Table of Contents

    Table of Contents

    Why Data Minimization is a Key Principle of Data Privacy

    Ian Tick

    Ian Tick

    Head of Content, K2view

    Data minimization is the practice of collecting and retaining only the personal or sensitive data that is deemed absolutely essential to an organization. 

    Table of Contents

    What is Data Minimization?
    Key Principles of Data Minimization
    Bringing Data Minimization to Your Organization
    Maximizing Minimization with Data Products

    What is Data Minimization? 

    A cornerstone of data protection, data minimization is the practice of collecting, processing, and storing the bare minimum amount of personal or sensitive data necessary to accomplish a particular task. By reducing the risk of data breaches and unauthorized access to confidential information, data minimization protects the privacy of individuals and facilitates compliance with data protection regulations. 

    Data minimization is frequently the first line of defense for organizations when it comes to protecting individual privacy. Endorsed by consumer protection legislation, including the California Consumer Privacy Rights (CCRP) act and the European Union's General Data Protection Regulation (GDPR), data minimization encourages organizations become more methodical, selective and intentional about protecting personal privacy.  

    The reasoning behind the concept is the less data an organization collects, the fewer chances there are of compromising personal privacy. Companies can build strong internal data minimization practices through access controls, data sharing agreements, and regular auditing. 
    In recent years, data minimization has become even more important for enterprises due to expanding legislative requirements governing the use of personal data, as well as growing privacy concerns. The frequency of data breaches, identity thefts, and other data-related problems has made people and authorities more mindful of how businesses handle personal or sensitive data. Alongside data anonymization tools, data masking tools, data tokenization tools, and synthetic data generation tools, data minimization is viewed as a proactive step to limit the possible exposure of confidential data and to safeguard people's basic right to privacy. 

    ​​​Beyond this, data minimization is cost effective. By implementing data minimization practices, enterprises won’t spend unnecessarily on data storage.​​ 

    Key Principles of Data Minimization 

    Data minimization doesn't mean “not collecting any personal or sensitive data.” Rather, it dictates that organizations may only collect such data for legitimate business purposes. As GDPR puts it, companies should only collect personal data that is "adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed.” 

    How do you know what is a legitimate reason for collecting data? GDPR lists 6 legal precedents for data processing:  

    1. Consent 

    2. Performance of a contract 

    3. Legitimate interest 

    4. Vital interest 

    5. Public interest 

    6. Legal requirement 

    Data minimization isn't simply about collecting less data. It also sets limits on what an organization can do with that data once it’s been collected, and who should have access to it.  

    After collecting data, companies should ensure that it’s not used for purposes that users haven’t explicitly consented to. Just because a user or customer has granted access to data for one reason, it doesn’t entitle an organization to use it for other purposes. 

    To really comply with data minimization principles, businesses need to rethink how they collect, process, store, and dispose of the personal or sensitive data they have access to. Before user privacy became a major issue, businesses weren't as concerned about what data they gathered, where it was stored, or how long it was kept. Today, data privacy management isn’t just about mitigating potential harm to consumers, but also to the business itself.  

    Bringing Data Minimization to Your Organization  

    Organizations must be more methodical and intentional about building data minimization principles into their processes. By incorporating the practice into the design of systems, processes, and products from the outset, they can take a proactive approach to reducing the amount of personal or sensitive data that’s been collected and stored – and limit the scope of data processing activities to what is really relevant and essential.  

    Here are a few steps to building a more mindful approach to data minimization: 

    • Select only essential data
      Restrict data collection to only what is required and sufficient. Start by identifying what is truly necessary to achieve your business objectives. For example, if your company ships products, it’s reasonable to require addresses, but not social security numbers.  

    • Limit data access
      Limit overall access to the data. In large organizations, data is frequently used by a number of different teams. Make sure that only data with immediate value be shared and that it is only shared with teams who need it. 

    • Curtail unnecessary or irrelevant data sharing
      Always ensure that data passed between teams only includes the absolute minimum for them to achieve their objectives. For example, if a credit card processor uses the ZIP code of an address to verify the owner of the card, the data should only include the ZIP code, and no other personal identifiers.  

    • Delete data that has fulfilled its specific purpose
      Erase data after it’s used. According to the GDPR, organizations "should collect only the personal data they really need, and should keep it only for as long as they need it." Review data retention patterns and assess the necessity of storing different types of personal data.  

    Maximizing Minimization with Data Products 

    Data minimization is particularly relevant for data analytics and big data processing, where enterprises often need to collect and process massive volumes of data with the aim of providing better service to their customers.  

    So how can companies ensure that the data they legitimately need to collect is still following best practices when it comes to user privacy?  

    ​​Many enterprises are now turning to a data product approach. ​This gives teams the ability to quickly create data products that organize and deliver data by business entities. Instead of querying massive, siloed databases, enterprises can instantly access a single business entity’s data whenever they need it. ​​ 

    By using individually encrypted Micro-Databases™, authorized users are only able to access the data related to a specific business entity, such as an individual customer, payment, order, or device. However, because all business entities are kept separate from one another, sensitive information isn’t centralized, rendering data breaches impossible.  

    A data product approach protects sensitive data while it's at rest, in use, or in transit – as well as in various environments such as production, testing, and analytics. It also allows for dynamic and static data masking, for both structured and unstructured data, while maintaining relational integrity. And internal access to data is better regulated by design, in line with data minimization best practices. 

    Minimize your data with K2view Data Product Platform.


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