The Best Data Monetisation Strategy for Maximising Revenue Potential

What is data monetisation?

Data monetisation is the process of using data to gain quantifiable economic benefit. Companies collect information about purchases, website visits, or user activity. After data collection, they use it to generate revenue by improving operations or selling data insights.

Here are some data monetisation examples from major companies:

• Google makes money from data through advertising, helping businesses reach specific audiences based on their interests and online behaviour.
• Amazon studies customer behaviour and preferences and then uses this data to personalise product recommendations, manage inventory, and sell insights to brands and vendors.
• IBM offers data-driven solutions such as analytics, cloud computing, and AI.
• Salesforce uses data monetisation in its CRM and cloud services.
• LinkedIn sells data through premium services for professionals and recruiters, providing networking opportunities and valuable insights.

These data monetisation examples show how companies leverage data to drive business growth and increase profitability.

Preparing for data monetisation

Assessing existing data and determining future collection

The first step in data monetisation is identifying valuable data sources within the organisation. Businesses need to assess what data they currently collect and determine if additional data points are worth tracking. IoT solutions and other digital technologies allow businesses to gather massive data that offers insights into consumer demographics, preferred products, sales performance, etc.

A key part of this process is conducting a data audit to evaluate which data holds value and which may require extra investment to become functional. Data monetisation relies on existing information, so organisations must capture all relevant data that could create revenue opportunities.

Gaining buy-in and establishing objectives

For data monetisation to work, company leaders must take charge. Senior executives need to explain its importance to key teams and make sure everyone understands that data is not just for cutting costs—it can also generate new revenue.

Before starting, the organisation must set clear goals for analysing data. Is the focus on saving money by improving efficiency, or is the plan to create a new service that generates income from data? Deciding this early is crucial because changing direction later can be expensive and inefficient.

 

Data monetisation strategies

• Internal data monetisation

Internal data monetisation is a concept used to boost your internal operations. You can use your data for internal purposes by establishing business intelligence to gain valuable insights that allow you to make data-driven decisions.

This is a way of indirectly gaining economic benefits from your data. Indirect data monetisation is not focused on monetising data or data analytics by selling it but rather on your business processes, products, and services, leading to cost savings, improved competitiveness, and overall efficiency as you streamline operations.

For example, a logistics company analyses delivery route data using logistics software and finds that certain routes cause frequent delays. By using this insight, they optimise routes, reducing fuel costs and delivery times. This saves money and improves efficiency without selling the data.

• External data monetisation

External data monetisation allows you to turn your internal data assets into a valuable product or service (raw data, insights, or even an analytics interface), ready to sell to the market and attract third parties—vendors, business partners, customers, etc.

It is a revenue source whereby you gain money directly by one-time selling or enabling third parties to access your stored data, providing them with insights/dashboards to drive their business decisions successfully by paying for them.

For instance, a telecom company collects anonymised data on mobile network usage. They package this information into reports on customer movement patterns and sell it to city planners and retail businesses to help them decide where to build new stores or infrastructure.

Monetising data through platforms and tools

Data marketplaces

Data marketplaces are online platforms where it is possible for companies to buy and sell data safely, list their data for sale, and set prices. The marketplace makes sure all sales are secure and helps connect sellers with interested buyers. Examples of data marketplaces include Dawex, Snowflake Marketplace, and AWS Data Exchange. With the help of these platforms and data providers, it is easier for companies to find valuable data or make money from the data they own.

Data monetisation platforms

Data monetisation platforms go beyond just listing data for sale. They provide advanced tools to refine, package, and manage data monetisation. Platforms like Monda and Harbr offer secure data sharing, subscription models, and analytics tools. They allow businesses to control access, track usage, and optimise data for sale while ensuring compliance and security.

Pricing models for data monetisation

Data-as-a-product

Data-as-a-Product (DaaP) means selling ready-to-use data packages. Companies prepare their data in an organised way, like a downloadable file, and sell it for a set price. It is a simple way to make money because the same data can be sold many times to different buyers without extra work or costs. This model works well for companies with high-value static datasets. It offers a direct revenue stream without ongoing maintenance beyond initial dataset preparation.

Customers appreciate this model because it provides a transparent pricing structure. They know precisely what they are paying for and can use the data as needed without ongoing costs.

Data as a Service

Data as a Service (DaaS) pricing model operates on a subscription basis, where customers pay for access to a data API. This model ensures a recurring revenue stream for businesses and allows flexibility in pricing. Companies can offer different subscription tiers based on data volume, frequency of updates, or additional features.

This model is simple to implement and often follows a business-to-customer (B2C) approach. If the data contains personally identifiable information (PII), it can be raw, aggregated, or anonymised. These data customers require a more tailored approach. However, since DaaS typically provides raw data, buyers must process it using analytics tools to extract value.

If the data contains personally identifiable information (PII), it can be raw, aggregated, or anonymised. However, since DaaS typically provides raw data, buyers must process it using analytics tools to extract value.

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Challenges in monetising data

Strategic challenges

1. Industry-specific difficulties. Businesses outside the tech sector often find it hard to monetise data because they may lack the right tools, skills, or experience. This makes it difficult for them to set up the systems needed to monetise data and turn it into revenue.
2. Defining the data value chain. With a wide array of data assets available, organisations must make crucial decisions about where they want to play in the data value chain. From collecting and storing data to analysing and selling it, each stage offers unique opportunities and risks. Without a clear strategy, companies may waste resources or miss key opportunities to monetise data.
3. Lack of clear objectives and resources. The major strategic challenge is the lack of clear objectives and data management. It is complicated to navigate data monetisation important process without defined goals. Additionally, organisations often lack resources such as technology, capital, or expertise.

Organisational challenges

1. Technology-driven decision-making. As companies and data providers seek to develop new data quality products, they may overcomplicate their offerings. In many cases, new products are packed with features that are not aligned with the actual needs of the end-users. These products may be technologically advanced but fail to resonate with the target market.
2. Moving up the value chain. Many data providers want to offer more advanced products, like data analytics or insights, to move up the value chain. However, it demands significant investments in new technology, infrastructure, data management and skilled staff. Organisations often find it challenging to build the expertise needed to go beyond simple data collection and provide higher-value services.
3. Lack of expertise in data monetisation. A successful data monetisation strategy is quite a complex process that demands specialised knowledge. Unfortunately, many companies lack the necessary skills or expertise in-house. Building the right team with the necessary expertise in data science, analytics, and monetisation strategies is crucial to success. Without this expertise, organisations may fail to identify how much value their data brings.

Best practices for data monetisation

Future-proofing analytics and business intelligence platforms

As data grows in volume and complexity, your analytics and business intelligence platform must support current and future data needs. As data grows in volume and complexity, the platform should adapt. Investing in scalable, flexible solutions ensures long-term success.

A future-proof platform evolves with organisational demands, such as integrating new data sources and accommodating emerging technologies. By staying ahead, businesses can extract more value from their data.

Comprehensive data monetisation

A strong data monetisation strategy covers key aspects like data sources, pricing, and delivery methods to maximise revenue. More data requires a more structured approach.

Evaluating the data's value and exploring various monetisation models is also necessary. A clear strategy helps organisations turn data into a valuable asset while ensuring compliance and security.

Measuring success in data monetisation

To achieve success in data monetisation, key performance metrics must be regularly tracked. Monitoring these indicators helps assess progress, identify opportunities for growth, and refine strategies for better results.

These metrics include:

• Revenue generated: Revenue is a clear measure of how well data monetisation efforts are performing. Businesses should track income from data sales, licensing agreements, and value-added services to understand their profitability. Regularly analysing revenue trends can reveal which strategies are working, highlight areas for improvement, and ensure long-term financial success. Analysing revenue trends over time can help identify successful strategies, highlight areas for growth, and ensure sustainable monetisation.
• Data usage: Monitoring how data is being used by customers or partners can reveal areas of high demand and inform the tailoring of data offerings.
• Customer satisfaction: Feedback from customers provides insights into whether the data products and services are useful and easy to use. High satisfaction means customers are getting value from the data, while complaints or low engagement indicate areas that need improvement.
• Return on investment (ROI): ROI compares the financial benefits of data monetisation with the costs involved. A high ROI means the strategy is delivering good returns, while a low ROI suggests that adjustments may be needed to improve efficiency and profitability.

By making data-driven decisions and continuously optimising their approach, institutions can maximise the value of their data assets and achieve their research and financial objectives.

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Creating revenue streams with data monetisation

Data monetisation is one of the key strategies that allows organisations to maximise revenue potential in today’s world. Companies that recognise its importance can develop effective approaches to monetise data and generate new revenue streams. As the demand for data-driven insights continues to grow, businesses that invest in strong data monetisation strategies will gain a significant competitive edge. Data monetisation works by turning business data into revenue opportunities. By leveraging customer data, collaborating with data providers, and utilising advanced data analytics and data management practices, organisations can achieve measurable business performance improvements, driving innovation and positioning themselves for long-term profitability in an evolving market.