Big data has become an important part of our daily lives, with organizations of all sizes and industries collecting and analyzing large amounts of data to gain insights to make better decisions and satisfy customers. However, the use of big data also raises some challenges and concerns, particularly around privacy and the potential misuse of personal data.
We encourage users to take control of their privacy in the following ways.
- Anonymize your connection using NordVPN.
- Create unbreakable passwords using 1Password.
- Take control of your personal data.
- Use pro-privacy browser plug-ins.
- Regularly clear your cache and delete your browsing history and cookies.
- Log out of websites when you’re not actively using them.
- Delete accounts you no longer use and try to stay away from big data companies.
Learn more about big data and privacy by reading our article below.
Big data and privacy are two concepts that are often seen as being at odds with each other. On the one hand, big data has the potential to provide great benefits to society, such as improved healthcare and personalized services.
On the other hand, the collection and analysis of large amounts of personal data can also raise concerns about privacy and the potential misuse of that data. In this article, we will explore the relationship between big data and privacy, and discuss some of the ways in which individuals can protect their personal data.
What is Big Data?
The term “big data” describes the enormous quantities of user data which is 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 started because (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 repositories are often so vast that it’s impossible to analyze them using traditional data analytics. However, if one analyses big data the right way, many interesting conclusions can be deduced.
For instance, big data is often used for large scale market research, including how users interact with software, websites, ads, and tracking their behavior.
In order 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 has to do with 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 within big datasets could even be combined to fill in any 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. For example, machines can be trained to recognize objects in images such as people and trees that can aid in autonomous driving.
Finally, big data is the reflection of 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
When big data is structured, it can be saved and presented in an organized and logical way, making the data more accessible and easier to comprehend. An example would be a company’s list of customers’ names, addresses, and contacts, all structured clearly in, for example, a chart or table.
Unstructured big data
Unstructured big data is not organized at all. It lacks a logical presentation that would make sense to the average human being. Unstructured big data doesn’t have the structure of, for instance, a table that denotes a certain coherence between the different elements of all the data available.
Hence this type of data is difficult to analyze and evaluate. Most enterprise datasets initially start out as unstructured big data.
Semi-structured big data
Semi-structured big data, as you might have guessed, has characteristics of both structured and unstructured big data. The nature and representation of this type of data aren’t completely arbitrary.
Yet it isn’t structured and organized enough to be used for a meaningful data analysis, either. An example would be a web page which contains specific metadata tags (extra information which isn’t directly visible in the text), for instance, because it contains certain keywords.
These tags effectively show specific bits of information, such as the author of a page or the moment it was placed online. The text itself is essentially unstructured, yet the keywords and other metadata it contains help to make it a somewhat suitable basis for analysis.
Classification based on the source of big data
Another common way to distinguish between different kinds of big data is by looking 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 would be books, pictures, 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 kind of big data, which is gathered and analyzed by (big) companies to improve certain processes in a business.
- Machines: This type of big data results from the ever growing number of sensors that are placed in machines. An example would be the heat sensor that is often built into computer processors. The data generated by machines can often be very complex, but at least this type of big data 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
Companies like Facebook collect user data and analyze it to determine what to display on your timeline. Of course, this is tailored to your personal wishes and interests. Facebook hopes this will get you to stay on their website for longer periods of time, and it also uses this information to show you relevant ads.
Almost all social media companies use big data in one way or another. They use cookies to track your preferences and behavior, and then serve relevant ads.
How ecommerce companies use big data
Amazon gathers information about the products you buy, browse, and tracks your search history. That way, Amazon can recommend products they think you’ll be interested in based on your previous purchases and as a result, increase customer satisfaction and their earnings.
Many ecommerce brands also track your browsing preferences across other sites. They use this information to create profiles about users, including their location and their interests.
This information is then used to serve relevant ads and to give you recommendations when you’re shopping online, including ads served on other social media platforms.
How transport companies use big data
Public transport companies can gather data about how busy certain routes are. They could analyze this data to decide, for example, which routes require additional buses or trains and vice versa.
While Google isn’t a transport company, it does use location data from Android smartphone users to give you traffic updates. Transport companies use this and collect first-party data to determine optimal routes.
How courier companies use big data
Companies like UPS use special software that was developed by big data platforms. For instance, this software helps UPS drivers avoid left-hand turns, which are costlier, more wasteful and more dangerous than right turns.
Supposedly, this system has already saved UPS millions of gallons in fuel, all thanks to big data. This is just one of many examples of how courier companies use big data.
Many companies also collect information about traffic on different routes at different times of the year, and then use that information for planning delivery routes.
How DNA testing companies use big data
Another interesting example of big data gathering are DNA testing companies such as MyHeritage DNA. They claim to “uncover your ethnic origins and find new relatives” through a simple DNA test.
Needless to say, this process involves a lot of data collection and big data analysis, making it another major player in big data collection and usage. Due to the nature of the service, these companies require clients to give explicit permission to track their lineage. They are also required by law to keep such information secure and encrypted.
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. We’ll be discussing the risks below.
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, contacts, home address, email, passwords and other information on places such as the dark web.
Often, this private data is stolen from official websites, companies, and other organizations. The bigger these data sets are, the more challenging (and rewarding) it becomes for hackers to try to obtain them. Needless to say, this causes great privacy risks.
Misuse of personal data
The practice of collecting personal data is becoming more and more widespread. So much so, the current data governance laws and regulations can’t keep up with the rapid developments in this field.
This leaves space for grey areas and uncertainties that can’t be solved by just studying the law. Important questions around data privacy concerns that arise include: what kind of data is allowed to be collected? About whom? Who should have data access?
Chances that sensitive personal information is included when collecting all this data is 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 pass on to 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 to changes within the company that result in higher margins due to increased customer satisfaction.
However, just like with any other normal dataset, an incorrect analysis of big data can have serious consequences, such as incorrect conclusions. These can in turn translate to ineffective or even counterproductive measures being taken.
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 there being a clear reason for analyzing them. In other words, it creates a huge database of raw information that has been gathered for processing later.
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 in the previous paragraph.
Collecting and saving big data with ill intentions
The collection of big data is used more and more often by companies, organizations, and government agencies so they can 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 by using the collected data.
Big Data and Privacy
As you’ve understood by now, big data collection comes with a lot of disadvantages and risks. Nevertheless, many companies and organizations still collect data at a huge scale.
This has consequences for our privacy protection. In this section, we discuss the different privacy concerns that come with big data.
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 are dependent on big data as well. They use this vast amount of information to track and investigate people they deem suspicious.
Of course, this means that there’s a lot of big data for cyber criminals to get their hands on for nefarious reasons due to poor data management. This can create all sorts of privacy and identity-related problems. One that comes to mind, is 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) is out there and is being collected, and that’s not a very comforting thought. Fortunately, there are ways of protecting data from the large-scale data mining going on.
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 greatly 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 abide all of their business to the GDPR. This is why Google, for example, now allows users to request a deletion of personal information.
However, privacy laws in the United States differ from state to state and don’t protect consumers as well as 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 protect privacy.
Fortunately, large-scale privacy infringements exposed by whistle blowers 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 await 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 reputable 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 5,000+ servers in over 60 countries. By using NordVPN, you have access to all these servers that will anonymize your connection and bypass geo-restrictions. Furthermore, NordVPN uses military-grade encryption to secure your data online from hackers and snoopers such as big technology companies and advertisers.
- Fast and large worldwide network of VPN servers
- Perfect for privacy and streaming
- Trusted by many, with over 14 million users
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, we advise 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, individuals have the right to access, correct, and delete their personal data held by organizations such as Google. For example, individuals can request a copy of the data that an organization has about them and can request that the organization correct or even delete their data.
If this sounds like an awful lot of work to you, you’re in luck. There are several data removal services out there that will contact big data companies and ask them to remove your data for you. DeleteMe is a great example. You can read more about them in our full DeleteMe review. To visit their website, click the button below.
4. Use browser plug-ins
Browsers such as Google Chrome, Mozilla Firefox and Brave come equipped with or are compatible with pro-privacy extensions. Plug-ins such as adblockers and anti-trackers will block out advertisers and keep online tracking companies at bay once installed on your browser.
Other ways to keep your data private
If you’d like to do more to improve your online privacy, here are a few extra tips:
- Regularly clear your cache and delete your browsing history and cookies.
- Log out of websites when you’re not actively using them.
- Delete accounts you no longer use and try to stay away from big data companies.
Taking these steps is a good start when it comes to safeguarding your online privacy and security. Keep in mind, however, that big data is collected in many different ways, and not just online.
In short, wherever you are and whatever you’re doing, you should always be vigilant and try to protect your (personal) data from big data collectors.
Conclusion: Big Data and Privacy
There’s no doubt that big data has many potential benefits, but it is also important that our personal data is protected and our privacy is not compromised.
To ensure the potential benefits of big data can be realized while preserving privacy, a balanced approach to its use including both government regulation and voluntary measures is needed.
By taking steps to protect personal data, we can ensure that the use of big data continues to be a valuable tool for businesses, governments, and other organizations, while also safeguarding our personal information. Here are steps you can take to safeguard your personal data:
- Anonymize your connection using NordVPN.
- Create unbreakable passwords using 1Password.
- Take control of your personal data using DeleteMe.
- Use pro-privacy browser plug-ins.
- Regularly clear your cache, delete your browsing history, and cookies.
- Log out of websites when you’re not actively using them.
- Delete accounts you no longer use and try to stay away from big data companies.
Got any questions? Take a look at our FAQ section below to see if we might have the answer for you. If not, you can always leave us a comment and we’ll answer your queries!
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 data sets, which can lead to errors and biases in the data and incorrect or unfair decisions. Read our comprehensive big data and privacy article to learn more.
There are several steps that individuals and organizations can take to prevent data collection and increase privacy, including:
- Anonymize your connection using a VPN.
- Create unbreakable passwords using a password manager.
- Take control of your personal data.
Read our big data and privacy article for a full list of measures users can take to protect their privacy.
The use of big data can have both positive and negative impacts on privacy. On one hand, big data can be used to improve decision-making, offer personalized services, among other benefits. On the other hand, the collection and analysis of large amounts of personal data can raise concerns about the potential misuse of that data, data security, and data quality.
