Introduction
In an era where personal data drives economic growth, digital services, and artificial intelligence, data minimisation and purpose limitation principles have emerged to become critical safeguards for individual privacy. Enshrined in leading data protection laws such as the General Data Protection Regulation (GDPR), these principles require that organisations collect only the strictly necessary data for clearly defined purposes and refrain from using it for unrelated or incompatible activities. While their conceptual clarity is widely accepted, the practical implementation of data minimisation and purpose limitation poses many significant challenges, especially in complex data ecosystems where business needs, legal obligations, and technological innovation frequently collide.
Defining the Principles
Data protection rules all around rely on the ideas of data minimisation and purpose limitation. Data minimising is gathering just the personal data required, which is relevant, and sufficient for a given use. Data must be gathered for particular, clear, and legal uses only; it cannot be further handled in ways incompatible with those uses under a purpose limitation. Their goals are to guarantee responsibility and openness in data use and stop overreach in data collecting.
These principles are codified in:
- GDPR (EU) – Article 5(1)(b) and 5(1)(c).
- India’s Digital Personal Data Protection Act, 2023 – Sections 4 and 6 require lawful, limited, and consent-driven processing.
Relevant Laws in India
Data privacy laws
Section 6 of the DPDP Act embodies the data minimisation principle. Any consent for processing digital personal data must be free, explicit, informed, unconditional, and unequivocal, with a clear affirmative action. Only when processing personal data for the designated purpose (i.e., "purpose limitation") may consent be requested, and it must be restricted to the personal data required for that specific purpose (i.e., "data minimisation"). The draft Digital Personal Data Protection Rules, 2025, released by the Indian government on January 3, 2025, incorporates the closely linked concepts of purpose limitation and data minimisation. In particular, according to the draft rules, the notice that must be given to a data principal along with the consent request must explicitly state the precise reasons for processing as well as a list of the goods and services that will be offered using the personal data that has been gathered.
The DPDP Act explains, using an example involving a telemedicine app, that even if users give permission for the app to access their "contact list," that permission will be void because telemedicine services do not require access to contact list data. A fine of INR 500 million could result from any violation of the data minimisation principle.
This principle is currently applied to the collection of sensitive personal data or information ("SPDI") under the Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information) Rules, 2011, to a certain extent. Companies are not allowed to collect SPDI unless a) it is collected for a legitimate purpose related to a function or activity of such a body corporate or any person on its behalf, and b) such collection is deemed necessary for that purpose.
Consumer protection laws
Additionally, it seems that India's consumer protection system incorporates the data minimisation principle. The Ministry of Consumer Affairs, for example, sent a notice to shops in May 2023 prohibiting them from requiring clients to disclose personal information, including cell numbers, as a condition of doing business. According to the Consumer Protection Act of 2019 ("CP Act"), the government had labelled such behaviours as "unfair contracts" and "unfair trade practices." Additionally, mandatory registration in "loyalty programs" as a condition of sale has resulted in sanctions from state consumer complaints redressal commissions.
Furthermore, in November 2023, the CP Act was used to notify the public of the Guidelines for Prevention and Regulation of Dark Patterns, 2023, also known as the "Dark Pattern Guidelines." According to the Consumer Protection (E-Commerce) Rules, 2020, a "platform" is defined as "an online interface in the form of any software, including a website or a part thereof and applications, including mobile applications." The Dark Pattern Guidelines, in general, aim to stop all "platforms" that regularly offer goods or services in India from engaging in any patterns or processes in their user interface or user experience interaction that could deceive or trick users. This includes all advertisers and sellers. Therefore, according to the Dark Pattern Guidelines, compelling a user to divulge personal information in order to complete a purchase or subscription (also known as a "forced action") has been classified as a dark pattern.
Advantages of Data Minimalisation
Data minimisation offers companies a number of advantages beyond respecting your clients' privacy rights and protecting their data from privacy threats.
- Risk Reduction for Data Loss: Maintaining a record of all the data you keep is one of the finest strategies to reduce data loss. It is far more difficult to integrate data monitoring throughout your systems if a company gathers more data than is necessary to meet goals.
- Effective Data Storage and Retrieval: The company and its employees need to be able to quickly find and access stored personal data when needed for operational and legal reasons. Retrieving and storing data is made easier with data minimisation and a strong data mapping system. This minimises the impact of a lack of visibility and the additional operational bandwidth needed to sort through extra data.
- Quicker Answers to Enquiries: Knowing where your customers' data is spread across your systems also makes it much easier to respond to their requests for data in conformity with your legal requirements.
If someone decides to exercise rights common to privacy frameworks, including the "right to delete personal information" or the "right to correct inaccurate personal information" under the CPRA, in that case, they must locate the data quickly.
- Increased Client Acceptance: Protecting customers' rights to data privacy also increases the likelihood that customers will stick with the company. The company will uphold its reputation and dedication to data privacy throughout the year, since data minimisation lowers the likelihood of privacy infractions.
- Preparedness for Regulation and Adherence: If the company conducts business with clients who live in an area where privacy guidelines are in effect, compliance with data privacy laws is essential. States in the US that have privacy laws in place include California, Virginia, Utah, Colorado, and Connecticut.
How to Implement a Data Minimisation Strategy
- Proportional Data Collection: To protect consumers’ privacy rights, it is crucial to only collect data that is proportional to the purposes for which it is being collected. In essence, the company must justify why it collects, processes, or stores consumer data and ensure that these purposes align with its business and data privacy objectives.
- Needs-Based Retention: Data minimisation also involves a strict data retention policy. This policy ensures the business only retains the data needed for specific purposes and only for as long as is needed. Once these purposes are met or the required retention period has passed, the data should be deleted.
- De-Identification and Anonymisation: De-identified data comprises that which “cannot be reasonably linked to an identified individual and is possessed by a controller who takes reasonable measures to ensure that a person cannot associate the data with an individual.” By de-identifying data, your business renders it useless in case of unauthorised use or disclosure, inherently protecting the privacy rights of customers better.
Practical Challenges in Implementation
While conceptually clear, the application of these principles in real-world settings is riddled with complexities:
- Identifying unnecessary data within large, dispersed datasets is difficult, especially when data is spread across multiple systems, databases, or includes legacy data.
- Balancing business objectives with privacy requirements is a major challenge, as organisations often want to collect more data for innovation and analytics, which can conflict with minimisation principles.
- Complex data ecosystems involving multiple sources, platforms, and third-party vendors make it hard to enforce consistent minimisation and purpose limitation practices across all data flows.
- Resistance to change from employees and stakeholders can hinder the adoption of minimisation strategies, often due to concerns about operational impact or reluctance to alter established processes.
- Lack of technological infrastructure, such as automated deletion tools, data anonymisation techniques, and access controls, limits the ability to consistently enforce minimisation and purpose limitation
- Ensuring compliance with purpose limitation requires organisations to clearly define, document, and communicate the reasons for data collection, and to update these declarations if data is to be used for new purposes.
Maintaining effective access controls and “point checking” mechanisms for purpose limitation is fragile, as it demands constant code audits and the physical separation of data assets to ensure each dataset is only used for its stated purpose.
Outdated IT systems and ingrained organisational processes can further complicate the implementation of effective data minimisation and purpose limitation strategies.
Conclusion
Navigating the principles of data minimisation and purpose limitation is a challenging yet essential aspect of responsible data governance. While legal frameworks like the GDPR and India’s DPDP Act offer a clear normative foundation, the real difficulty lies in implementing these abstract principles within daily business operations. Organisations must move beyond basic compliance checklists and adopt a proactive, privacy-first approach that integrates these principles into system design, business models, and organisational culture.
Embedding privacy into the core of digital systems, commonly known as "privacy by design” can help operationalise these principles. Tools like differential privacy, federated learning, and secure multi-party computation allow for data analysis while safeguarding individual privacy. Conducting Data Protection Impact Assessments (DPIAs) helps identify and mitigate risks at early stages. Policies tailored to specific data purposes and strict access controls further ensure responsible handling.
In a global digital environment, where data flows are increasingly complex and borderless, embedding the principles of data minimisation and purpose limitation into digital infrastructure is not just a legal necessity but a foundation for ethical and sustainable innovation. Doing so helps maintain regulatory compliance while building public trust an essential asset in the digital age.
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