We’re about halfway through data monetisation week 2025 and our 5-day blog series on how to unlock more value and revenue from your data.
We’ve covered quite a lot of ground already, with a few items left on the agenda:
Day 1: What is Data Monetisation?
Day 2: Data Monetisation Use Cases
Day 3: The 4 Stages of Data Monetisation
Day 4: How to turn data into revenue
Day 5: Building a data application
Today, we’ll be exploring the data monetisation journey and the different stages of turning your data into revenue.
No time to read? Listen to the podcast instead.
1. Data monetisation requires data maturity
Like with all data activation use cases, the output of what you’ll get strongly depends on the quality of your input. That’s the case for AI-driven use cases, but also for embedded analytics and data monetisation use cases.
There is little value in just possessing data. It’s how you leverage data that makes a difference. Even with improved reporting, data activation and AI, there will be costs associated with data storage and processing. Turning that data into a potential driver of revenue is how you get real measurable ROI from data.
In order to turn data into revenue, however, you need to operate at a certain level of data maturity. A mature data approach will allow you to anticipate and meet customer needs, and offer tailored experiences to the audience of your data monetisation use case - while ensuring high data quality and robust security.
Do our free data maturity audit to establish a baseline and understand your current level of data maturity.
2. The Data Monetisation Journey: 4 Stages to unlock value from data
Having focused on data maturity first - you’re all set to embark on your data monetisation journey. But what steps are part of that journey?
According to Bill Schmarzo, there are 4 different stages on a data monetisation journey. Moving through these stages will allow you to build a mature data monetisation model and strategy that transforms your data into revenue (or any other way of generating monetary value).
(image is based on Bill Schmarzo’s model)
Stage #1: Data as an asset
In this first stage, data is considered an “asset” for the organisation. The business recognises that the data holds value and potential, but is still unsure on how to unlock that value.
Mostly, there are costs and liabilities associated with data like storage, management, governance, etc. Given the increasing volume of data, it’s becoming a priority to minimise these costs first by implementing a modern data tech infrastructure - for example: on-premise vs the cloud - that will also be the foundation to scale further data initiatives.
Stage #2: Exploring data monetisation use cases
In this stage, companies start to see the uncovered potential data holds. Data is no longer just a strategic asset - it’s something that can be exploited by the business. This is where companies enter the data monetisation conversation. The first thing to do here, is to identify the potential use cases for data monetisation. What initial initiatives will you focus on first, to generate value from data? Note that this mostly is a business conversation - not a technology conversation.
The goal here is not to get ROI from data immediately. It’s more about building Proof-of-Concepts and prototypes that validate your use case and its potential benefits for the organisation. As the cost of data probably still outweighs the value you’re getting out of it, this is also the stage where you still have the flexibility to apply different data monetisation models to your use case to find the optimal solution before you scale.
Step 3: Turning data into value
If your initial proofs-of-concept and prototypes deliver the value you’re hoping for and the potential for bottomline ROI on your data monetisation use case has been identified, it’s time to up the stakes by getting your prototype into production at a larger scale.
Having identified the types of data you own and need for your specific use case in the previous stage, you’ll now need to decide whether giving access to the raw data will be of value to your target audience, or if the data needs to be combined or enhanced with additional data sets.
Depending on your target audience, adding a layer of analytics to the data might be exactly what they need to make better decisions - creating more value for them as users, but also allowing you to charge a more premium price for your data monetisation project.
Typically, the more insights and enrichments you add to the data, the higher its potential value will be to your target audience.
Stage 4: Accelerate your data monetisation strategy
At this stage, your data monetisation strategy is no longer experimental or incremental—it’s time to scale up. With a proven use case and early ROI, you can confidently invest in accelerating growth and maximising value extraction from your data. This involves not only refining the data product but also enhancing how you bring it to market and expanding its reach to a broader audience or new verticals.
To successfully scale, you’ll need to align resources and invest in capabilities that support scalability, such as automated data processing, real-time analytics, and enhanced security measures. At this point, standardising processes and tools becomes essential, enabling you to deliver consistent, high-quality insights efficiently. Additionally, you should explore partnerships, distribution channels, and even licensing opportunities to broaden your data’s impact and revenue potential.
Scaling your data monetisation model goes beyond simply selling more; it’s about embedding data as a continuous driver of value across the business, positioning it as a central pillar in your overall growth strategy. With every step, you’re building a sustainable and future-proof monetisation model that not only generates revenue but also strengthens customer trust and satisfaction through reliable, impactful insights.
3. Conclusion
Data has evolved from a back-office asset to a core driver of revenue and strategic growth. As you progress through the stages of data monetisation—from treating data as an asset to accelerating a robust, scalable strategy—you’re building a foundation that turns data from a cost centre into a profit centre. With each step, you refine how you generate value, learning to align data’s potential with your business goals and customer needs.
Embracing this journey requires a commitment to quality, security, and innovation. It’s not simply about unlocking revenue but doing so in a way that enhances customer trust and embeds data-driven insights at every level of your organisation. The end goal is to create a sustainable data monetisation model that delivers measurable ROI while positioning you to adapt and thrive in an increasingly data-centric world.
