Overcoming Common Obstacles to Data Minimisation: Challenges and Solutions
Data minimisation is a fundamental privacy principle, promoting the collection, processing, and retention of only the minimal amount of personal data necessary to achieve specific purposes. While the benefits of data minimisation are clear—improved privacy, reduced risk of data breaches, and easier compliance with regulations—the path to implementation is often fraught with challenges.
We have written previously about data minimisation including:
In this blog post, we’ll explore some of the most common obstacles organisations face in adopting data minimisation practices and offer practical solutions to overcome them.
Balancing Business Needs with Privacy Requirements
Challenge:
One of the biggest challenges in data minimisation is striking the right balance between business objectives and privacy requirements. Organisations often collect vast amounts of data to drive innovation, improve customer experiences, and gain competitive advantages. However, this can conflict with the principle of data minimisation, which dictates that only essential data should be collected and stored.
Solution:
To address this challenge, organisations need to adopt a purpose-driven approach to data collection. Start by clearly defining the specific business objectives for which data is being collected. Then, assess whether the data being gathered is truly necessary to achieve these objectives. Conducting data mapping and implementing data mapping tools can help visualise data flows and identify areas where unnecessary data is being collected or stored.
From this, businesses can identify and eliminate non-essential data collection practices. Regular data audits and reviews can also help ensure that only relevant data is being retained.
Complex Data Ecosystems
Challenge:
Modern organisations often operate within complex data ecosystems, involving multiple data sources, platforms, and third-party vendors. This complexity makes it difficult to implement and enforce data minimisation practices across all data flows and touchpoints.
Solution:
A robust data governance framework is essential for managing data minimization in complex ecosystems (more on data governance here). This framework should include clear policies and procedures for data collection, processing, and retention across the entire organisation. Establishing strong vendor management practices is also crucial, ensuring that third-party partners adhere to the same data minimisation standards as the organisation.
Resistance to Change
Challenge:
Data minimisation often requires significant changes to existing processes and systems, which can be met with resistance from employees and stakeholders. This resistance may stem from concerns about the potential impact on business operations, the perceived complexity of new procedures, or simply a reluctance to change established practices. It may also stem from ‘data hoarding.’
Solution:
Overcoming resistance to change requires a combination of education, communication, and leadership. Begin by educating employees and stakeholders about the importance of data minimisation, highlighting the benefits in terms of privacy, security, and regulatory compliance. Clear communication from leadership is key to demonstrating commitment to these practices. Involving key stakeholders in the development of data minimisation strategies can also help build buy-in and reduce resistance. Providing training and support during the implementation phase will further ease the transition.
Lack of Technological Infrastructure
Challenge:
Implementing data minimisation effectively often requires advanced technological infrastructure, such as automated data deletion tools, anonymisation techniques, and data access controls. Organisations without these technologies may struggle to enforce data minimisation consistently and efficiently.
Solution:
Investing in the right technologies is crucial for enabling data minimisation. Start by conducting a technology assessment to identify gaps in your current infrastructure. Look for solutions that offer automation of data retention policies, data anonymisation or obfuscation, and secure data storage. Additionally, consider adopting Privacy-Enhancing Technologies (PETs) that support data minimisation while allowing for meaningful data analysis. When resources are limited, prioritise technology investments that offer the greatest impact on minimising data risks.
Difficulty in Identifying and Deleting Unnecessary Data
Challenge:
One of the practical challenges of data minimisation is identifying unnecessary data within large datasets and ensuring it is deleted or anonymised. This is particularly difficult when data is dispersed across multiple systems or when legacy data is involved.
Solution:
Using tools for data cataloguing (including discovery and classification) are essential for efficiently and effectively identifying unnecessary data. These tools are not without cost, but can scan and categorise data across different systems, making it much easier to determine what can be deleted, pseudonymised or anonymised and to build a sustainable data minimisation program.
See some of our previous posts on de-identification here:
Implementing automated data deletion policies can also help ensure that data is removed when it is no longer needed.
For legacy data, a thorough data audit is necessary to identify redundant or outdated information that can be purged. Regularly updating data retention policies will help prevent unnecessary data accumulation in the future.
Conclusion
While the challenges of implementing data minimisation are significant, they are not insurmountable.
By adopting a strategic, well-governed approach, supported by the right technologies and a culture of privacy awareness, organisations can overcome these obstacles and reap the benefits of minimised data collection, handling and retention. This not only reduces risks but also strengthens trust with customers, partners, and regulators, positioning the organisation for long-term success in a data-driven world.
Shelley Caverzan
Shelley brings a wealth of experience in data privacy, project management, change management, and IT service management across industries including education, banking, credit reporting and government. Her expertise includes designing and implementing privacy programs, conducting impact assessments, interpreting global regulations, and managing privacy incidents.