useful for analysis. It plays an important role in modern data systems, where large amounts of mixed-format information need to be processed efficiently.
Classification based on the source of big data
Another common way to distinguish among different kinds of big data is to look at the source. Who or what has generated the information? Like the previous division, this classification method also consists of three different categories.
- People: This category concerns data generation caused by people. Examples include books, pictures, and videos, as well as personally identifiable information on websites and social media, such as Facebook, Twitter, Instagram, and so on.
- Process registration: This category includes the more traditional form of big data, which is gathered and analyzed by (big) companies to improve business processes.
- Machines: This type of big data results from the ever-growing number of sensors installed in machines. An example would be the heat sensor that is often built into computer processors. The data generated by machines is often very complex, but at least it is generally well-structured and complete.
What is big data used for?
There are several ways in which companies and organizations use big data. Many companies collect data directly, while others also purchase large datasets through independent brokers. Here are some examples.
How social media companies use big data
Social media platforms rely heavily on big data to shape what you see and how you interact online. Companies like Facebook collect large amounts of user data, such as your likes, shares, clicks, watch time, and interactions. This data is used to understand your preferences and behaviors, and is analyzed to personalize your experience. It selects what appears in your feed, what posts are prioritized, and which content you’re most likely to engage with. The goal is simple: keep you on the platform longer and make the experience more relevant to you.
At the same time, these data powers targeted advertising. Most social media platforms use tools like cookies and tracking technologies to monitor your activity both on and off the platform. This allows them to build detailed user profiles and serve ads that match your interests, browsing habits, and even purchasing behavior. In short, big data helps social media companies create more engaging and personalized experiences, but it also means your online activity is constantly being tracked and analyzed.
How e-commerce companies use big data?
Platforms like Amazon collect information about the products you browse, purchase, and search for. By analyzing this data, they can recommend items you’re likely to be interested in, enhancing customer convenience while boosting company sales.
Many e-commerce brands also track your activity across other websites to build detailed user profiles, including your location, interests, and shopping habits. This data allows them to serve personalized ads and product suggestions, not just on their own platforms, but also across social media and other online services.
In short, big data helps e-commerce companies deliver highly personalized shopping experiences, but it also means your browsing behavior is constantly tracked and analyzed to influence your choices.
How transport companies use big data?
Transport companies use big data to optimize routes, schedules, and overall efficiency. Public transit providers, for example, collect information on passenger numbers across different routes and times. By analyzing this data, they can determine which routes need additional buses or trains and which are over-served, reducing congestion and improving service.
Companies such as Google, which aren’t traditional transportation providers, also play a role. Google uses location data from Android smartphones to provide real-time traffic updates, which transport companies can leverage alongside their own data to plan optimal routes and improve overall traffic flow.
As a result, big data allows transport providers to make smarter decisions, improve efficiency, and offer more reliable services to passengers.
How courier companies use big data?
Companies like UPS use specialized software powered by big data analytics to plan delivery routes. One famous feature of this system is its ability to help drivers avoid left-hand turns, which are generally slower, less fuel-efficient, and more hazardous than right-hand turns. This optimization has saved UPS millions of gallons of fuel over time.
Beyond routing, courier companies analyze traffic patterns, seasonal trends, and delivery times to make smarter logistical decisions. By collecting and processing this data, they can plan the most efficient routes, predict delays, and ensure packages reach customers faster.
In short, big data helps courier companies save money, reduce environmental impact, and improve delivery reliability, turning raw information into actionable insights.
How DNA testing companies use big data?
DNA testing companies leverage big data to provide insights into ancestry, health, and genetic traits. Companies like MyHeritage DNA collect vast amounts of genetic information from customers’ DNA samples. By analyzing this data, they can reveal ethnic origins, identify potential relatives, and even provide health-related insights.
Since genetic data is sensitive, these companies require customers’ explicit consent before collecting and analyzing it. They are also legally obligated to store this information securely, using encryption and strict privacy measures to protect users’ personal and genetic information.
In this way, DNA testing services demonstrate how big data can unlock powerful insights, while also highlighting the importance of strict privacy controls when handling highly personal information.
Risks of big data
Big data can be useful in many cases. It provides us with tons of information that we can use to streamline processes. Hence, making companies more efficient and profitable and customers more satisfied.
However, this doesn’t mean that collecting and using big data is completely risk-free. Big data also causes privacy risks for users. Below you’ll see the risks of big data:
Data breaches
With everything we do online, there’s an inherent risk that our personal data could be stolen. The number of data leaks and breaches has increased drastically over the past few years.
There are numerous instances of cybercriminals selling personal and sensitive information such as full names, contact details, home addresses, email addresses, passwords, and other information on platforms such as the dark web.
Often, this private data is stolen from official websites, companies, and other organizations. The larger these data sets are, the more challenging (and rewarding) it becomes for hackers to obtain them. Needless to say, this causes great privacy risks.
Misuse of personal data
The practice of collecting personal data is becoming increasingly widespread. The current data governance laws and regulations can’t keep up with the rapid developments in this field.
This leaves room for grey areas and uncertainties that can’t be resolved by studying the law alone. Important questions regarding data privacy concerns that arise include: what kinds of data are allowed to be collected? About whom? Who should have data access?
The chances that sensitive personal information is included when collecting all this data are high. This is problematic, even when hackers and thieves aren’t at play. After all, privacy-sensitive data could be abused by anyone with ill intentions. This includes (malicious) companies and organizations.
You should know that even ISPs collect a lot of information about their users, which they sometimes sell or share with data brokers and exchanges.
Data quality
Many companies and organizations collect big data because they can use it for interesting analysis. This might give them important new insights into whatever they’re researching, for example, consumer habits. In turn, these insights and conclusions could translate into changes within the company that lead to higher margins due to increased customer satisfaction.
However, as with any other dataset, an incorrect analysis of big data can have serious consequences, such as incorrect conclusions. These can, in turn, translate into ineffective or even counterproductive measures.
Gathering irrelevant data
The use of big data is becoming increasingly common, and organizations are now aggressively collecting all sorts of data to gain a competitive advantage. This means large volumes of data are being collected without a clear reason to analyze them. In other words, it creates a large database of raw information gathered for later processing.
Companies are likely thinking it’s easy enough to gather all that data, so they might as well do it. Needless to say, this isn’t good for anyone’s privacy. It could even lead to irrelevant or “wrong” data being gathered and analyzed. If the conclusions drawn from this data analysis are used for decision-making, it could lead to the same ineffective measures mentioned previously.
Collecting and saving big data with ill intentions
The collection of big data is increasingly used by companies, organizations, and government agencies to make informed decisions. End-users generally don’t bother reading through complex agreements that detail how their information is collected and used either.
Needless to say, this has serious implications for their data security and online privacy. Everything they do online can be saved and viewed later. Moreover, big data collectors could easily influence and manipulate people’s decision-making using the data they collect.
Big data and privacy
There are many benefits to big data, but it also has significant risks. Despite these concerns, companies and organizations continue to collect massive amounts of personal information from users every day.
This widespread data collection has direct implications for our privacy. Personal information can be tracked, analyzed, and sometimes shared or sold without our full awareness. In this section, I’ll explain the key privacy concerns associated with big data, from profiling and targeted advertising to data breaches and unauthorized access, helping you understand the potential impact on your personal information.
Large-scale data collection
Lots of companies, including Google, Facebook, and Twitter, are heavily dependent on advertising models to sustain themselves and make a profit. To make these ads as effective as possible, these companies create detailed profiles on their users, especially taking their likes and interests into account.
Likewise, governments and secret services depend on big data. They use this vast amount of information to track and investigate people they deem suspicious.
Of course, this means there’s a lot of big data for cybercriminals to get their hands on for nefarious purposes due to poor data management. This can create all sorts of privacy and identity-related problems, such as identity theft.
Still, the possibilities that come with the collection in databases are much broader than this. These days, technology has become so advanced that it can combine data sets. This can be done in such a clever and crafty way that large corporations and organizations likely know more about you than you do!
Who you are, where you live, what your hobbies are, who your friends are: this is all information (for most people) that is out there and is being collected, and that’s not a very comforting thought. Fortunately, there are ways to protect data from large-scale data mining.
Laws on privacy
Privacy laws and regulations can protect us against privacy infringement, but only up to a certain extent. To make matters more complicated, privacy laws often differ between different countries and regions.
For instance, in Europe, a relatively strict consumer privacy law called the General Data Protection Regulation (GDPR) is in effect.
This law applies to all EU member states, although the details might differ per country. Many international companies have decided to comply with all of their business under the GDPR. This is why Google, for example, now allows users to request the deletion of personal information.
However, privacy laws in the United States differ from state to state and don’t protect consumers as well as those in the EU. Unfortunately, this is even true for the toughest privacy regulation in the US, the California Consumer Privacy Act.
In short, there’s no such thing as a global privacy law that applies to all big data collectors and protects privacy.
Fortunately, large-scale privacy infringements exposed by whistleblowers like Edward Snowden and Chelsea Manning have greatly increased awareness of the risks of big data. Of course, this is only a first step in improving current big data privacy laws.
Many internet users aren’t willing to wait for an improvement in big data privacy regulations – and rightfully so. Rather, they want to take action themselves by doing whatever they can to protect their privacy. Do you want to avoid becoming part of countless large data sets as well? There are several tips and tricks to help you on your way.
How to keep your data private
Big datasets affect your privacy and security. Big companies and cybercriminals can abuse these datasets that contain all sorts of (personal) information.
That’s why you should always make sure to leave as little of an online trace as possible. The following tips can help you accomplish this.
1. Use a reliable VPN
A virtual private network anonymizes your connection by replacing your IP address with another one. This makes it difficult for technology companies or your internet service provider to track your activities online. With a VPN, you’ll browse the internet anonymously and securely.
NordVPN is an industry-leading VPN with + servers in over countries. By using NordVPN, you have access to all these servers that will anonymize your connection and bypass geo-restrictions. Plus, NordVPN uses military-grade encryption to protect your data online from hackers, including big technology companies and advertisers.
2. Create strong passwords
Remembering different passwords is not easy for anyone, especially not when you have to make every single one unique and secure. As a result, most people tend to use weak passwords based on things they can remember such as their birthdays, names, phone numbers, and so on. To make it worse, most people use the same password across different devices and online services. All these can lead to serious problems when a data breach occurs.
To be safe, I recommend you to create and store strong, secure passwords using a password manager. 1Password is equipped with modern encryption for safeguarding all your passwords online. It also helps you create strong passwords that are not easily hacked.
3. Take control of your personal data
Because of GDPR, people have the right to access, correct, and delete their personal data held by organizations such as Google. For example, you can request a copy of the data that an organization has about you and can request that the organization correct or even delete your data.
Several specialized services can remove your personal data for you, rather than contacting big data companies on your behalf to request data removal. One well-known option is DeleteMe. You can learn more about how it works in our full DeleteMe review.
4. Use browser plug-ins
Modern browsers like Google Chrome, Mozilla Firefox, and Brave support a wide range of privacy-focused extensions. Installing plug-ins such as ad blockers and anti-trackers prevents advertisers and tracking companies from monitoring your online activity, giving you control over your personal data and reducing unwanted profiling while browsing the web.
Other ways to keep your data private
If you want to protect your online privacy, consider these practical tips:
- Clear your cache and cookies, and delete your browsing history regularly. This reduces the amount of data that websites and advertisers can track over time.
- Log out of accounts when not in use. Staying signed in makes it easier for platforms to collect ongoing activity data.
- Delete unused accounts and limit interactions with big data-driven companies. Fewer accounts mean less personal information floating online.
These steps are a solid starting point for safeguarding your online privacy and security. However, it’s important to remember that big data is collected in many ways, not just online. Whether you’re browsing, shopping, or using location-based services, your information can be tracked.
Conclusion: Big data and privacy
Big data is everywhere, shaping the way we shop, browse, and even connect with others. It brings plenty of benefits, smarter services, personalized experiences, and better decision-making for businesses and governments. However, protecting your privacy doesn’t have to be complicated. With a few simple habits and the right tools, you can stay in control while still enjoying the advantages of the digital world. Here’s how to protect your personal data:
- Anonymize your connection with a VPN like NordVPN to keep your online activity private.
- Create strong, unique passwords using a password manager like 1Password.
- Manage your personal data with services like DeleteMe to remove it from big data platforms.
- Install pro-privacy browser plug-ins such as ad blockers and anti-trackers.
- Clear your cache, browsing history, and cookies regularly.
- Log out of websites when you’re not actively using them.
- Delete old accounts and limit your interactions with big data-heavy companies.
By combining awareness, smart habits, and simple tools, you can keep your personal information safe. Privacy isn’t just about technology; it’s about taking small, consistent steps that put you in control.
FAQ
The top three big data privacy risks are misuse of personal data, data security, and data quality. Misuse of personal data can lead to a loss of control and transparency. Data breaches are a major challenge as they can expose personal data to potential misuse. Ensuring data quality is critical, but can be difficult with large datasets, which can lead to errors and biases and to incorrect or unfair decisions.
One of the most effective steps is to anonymize your internet connection with a VPN, like NordVPN. This hides your IP address and keeps your online activity private. Moreover, using a password manager such as 1Password to create strong, unique passwords for every account adds another layer of protection. This makes it harder for hackers to gain access to your information. You can further protect your privacy by reviewing what you share online, deleting old accounts, or using services that remove your information from big data platforms
On the positive side, it helps companies and organizations make better decisions, provide personalized services, and improve products and experiences that benefit users. At the same time, collecting and analyzing vast amounts of personal information can create serious privacy concerns. Your data could be misused, exposed in breaches, or handled in ways that aren’t transparent, and sometimes the quality or accuracy of the data itself can lead to incorrect assumptions or decisions about you. In short, while big data brings convenience and innovation, it also means our personal information requires careful protection.
