What is Data Minimization? Why Is It Important?

Companies are always collecting and analyzing data about their customers. Previously, organizations would indefinitely save all the data collected. However, with the growth of the internet, data has become easy to obtain, and customers share more information about themselves than ever before. 

Saving all this data means that these companies spend more money and time when looking for the specific information they need. To make their work easy, data managers are now collecting and saving only the data that is relevant and necessary for the intended purpose. This approach is called data minimization.

How To Achieve Data Minimization

Data mining is considered the source of “new knowledge.” The modern and automated data mining methods use superior techniques to mine relevant data from the complex vast amount of information, events, or people.

Actual data mining takes many forms. The techniques include

  • Classification analysis – This technique uses an existing structure to store data.
  • Cluster analysis – This technique lamps everything together, such as things, people, activities, to try and come up with meaningful subgroups.
  • Association analysis – This is where the co-occurrence of people or events are captured in large volumes for analysis.

Sources of Personal Data

The web is one of the largest data sources. Oblivious to their actions, internet users consistently leave behind a traceable trail of their online activities that data miners can easily have access to. 

A typical example is the use of the browser to search for answers online. Your browser history, blogs that you visit, or read online and items that you buy online leave behind valuable data regarding your personal preferences, topics of interest, and browsing patterns.

In the physical world, alternative sources of raw and unfiltered data come from technologies that we interact with daily. Some of these sources include GPS location trackers, watches, or trainer shoes that track your training regime. Personal data mining can focus on a solo item or in a cluster, and from one source or multiple sources to reveal a more profound outcome. 

It is essential to be clear about the kind of data you need and what you don’t need at the initial planning stage. Asking yourself the following questions will guide you in the process.

  • What is the intended purpose of the data to be collected?
  • Are there other alternative ways of achieving that purpose without collecting the data?
  • What’s the duration it will take to achieve the purpose before discarding the data?
  • Do I have express permission from the source of the data that I am collecting?

Companies can apply the following strategies to achieve the desired minimization.

  • Narrow and Specific Data Collection

Companies need to decide and specify beforehand what data will be adequate, necessary, and relevant to achieve their goal. For a successful data minimization approach, they must also narrow down the techniques used to collect the data.

  • Create User Verification and Customer Screening Processes

The assumption that customers and users will submit only relevant and necessary information during data collection is wrong. Therefore, companies need to create customer screening and verification processes to get rid of unnecessary data and only gather relevant information from verified and qualified users.

  • Establish Progressive Data Management

Companies should encourage their users to frequently update their information to avoid keeping stale and unusable data in their databases. Incorrect data could lead to wrong analysis and create an unnecessary overload on the IT infrastructure.

  • Delete Unused/Unusable Data

To achieve data minimization, companies must get rid of data that has outgrown its lifespan and usefulness. Outdated information should not be allowed to stay in the servers as they could pose a security threat.

oneDPO’s PurposeGraph can help you with data minimization. Our AI-based automated privacy platform can help you minimize data faster and with less effort. Learn more.

Benefits of Data Minimization

In addition to customer data protection, the following are some of the benefits companies realize by the minimization of data. 

  • Reduced Cost of Data Storage

Data storage costs money. The less it is, the less it costs to the benefit of the organization. With this in mind, it is advisable that companies only collect relevant data and store it for the duration of its usefulness.

  • Efficient Data Management

Data storage and retrieval are more manageable when there is less of it. It takes less time and creates confidence that the retrieved data is current and appropriate. When requests are sent out, data managers are sure to respond quickly.

  • Improved Customer Participation

Most customers are responsive to less personal questions and would only give their data to companies that assure them that their private information will not be stored in their servers.

  • Compliance with European Union Data Protection Act

This Act requires that businesses hold information about EU citizens to apply data minimization policies to protect such citizens.

How Mined Data Exchange Hands and Purpose Limitation

Data is a rich source of information with flatulating value depending on who needs it and its intended use. One purchase data from a third-party source that collects data, or receive it for free if its intended purpose is for the collective good. On occasion, one might not be granted access to data given its sensitive nature and value. 

The challenges of access to data minimization are the potential to abuse of available information. Limiting data collection and storage to safeguard privacy might be as helpful as limiting its misuse. Data protection recommendations should allow for stringent remedies against the misuse of data should it be discovered that there was privacy infringement. 

Advancement of data analytics bases their operations on re-purposing data for other use, a principle that goes against purpose limitation.

Purpose limitation is a principle of data collection protection that states that all collected and processed data should not be reused for an unintended purpose after it has served its initial purpose. Companies collecting data should adhere to the Data Protection Act guidelines to ensure proper use of data collected is observed. 

Since the opportunity to collect and centralize data is massive when companies collect data, the possibility of spinning off segments of collected data to analyze specific users is bound to happen.