Data monetisation: How to do so while avoiding legal trouble

Data is one of the most valuable assets in the modern economy, often compared to oil due to its potential for generating revenue. However, unlike oil, data can be replicated and monetised in multiple ways without depletion.

This article provides a structured, actionable approach to monetising data lawfully in the private sector. It covers legal and regulatory considerations, business models, contractual safeguards, technical controls, and ethical implications, ensuring that businesses can leverage data while maintaining compliance and consumer trust.

Understanding data monetisation

Data monetisation refers to the process of generating revenue from data assets.

Businesses can monetise data in several ways:

1. Direct monetisation – Selling or licensing raw or processed data.
2. Indirect monetisation – Using data to enhance operations, improve decision-making, or personalise services.
3. Embedded monetisation – Integrating data-driven insights into products and services.

A well-structured data monetisation strategy should align with legal frameworks, ethical standards, and business objectives.

Legal and regulatory considerations

Key data protection laws

Compliance is essential when monetising data, particularly personal and sensitive information. Some of the most relevant laws include:

• General Data Protection Regulation (GDPR) [EU] – Requires a legal basis for processing personal data and grants data subject rights.
• California Consumer Privacy Act (CCPA) [US] – Focuses on consumer rights, including opt-outs from data sales.
• Protection of Personal Information Act (POPIA) [South Africa] – Regulates data processing and ensures accountability.
• Personal Information Protection Law (PIPL) [China] – Imposes strict data localisation and cross-border transfer rules.
• Sector-specific regulations – Financial services, healthcare, and telecommunications industries often have additional requirements.

Key legal principles

• Consent and lawful basis – Businesses must obtain clear consent or justify data processing through legitimate interests.
• Anonymisation and pseudonymisation – De-identifying data minimises compliance risks.
• Cross-border transfers – Data localisation laws and standard contractual clauses (SCCs) may apply.
• Transparency and accountability – Companies must disclose data usage clearly and be accountable for its processing.

Business models for lawful data monetisation

Direct Data Monetisation Models

Data-as-a-Service (DaaS)

Companies provide access to structured datasets via APIs, dashboards, or reports.

• Example: Bloomberg sells financial market data insights to businesses.
• Compliance considerations: Ensure contracts define ownership, usage rights, and data protection obligations.

Data licensing and syndication

Businesses license data for third-party use while retaining ownership.

• Example: Airlines syndicate flight data to travel aggregators like Skyscanner.
• Best practice: Contracts should specify usage scope, redistribution rights, and liability clauses.

Data marketplaces and brokerage

Companies act as intermediaries, facilitating data trade under strict conditions.

• Example: Snowflake Data Marketplace allows businesses to securely exchange data.
• Risk: Selling personal data may trigger GDPR/CCPA compliance issues.

Indirect data monetisation models

Data-driven product and service enhancement

Businesses leverage data to optimise offerings and improve user experience.

• Example: Netflix refines content recommendations using viewing history.
• Best practice: Use legitimate interest as the basis for data usage and avoid unfair profiling.

AI and machine learning models

Data is used to train AI systems, but the raw data isn’t directly monetised.

• Example: OpenAI trains models using vast datasets and offers API access.
• Ethical consideration: Ensure AI models are free from bias and comply with Fair AI principles.

Embedded monetisation

Data insights are integrated into core product offerings.

• Example: Google Nest uses sensor data to optimise smart home automation.
• Regulatory compliance: Transparent terms of service should outline data usage.

Collaborative and shared models

Data cooperatives and partnerships

Organisations share anonymised data pools for mutual insights.

• Example: Open Banking initiatives facilitate secure financial data sharing.
• Legal consideration: Agreements should define data ownership, security standards, and liability.

Federated learning and privacy-preserving collaboration

AI models learn across distributed datasets without exposing raw data.

• Example: Google’s Federated Learning improves Android keyboards without sending user data to central servers.
• Security measure: Use homomorphic encryption or differential privacy to protect user data.

Key contractual and commercial considerations

Data licensing agreements

1. Scope of use – Define permissible use, duration, and renewal terms.
2. Data ownership – Clarify rights over raw and derived data.
3. Data quality and liability – Address accuracy, completeness, and liability.

Data protection agreements

• Specify obligations under GDPR, CCPA, POPIA.
• Define data breach response procedures and audit rights.

Revenue models

1. Per-usage pricing – Charge based on data access or API calls.
2. Subscription models – Recurring payments for continuous insights.
3. Tiered access models – Different data access levels for varied pricing.

Technical and security considerations

Data governance framework

• Data cataloguing – Maintain an inventory of data assets.
• Classification and minimisation – Store only necessary data.
• Access controls – Restrict data access based on roles.

Security and anonymisation techniques

• Encryption – Secure data at rest and in transit.
• Tokenisation and differential privacy – Prevent re-identification risks.
• Secure APIs – Implement authentication and rate-limiting measures.

Ethical considerations and reputation management

• Transparency and fairness – Avoid deceptive or exploitative data practices.
• Bias and discrimination prevention – Ensure AI and analytics models do not reinforce bias.
• Consumer trust – Ethical data handling builds brand reputation and long-term success.

Future trends and opportunities

• Regulated data markets – Emerging frameworks for ethical data exchanges.
• Privacy-enhancing technologies (PETs) – Zero-knowledge proofs, secure multiparty computation.
• Synthetic data – AI-generated datasets to mitigate privacy risks.
• AI governance and data ethics – Stricter compliance requirements for automated decision-making.

Actionable steps for businesses

1. Conduct a data audit – Identify legal and compliance risks.
2. Select a monetisation model – Choose direct, indirect, or hybrid approaches.
3. Implement data governance – Ensure security, anonymisation, and responsible data usage.
4. Draft robust contracts – Clarify rights, revenue-sharing, and legal obligations.
5. Monitor regulatory updates – Stay ahead of changing laws and compliance requirements.

How ITLawCo can help

Navigating the complexities of lawful data monetisation requires a blend of legal expertise, technical understanding, and strategic insight. ITLawCo provides tailored advisory services to help businesses:

• Develop compliant data monetisation strategies aligned with global regulations.
• Draft and negotiate data licensing agreements to protect your interests.
• Implement robust data governance frameworks to mitigate risks.
• Advise on cross-border data transfers and compliance with international standards.
• Ensure AI and machine learning models meet ethical and regulatory requirements.

Whether you are exploring new revenue streams through data monetisation or seeking to optimise existing data assets, ITLawCo offers practical, actionable guidance to help you stay compliant while unlocking value. Contact us today to discuss how we can support your data-driven initiatives.