In today’s data-driven economy, businesses are sitting on a goldmine of information. However, simply owning data isn’t enough; the key lies in knowing how to monetise it effectively and efficiently. Data monetisation, the practice of extracting measurable economic value from data, can be broadly categorised into two types: Direct and Indirect monetisation. In this blog post, we will dive into these two areas, explore their key differences, and offer insights on how data and digital leaders can leverage both for maximum tangible value.

The Importance of Effective Data Monetisation

Before diving into the specifics of direct and indirect monetisation, it’s essential to understand why monetising data is critical. Think of Reddit for a moment. A company that has made a fortune with their data as it became the essential ingredient to the big success of Generative AI companies like OpenAI. But only because they’ve always treated their data as their most valuable resource. Data is no longer just a byproduct of operations—it’s a strategic asset that can drive growth, reduce costs, and enhance customer satisfaction.

Key drivers for data monetisation include:

• Increasing customer acquisition and retention
• Creating supplemental revenue streams
• Enabling competitive differentiation
• Improving operational efficiencies
• Reducing risks and fraud

Organisations often fail to see the full potential of their data because they view it narrowly—as something to be sold directly rather than a broader resource that can deliver value through various channels and different forms. Data itself isn’t value. Data + Domain Knowledge = Insights is value.

What is Direct Data Monetisation?

Direct data monetisation involves explicitly generating revenue from data assets. Thereby, the economic benefit of direct monetisation can be directly assigned to the data asset and usually occurs in the form of transactions. This typically means selling or licensing raw data or curated insights to third parties, enhancing existing products with analytics capabilities, developing new data enhanced products or using the data to strengthen existing business relationships. In this approach, businesses treat data as a standalone offering or data is directly integrated and enhances an existing product or service.

Examples of Direct Data Monetisation:

• Selling raw data to third-party aggregators or brokers
• Offering pre-defined or custom data/report subscriptions
• Licensing anonymised customer insights to suppliers
• Selling analytics solutions as standalone products
• Integrate data analytics as revenue driving feature in products or services

Key Characteristics of Direct Monetisation:

• Tangible revenue streams tied directly to data transactions
• Clear pricing models for data products
• Requires robust data governance and compliance structures

What is Indirect Data Monetisation?

Indirect data monetisation focuses on leveraging data internally to optimize the business and drive cost savings, improve efficiency, and enhance decision-making processes. This includes using the information to improve a process or product in a way that results in measurable outcomes, such as income growth or cost savings. Instead of selling the data itself, organisations use insights derived from data to optimise operations. As measuring the economic benefit of indirect data monetisation can be a very challenging task, it remains difficult to claim that the data is actually being monetised.

Examples of Indirect Data Monetisation:

• Enhancing supply chain efficiency through predictive analytics
• Reducing risks and fraud using data-driven insights
• Improving customer experience with targeted recommendations
• Supporting strategic decision-making with actionable insights

Key Characteristics of Indirect Monetisation:

• Benefits manifest as internal efficiencies or cost reductions
• Data supports the development of products or services rather than being sold or used as revenue driving feature
• Measurable outcomes such as reduced waste or increased customer loyalty

Key Differences Between Direct and Indirect Monetisation

Both strategies have unique advantages, and the choice between them depends on an organisation’s data maturity, industry, and strategic goals.

When to Use Direct vs. Indirect Monetisation?

When companies begin their data journey, focusing on indirect monetisation can deliver quicker wins. Initially, it is mostly advisable to prioritise internal use cases for data analytics while also working to improve data literacy within the organisation as well as among its clients and partners. This approach helps build trust, align on common definitions, and achieve quick successes with internal analytics—without the need to create new products or services straight away.

As the company becomes more mature in managing data, it can leverage the knowledge and trust built through its internal analytics efforts. This solid foundation can then be used to develop new products and services, enabling the company to diversify its revenue streams.

Direct Monetisation

• Established Trust and Literacy: Your organisation has already built trust and data literacy within the company.
• Proprietary Datasets: You possess unique, high-value datasets that set you apart from competitors.
• Infrastructure for Secure Data Sharing: The organisation has the systems in place to sell and deliver data securely and efficiently.
• Integration Capabilities: You have the expertise to embed data into your existing products and services.
• Market Demand: There is a clear and evident demand for your data assets in the market.

Indirect Monetisation

• Operational Efficiency Focus: The emphasis is on improving operational efficiency and achieving cost savings.
• Limited Internal Analytics: Your organisation lacks an internal intelligence suite to make data-driven decisions.
• Internal Improvements: The goal is to leverage data insights to drive enhancements within the company.
• Regulatory Constraints: Operating in a highly regulated industry may limit the ability to sell data directly due to compliance risks.

The Ideal Approach: Benefit From Using Both Strategies

The most successful organisations don’t see these two approaches as mutually exclusive but rather as complementary. For instance:

• A retailer may use customer data to optimise stock management (indirect monetisation) while simultaneously licensing anonymised trend data to suppliers (direct monetisation).

Building a Data Monetisation Strategy

1. Understand Your Data Assets: Conduct a data audit to identify valuable datasets.
2. Define Monetisation Goals: Are you aiming for direct revenue or operational efficiency?
3. Invest in Governance and Compliance: Ensure data security, privacy, and regulatory compliance.
4. Develop Data Products: Package data insights into market-ready offerings.
5. Measure Outcomes: Use KPIs to measure financial and operational benefits.

Emerging Trends in Data Monetisation

• Data Bartering: Exchanging data for strategic partnerships or other non-monetary benefits.
• Dark Data Utilisation: Unlocking value from unstructured or overlooked datasets.
• Analytics-as-a-Service: Offering analytics solutions to third parties.
• Embedded Analytics: Integrate Data Analytics directly into a product as revenue driving feature.

Final Thoughts

Data is one of the most valuable assets a business can possess, but its value depends entirely on how it’s utilised. Direct and indirect monetisation aren’t opposing forces—they are two sides of the same coin. Organisations that successfully integrate both approaches are better positioned to create new revenue streams, drive efficiencies, and gain a competitive edge.

Are you ready to start monetising your data? Whether you’re exploring direct or indirect pathways, the first step is understanding your data assets and aligning them with your business goals.

If you’re curious what hidden value your data holds, check out our Data Monetisation Lab – a 3-4 weeks long collaboration to develop up to three fully validated data monetisation business cases for your company.