All the activities planned and carried out within any company are to create profit. The use of data as a new business asset maintains this line. There are several ways to use company data; even in this sense, the final purpose is to try to take advantage of and profit from it. Data Monetization concerns this aspect precisely because it allows the company to optimize and generate revenues thanks to the intelligent use of its data, creating value starting from them.
The process of using data to obtain a quantifiable economic benefit. In other words, it is a question of generating measurable economic benefits from the available data sources. All this can happen internally to the company through the development of strategies and projects based on Analytics or externally, just as it happens for products and services through the sale, exchange, or sharing of their data with external actors. We are therefore talking about direct (or external) Data Monetization and indirect (or internal) Data Monetization.
Data Monetization: The Two Methods To Implement It
There are two main methods for using company data to do Data Monetization, and they reflect on the direct and indirect use of company data.
Direct (Or External) Method
Direct monetization is exchanging, selling, or sharing company data and analytics with third parties. This type of monetization provides access to company data, obtaining additional revenue in return. Given this benefit, it is necessary to consider any risks related to privacy, the best method to make the data available to the customer, and the required matchmaking to find customers willing to recognize the value in the data provided. For this approach, we can identify two options to implement it:
Direct Sale Of Raw Data
It is through centralized or non-centralized platforms. The alternatives to doing this are to share a dataset or to provide access to data via API, a helpful choice significantly if it is constantly updated data for which real-time access is required;
Sale Of Analysis And Insights
It is therefore not directly of the data, but of their processing and analysis. This way, the value increases because the service offered is of higher quality. It also increases the number of potential users as it allows companies to approach companies that do not, by their nature, have internal analytical skills and, therefore, would not have an advantage in purchasing raw data. Alternatively, sharing data and analysis can also take place to collaborate with partner companies to create value with a more in-depth analysis than achievable within the company to have better insights available or create new products jointly.
Indirect (Or Internal) Method
On the other hand, indirect monetization is based on the use of data within the company to obtain a competitive advantage from their analysis, make measurable improvements in business performance, and support decision-makers to make informed decisions. The indirect approach is today the most common form of monetization as it requires fewer complications related to security and privacy than the regulations governing sharing. Using the data internally can be done in various ways, but above all, it is essential to understand and investigate what is the ultimate goal you want to pursue. Typically the data are used with two main objectives:
- Data-based optimization, i.e., those contexts in which data analysis is set up to seek insights and valuable information for optimizing business performance, for example, by reducing costs and risks, increasing the efficiency of processes, or even testing the positive or negative effect of product changes/updates on customers.
- Data-driven business models aim to identify new business opportunities and discover new types of consumers or market niches. These are helpful analyzes to diversify the offer and look for ways to evolve the business model towards new markets, anticipating the competition. At high levels of efficiency, it is also possible to increase one’s datasets by purchasing data from other markets to anticipate needs based on more complete datasets and values for the new target of customers.
These two approaches are not alternatives. You don’t necessarily have to choose one or the other. An example of this is large platforms such as Google and Amazon, which used their data and third-party data to continue improving and anticipating the market. If until a few years ago, using Data Monetization with the indirect method hid a series of pitfalls related to technological constraints, today there are no longer limits for this activity: companies have large amounts of data coming from the various departments and also technologies they have evolved a lot, to the point of no longer being an insurmountable obstacle.
Unfortunately, according to the Observatory’s findings, in 2019, as many as 7 out of 10 companies did not use their data. On the contrary, they made them available to external actors, often free of charge. This is undoubtedly an indication that a data culture is not adequately widespread. Therefore many companies still do not perceive the hidden value in their corporate data.
How To Set Up A Data Monetization Strategy
To best set up a good Data Monetization process, we have identified five fundamental steps that can be represented with five questions to answer:
- Where and what is the data? This is the identification of data sources, both internal and external, to the company.
- How can I aggregate and validate them to make them usable by the different departments? These are the operations of connection, aggregation, validation, authentication, and data exchange between the various company functions or departments to put them at the service of the company strategy in the most effective way.
- On what terms I am willing to trade or sell them? This aspect characterizes direct monetization and concerns the definition of the conditions and prices for the final exchange to the customer. This offer is complete and captivating for the real needs of the market.
- What path can I set to exploit the data internally? This aspect characterizes indirect monetization, therefore, a way of research and predictive analysis to obtain valuable insights to be used in the company, for example, for the management and reduction of risks, for the improvement of the customer experience, or the optimization of resources;
- How does the business update the insights obtained? The exploitation of insights affects the evolution of the current business model (data-driven perspective or new servitization methods), even going so far as to provoke a radical transformation with benefits in terms of differentiation and the creation of competitive advantage.
The data monetization strategy is part of the broader corporate data strategy. The first step is undoubtedly linked to corporate culture: recognizing the importance of data, an asset that has a life of its own in the corporate context and evolves with it. Assuming that company data are at the center of this process, we cannot ignore their identification, understanding, and contextualization. On the other hand, observing them is not enough: transformation and cleaning operations are needed to make them coherent and analyzable.
Here, technical concepts of data analysis come into play that refer to Data Science, Machine Learning, and all the algorithmic and statistical experiments necessary to extract useful information from data for decision-making processes. Below we propose in-depth content on the topic of Data Monetization, a guide to help companies define their Data Monetization Strategy in the way that best suits their context.