DATA MINIMIZATION: Definition, Importance & How to Apply It

data minimization
Image Source: Archive360

The idea behind data minimization is to simply collect the minimum amount of information necessary to achieve a goal. When a company practices data minimization, they merely process (analyze) the minimal amount of data required to provide valuable insight. Additionally, without the authorization of the data subject (the person whose information was obtained), no other use of the gathered data should be made. Due to the fact that data is sometimes collected and stored indefinitely, businesses are encouraged by the GDPR to adhere to data protection principles, including data minimization. To be able to apply data minimization to your business, you’ll need a clear understanding of this principle and why your business needs it. 

What Is Data Minimization?

Data minimization, according to the International Association of Privacy Professionals (IAPP), is the practice of businesses to “only collect and retain that personal data that is necessary.”  

It describes the process of collecting only the minimum amount of personal data required to achieve a goal. The less data your company stores and maintains, the lower the risk of a data breach

However, data minimization is more than just reducing data collection. It also restricts the use of user information by your company. For instance, businesses should only collect data when it’s needed, keep it safe until it’s needed, and then dispose of it. The minimization principle encourages businesses to take a more thorough and deliberate approach to consumer privacy.

There was a time when the inclination was to hoard it all indefinitely. However, as the Internet of Things develops, businesses are faced with an increasing number of opportunities to acquire data, particularly private, personally identifiable information.

Some businesses may be holding on to the data in the hopes that it may be useful in the future, but the risks of data hoarding are analogous to those of physical hoarding: piles of useless rubbish that make it harder to discover what we need when we need it.  It’s not just inconvenient and risky, but also expensive.

Hence, smart data managers are moving away from a “save everything” mentality and instead adopting a data minimization philosophy, wherein only the most essential data is kept. 

Data Minimization Principle

While the principle of data minimization may appear simple, in practice it necessitates a shift in how organizations approach data collection, processing, storage, retention, and deletion. Businesses weren’t as wary of what data they obtained, where they stored it, or how long they kept it before user privacy became a key concern. Now more than ever, businesses need to manage user data in a way that protects both customers and the company itself from harm.

Put simply, adopting the data minimization principle is a great way to address privacy concerns. If your company doesn’t have any data, there’s nothing to mishandle. However, to eliminate unnecessary data, you must first become familiar with the information at hand. Following these guidelines will help your team take data minimization seriously.

Read Also: DATA WAREHOUSING: Definition, Types, Examples & Tools

The Little Blue Book of Privacy Design Strategies lists the following four practical actions as a place to start when starting to practice data minimization:

#1. Select

Data minimization does not preclude your company from collecting data. Instead, your organization must gather data for important business needs. In Europe, for instance, data collection is only permitted if it has a GDPR-compliant legal basis for processing. There are a total of six legal justifications for data processing under GDPR:

  • Consent.
  • Legal requirement.
  • Legitimate interest
  • Performance of a contract.
  • Public interest.
  • Vital interest.

Additionally, your company shouldn’t use that information for any other objectives for which customers haven’t given their approval, including targeted advertising. Users should be able to reasonably anticipate that your company will use their data in a certain way. By choosing only the information that is really necessary, your company can avoid collecting more data than it can handle and using it for secret objectives.

#2. Exclude

Limiting the amount of data your company collects will also prevent it from amassing more data than it can handle. Big Tech firms are beginning to understand how challenging it is to handle amassing massive oceans of data while maintaining customer privacy. Because of this, it’s critical for businesses to collect the minimum amount of information necessary to achieve a certain objective.

For instance, you might need to get the consumers’ addresses if your company distributes things to them. However, you wouldn’t have to ask for their social security number. This may seem like a simple example, but it highlights how crucial it is to determine which types of data are pertinent to your company, which are not, and why.

Your company and customers will be protected from privacy violations if you only collect the data that is absolutely essential.

#3. Strip

It’s possible that some data pieces still don’t need to be pushed farther downstream into the data structure after choosing the categories of data to gather and eliminating irrelevant data from the collection.

For instance, credit card processors frequently just require an address’s ZIP code to confirm card ownership. In a situation like this, privacy experts can strive to remove all information that could be used to identify the user from the address data sent to the backend, leaving only the ZIP code.

Specific GDPR fines have been levied in Europe for “non-adherence to the principles of data minimization.” Data minimization is still a great business practice to make sure that a company’s data operation is lean, efficient, and low-risk even though there aren’t yet comparable penalties under California’s CCPA or other U.S. privacy laws.

#4. Destroy

A crucial part of data minimization is data deletion ( data erasure). The GDPR mandates that businesses “should collect only the personal data they really need and should keep it only for as long as they need it.” This means that businesses must be thorough when determining the retention times for customer data.

The data should be properly deleted once the intended business use has been achieved since it has reached the end of its lifecycle. Even though efficient data erasure is frequently more difficult than merely wiping out cell values, your company should not keep it if it no longer needs the information. This holds true for backup copies as well; even if they need to be cleaned up at the conclusion of a specified retention period.

By following these data minimization principles, your company can stay in compliance with international laws. 

What Are the 3 Points of Data Minimization?

  • Adequate – enough to accomplish your stated purpose;
  • Relevant – is logically related to that purpose; and,
  • Confined to only what is necessary, – You only keep what is necessary for the given purpose.

What is the Benefit of Data Minimization?

To minimize data collection, organizations must carefully consider what information they need and why they need it. Your data systems will remain compliant and well-organized, and your company’s credibility will increase as a result. Users, on the other hand, now have more say over their information and protections under the new privacy rules.

The main advantages of adopting data minimization in a company include;

  • Lowers the cost of storing data
  • Improves the state of data security
  • Strengthens data privacy protections
  • Simplifies business processes
  • Upholds adherence to data regulations

What Is Data Minimization vs Anonymization?

Data minimization is the practice of collecting or retaining as little data as possible. Anonymization, on the other hand, refers to the alteration of data that a firm has with the goal of making re-identification unlikely.

What Is the Best Method to Prevent Data Loss?

Below are some of the finest ways to prevent data loss;

  • Do a backup of your files or data
  • Safeguard your hardware
  • Maintain an orderly computer
  • Educate your staff on the dangers of information leaks
  • Use software for antivirus and anti-malware protection
  • Create strong endpoint and device security policies  
  • Make sure to encrypt any private or important information
  • Update your program frequently.

What Is the Difference Between Purpose Limitation and Data Minimization?

Purpose limitation: Only use personal information for the intended reason for which it was obtained or for additional, compatible purposes. Data minimization means processing only the personal data that is necessary, pertinent, and sufficient for the purposes you have requested.

How to Apply Data Minimization to Your Business

#1. Narrow Data Collection

You must first decide what information your firm requires to perfectly operate. Meanwhile, if you want to analyze only the most relevant data—however you define that—you’ll need to refine your data collection methods. It is essential to restrict access to sensitive information in the data you do store. The information a secretary has access to about a customer is different from that of a salesperson or customer support agent. So, narrow it down. Ensure that each user gets access to only the information they require to complete their tasks.

#2. Verification and Vetting of Users

The majority of bulk data collection procedures operate under the presumption that users submit useful, pertinent data in large numbers. This is not actually the case. Many companies, from small startups to giant multinationals, unwittingly gather a lot of harmful data. Simply by existing on corporate systems, the data you hold could be fake or unconditioned, posing a risk to all parties. 

In order to eliminate unnecessary information, reliable data minimization strategies implement user verification and screening procedures. If a ridesharing service had such measures in place, they might notice an applicant with a history of violent crimes trying to use a fake identity. 

With these basic assessment procedures in place, businesses will only get information from reliable sources that are actually useful.

#3. Progressive Data Management

Many companies overlook the fact that user data will become outdated over time. This leads to databases being bloated with useless or false data. This puts stress on your IT systems and distorts the findings of any business analysis you conduct with the information.

Plans for data minimization that incorporate progressive review processes help users maintain data accuracy and freshness, thereby avoiding this problem. This method simplifies the development of report databases by making them more responsive to user input. As the amount of customer data rises, this helps the company save money and effort in the long term while also reducing risk.

#4. Strategic Erasure

Data minimization relies heavily on the practice of selective deletion for strategic purposes. In the modern, constantly evolving digital market, the usefulness of every piece of consumer data quickly expires. To maintain the usefulness and safety of their data, businesses should routinely remove old records from their servers. Deletion processes are an essential part of any data minimization strategy.

Business choices going ahead should always factor in the identification of new data types needed by the company and the removal of data types no longer useful to the business.

When you keep the information, you open the door to security breaches, unverified information, and other threats. You can’t completely avoid those dangers. Nonetheless, businesses that adopt effective data minimization measures can improve the efficiency of data collection processes, amass more useful information, and reduce exposure to risk.

What are Examples of Data Minimization?

The data minimization principle also stipulates that this collection of data must be necessary in order to fulfill the processing goal. For example, the goal of gathering biometric information as part of a fingerprint check at a building’s door is to stop unauthorized people from entering.

Data Protection Principles

There is a direct correlation between the fundamental principles of the GDPR and the numerous data protection and privacy regulations.  A deeper grasp of the data protection principles will help you in your efforts to adhere to the General Data Protection Regulation (GDPR). 

The General Data Protection Regulation (GDPR) is a set of rules for how personal information can and must be processed, based on seven privacy principles. Getting a handle on the GDPR’s seven guiding principles is the first step in mastering its laws and regulations. 

The 7 data protection principles;

  • Lawfulness, Fairness, and Transparency
  • Purpose Limitation
  • Data Minimisation
  • Accuracy
  • Storage Limitations
  • Integrity and Confidentiality
  • Accountability

Bottom Line

Organizations should only collect and retain the data they actually need, then erase the rest.  Data’s value rapidly declines, therefore, keeping it “just in case” is a risky course.

Data minimization is a fundamental privacy protection principle that can be useful to ensure that processes for collecting, storing, and erasing data are intentional and legal. Incorporating this procedure into all aspects of your data operations will not only help you safeguard the privacy of your consumers but also reduce any potential risks and high fines for your company.


Leave a Reply

Your email address will not be published. Required fields are marked *

You May Also Like