Perhaps you don’t know it, but with your company or startup you can monetize data collected from customers and processes and create new income streams. This guide dedicated to data monetization will help you do so.
Data monetization what it is: definition and meaning
Let’s start from what data monetization is: the first step to discover how to best exploit this practice, in fact, is to start from its definition.
Data monetization definition: internal vs external
The term data monetization refers to the possibility of transforming customer data into revenue for the company. This possibility can translate into reality in two contexts, one “internal” and one “external”: in the first case this happens through the development of Data Analytics projects, while monetizing data externally means in practical terms selling, exchanging, or sharing data with external subjects to the company.
Why now: big data monetization and company maturity
“Internal” data monetization is a practice already widely spread among companies while the second area is less mature, although the opportunities, even in this second case, are multiple.
In this regard, it’s good that you know that not only the classic Information Providers can sell, exchange, or share data. On the contrary: companies with even very different core businesses can exploit this possibility to seize new market opportunities.
Data monetization business model
In concrete terms, data monetization can translate into more than one business model.
Direct models: data selling, licenses, data-as-a-service (DaaS)
Among direct data monetization models is included, obviously, the direct selling of them. But that’s not all: it’s also possible to sell licenses (that is, authorize the use of data or software).
Another possibility is the so-called Data-as-a-service (DaaS), which provides the supply of data and related analyses in on-demand and cloud-based mode and which therefore allows companies to access this information without the need for complex infrastructures or particularly extensive internal data management.
Indirect models: product wrapping and insight-driven decisions
Indirect data monetization models use data collected by the company to improve internal efficiency and profitability and thus obtain a competitive advantage.
A concrete example is product wrapping, which consists of integrating information and analyses into products and services offered to customers, so as to improve their purchasing experience and give life to new revenue streams.
To improve business processes through data monetization, it’s then possible to adopt insight-driven decisions, that is, guided by data analysis for the development and innovation of products and services.
Ecosystems and partnerships for data exchange/sharing
In addition to direct and indirect models, the third way is represented by the creation of ecosystems and forms of business partnerships for the exchange and sharing of data in order to achieve common goals, such as the creation of new products or services or the improvement of operational efficiency.
Data monetization, the phases: from discovery to scalability
The data monetization process consists of multiple phases. Let’s discover them together.
Data identification
The first phase of the process is the one in which to identify valuable data, that is, those that – whether internal or external – can be used to generate economic benefits based on expectations and needs.
Data evaluation
In the second phase, you must evaluate data quality: in particular, you must take into account their completeness and consistency, without neglecting however the aspect related to security and compliance. You can apply specific techniques to clean, integrate, and protect the data at your disposal.
Define the business model
Another fundamental step is the one concerning the definition of the business model, based on the characteristics of the data and the market, between demand, competition, and added value that you can bring. Among the aspects you must define in this phase is calculating the selling price.
Manage data
To manage data that your startup has access to, you must develop the right skills and technologies, but also use the most appropriate platforms and tools. Remember the words of Ronald H. Coase:
“Torture the data long enough and they will confess anything.”
Among the resources at your disposal, it’s possible to mention the already cited cloud computing, but also machine learning and APIs.
Monitor results
The last phase is the one dedicated to monitoring and measuring results, through the identification of the right marketing KPIs, but also based on customer and partner feedback and market analyses.
Data monetization: examples and real cases
In the previous paragraph we started to approach the data monetization process in a more practical way. Some examples and real cases of data monetization can help you understand even more concretely what we’re talking about.
Telco and mobility: geospatial datasets and smart cities
You can find various examples of data monetization in the telco and mobility fields. As for the first case, keep in mind that telecommunications providers have the possibility to access a large amount of data thanks to their networks. Among these stand out geospatial data, whose analysis allows – among various opportunities – to identify areas where there is more demand for services.
As for the mobility sector, instead, a particular mention is deserved by smart cities, which exploit data to improve the offer of services to citizens, support public policy decisions, and perfect the functioning of internal processes.
Cloud provider: pay-as-you-go for dataset/analytics embedded
One of the most interesting innovations in the field of data management is the one concerning the so-called embedded analytics, which allow companies to make data accessible and usable at any time, in cloud mode, by all business levels. Business intelligence tools are integrated directly within software applications, so as to avoid the need to change platform to access data reports and analytical features.
Retail/Finance: data sharing with partners for new revenues
Data sharing with partner companies allows obtaining new revenues in various areas, among which Retail and Finance stand out.
In the first case, a retailer who shares data with brands and marketplaces can, for example, better understand customer behavior and offer personalized shopping experiences.
In the second case, data sharing – among other things – facilitates the creation of diversified and personalized products and services and also allows improving so-called financial inclusion.
Benefits, risks and barriers to adoption
To understand even better the opportunities and risks related to the adoption of data monetization, it’s appropriate to focus now on the advantages and limits of this approach.
Competitive advantage and new revenue lines
The benefits that data monetization offers to companies are related, first of all, to the creation of new revenue lines, through direct data selling or their integration into products or services. With it, it’s possible to optimize internal processes, enrich the offer and, in this way, ensure a competitive advantage.
Risks: data quality, trust, compliance, cannibalization
The main limits of data monetization are related, first of all, to data quality but also (and, in some cases, especially) to regulatory compliance: companies must comply with stringent regulations regarding personal data protection. Improper use of data, in addition to exposing the company to legal risks, undermines the trust that customers place in it.
Another risk is related to so-called digital cannibalism: collecting data in a traditional way requires complex and long work and it’s for this reason that, in the very last years, the practice of creating (and selling) “artificial” data created by algorithms, on which other data are then built, has spread.
Technologies and emerging trends
Before concluding this guide, it’s appropriate to take a look at technologies and emerging trends.
Data as product (DaaP) and MDM for support
DaaP (Data as a Product, data as product) is a modern approach to data management and analysis in which datasets are considered autonomous products and are created and managed with end users in mind.
Master Data Management (MDM) is a discipline that has the purpose of providing the company with a single reliable, complete, and coherent version of the most important data at its disposal, eliminating incorrect or redundant data and thus improving operational efficiency.
AI for monetization: from “raw” data to contextual answers
Artificial Intelligence has also found fertile ground in data monetization: in this field, AI can be of great help by transforming raw data into more valuable information and giving the possibility to quickly discover hidden patterns in the large amount of available data.
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