Data Monetisation at a Glance
Data monetisation is more than just a technological issue – it is a key component of modern business strategies.
Whether internally through increased efficiency or externally through data-driven products and services – data monetisation offers new opportunities to businesses across all sectors. However, it requires clear objectives, appropriate governance structures and an understanding of the business model, core systems and the key capabilities that drive value creation.
The main objectives of data monetisation
Data monetisation pursues several strategic objectives:
- Creating value from existing data sets: Companies continuously generate data through production, sales or customer interactions. This data can be analysed and utilised in the form of internal services or products, thereby optimising existing value creation or generating new value.
- Developing new and optimising existing business models: Data can form the basis for new digital business models, such as data-driven platforms, pay-per-use models or personalised services, whilst helping to improve existing business models by enhancing quality.
- Increased efficiency and cost optimisation: Internal data analysis can be used to improve processes, optimise maintenance cycles or make supply chains more efficient – an indirect but effective financial benefit.
- Partnerships and data ecosystems: The controlled exchange of data with partners or within industry ecosystems can open up new markets – for example, through data-sharing platforms or data-driven collaborations.
- Transparency and better decision-making: Data provides a factual basis. Well-informed decisions at management level or in product development contribute to the company’s long-term success.
How can data monetisation work using a plant component as an example?
A practical example illustrates how data-driven added value can be specifically realised across technical plant systems. A client of Materna SE manufactures components for plant machinery. To expand its existing product and service portfolio, the aim was to develop a data-driven service as a digital business model.
First and foremost, a deep understanding of the technical domain was crucial. Only those who understand the technology and its application can make meaningful use of data. In our case, this meant: How do certain plant components work? When do they operate efficiently – and why do actual operating conditions deviate from this?
This formed the basis for a clearly formulated business hypothesis: ‘If we identify the optimal operating state, the client will save X % in energy and, consequently, in costs.’ The focus was on a concrete economic benefit, not on a purely analytical product.
To test this hypothesis, we followed a structured validation process:
- Internal analysis: Initial estimates, calculations and modelling.
- Interviews: Discussions with technical, maintenance and management teams to assess relevance and feasibility.
- Proof of value: Verification using real operational data, with the aim of demonstrating the potential savings in a transparent manner.
The key question throughout was: Is the identified added value significant enough for the customer to pay for it?
Another key to success was targeted dialogue with the relevant user groups. Good customer contact meant engaging with those who actually operate, assess and improve the plant components – not just the procurement department. Only in this way could we ensure that the product met real-world needs.
This raises a key question: does data monetisation always mean that revenue is generated directly? The clear answer is: no. Monetisation can also mean creating strategic value, such as a better understanding of processes, more efficient workflows or the foundation for new business models. These benefits do not immediately translate into revenue or profits, but they strengthen the company’s market position in the long term.
The result: the client was able to identify operational inefficiencies, realise savings and develop a digital service offering based on these insights – both for its own operations and for its customers.
Time to act
Our client will not be the only company to possess large volumes of unused data. Particularly in the manufacture of machinery, which generates vast amounts of data, there remains significant potential to further develop one’s own business and secure competitive advantages – using resources that are, in effect, already available. Now is the right time to modernise your data strategy so that data can be used in a targeted manner to generate new sources of revenue or achieve internal efficiency gains.
