Businesses use data monetization as a starting point for treating data as a competitive asset. Companies like Bosch and the automotive industry are doing it, as well as internet giants such as Amazon and Google. The data monetization industry is estimated to be worth $15.4 billion by 2030. And most businesses worldwide are far away from reaching their full potential in this regard. The advantages of monetizing your data are clear: gaining a competitive advantage, creating new opportunities in the market, increasing productivity by developing a new revenue source, efficiency, increased ROI, etc., and these are only some of them. Sounds promising? Keep on reading this post, where we discuss possible ways, methods, and strategies for turning your data into money.
Before considering how to capitalize on data monetization, one must define it first because there are a lot of definitions and uncertainties about it. For this article, we define data monetization as “the generation of new revenue streams from data-driven products”. Even though it is not a new concept, it has gained notoriety in recent years.
Embedded in data sharing, more and more companies are exploring opportunities to monetize their data. One absolutely needs to consider the legal part of data monetization because data protection and privacy play a huge role if companies want to sell or use data. These aspects are still maturing alongside the technical parts of data monetization. But they need to be considered from the beginning, for example, by asking the question about privacy-preserving techniques.
One thing is for sure: there will be companies who are establishing methodologies and strategies to monetize data in the most efficient way, and there are companies who think this will not be necessary. And in every industry, even in the least digitized ones, the companies that are investing in data monetization now will be the winners in the future. It will come down to: “To be or not to be”. And this should not be a question for your business. Luckily, there are several methods and strategies that can guide your data monetization efforts.
To be effective, data monetization initiatives should not exist on their own, but they should fit the business strategy. It should be clear that it can serve internal as well as external purposes. There are several methods of monetizing data from which you can choose one or a combination. The most important thing is that these methods are tools that need to fit your data monetization strategy, which in turn is embedded in the general business strategy. This holistic view can be structured into four stages to help start any data monetization initiatives. The methods and stages we list here serve as guidelines and will help you to shape your data monetization strategy:
Some people consider data monetization an internal affair to generate business efficiencies, and others see it as selling data to external clients. Both viewpoints are correct:
Internal data monetization refers to a method of making informed business decisions with the use of data and analytics to make the business more efficient. This data can be managed in an internal data marketplace, where everyone inside an organization can look for data as needed. The emphasis here lies on efficiency improvements and, thus, savings by, for example, upselling and cross-selling opportunities via targeted marketing or improvements to the supply chain or products.
This can be a widespread thing in the organization, such as the definition of an “active customer” term within a company. If not properly defined and aligned across the company, the company wastes money by doing activities towards people who are not interested instead of focusing on their active customers. This is because they do not know who their active customer actually is, based on their data. They need business users to properly define what an active customer is supposed to mean and data analysts to check data based on these defined parameters.
Another example can be improved metadata management enabling monetization of content, such as in the case of one of our customers where Accurity helped ZDF Studios to manage metadata for their media supply chain.
On the other hand, external data monetization is about selling data to third parties outside of the organization. It can be sold in raw form or as analysis and insights at an external data marketplace. This way of monetizing data is more about revenue generation. Data can be sold externally, for example, by selling lists of contact information that companies can use for advertising purposes or providing access to research or specific information behind a paywall.
Besides the question of to whom data can be monetized, organizations must also ask themselves how and in which form they can do it, thus defining their data monetization methodology:
The Data-as-a-Service or service method is the simplest, most direct data monetization method. Here, data is sold directly to customers. For this method, data is raw and unstructured, anonymized, or aggregated for a high-level overview.
The Insight-as-a-Service method or insight method, on the other hand, provides summarized analytical insights. So, data that has been processed beforehand is generally more expensive because more work is required to generate insights and visualizations. These insights must also align with the buyer’s requirements to be valuable to them and thus monetized. Companies can sell this type of data as a one-off report or continuously for a steady income source.
The analytics-enabled platform and embedded analytics method integrate the analytics and business intelligence platforms into a software application. These analytical and visual features provide customers with highly relevant and versatile data, so they can analyze the data held within the software application into which the analytics platform is embedded. This can generate many benefits like competitive advantages, faster market capture, etc. Thus, generating money and efficiency.
With data-driven business methods, companies can leverage every available data source, like sales, marketing, or finance, in pursuit of efficiency and productivity. It can be used, for example, to check marketing data to discover if customer buying habits have changed, to adapt sales metrics, and optimize the supply chain. Extended to the whole company, this method helps to make more informed decisions.
Regardless of the chosen data monetization methods, it is always important to build up these capabilities with a plan and by considering the wishes of the relevant stakeholders because every data monetization framework is unique. Here are four possible stages of how to start your data monetization initiatives:
The Foundation is the first stage. The data monetization journey can start with a business-focused assessment of the data landscape and the identification of specific use cases and revenue potentials. In this stage, managing the relevant stakeholders' expectations and establishing clear communication to get executive and business leadership support is necessary. This stage aims to develop a roadmap to generate business value through data and insights with company-wide support.
The Beginning of data monetization efforts should focus on supporting improvements to internal operations. To identify efficiencies and develop capabilities to collect additional information in support of enhancing existing processes. At this stage, companies should develop a culture of data-driven decision-making and focus on strengthening the skills required for the upcoming stages via, for example, talent acquisition. A characteristic of this stage is speed and use case specific data monetization.
The Advancement. Where the foundation and practices are extended to the community of entities that play a role in the business value chain, the scope of this stage is rather extensive. Still, it brings a lot of business-related benefits. Businesses will also acquire new data sources here, and there are a lot of technology integration efforts. Organizational change management and training are also important success factors at this stage. Depending on the use cases, this can be the last stage, from which the advancement of the data monetization efforts can be evaluated and adapted.
The Surpassing. This consists of incorporating external factors that could disrupt certain aspects of the value chain. This stage is mainly about acknowledging and incorporating these external factors, and learning from data monetization initiatives of other companies, to optimize their own strategy. A heavier reliance on data insights channels and predictive models, as well as more active participation in data and insights marketplaces, are characteristics of this stage. Depending on the maturity of correlated internal processes, businesses will generate extended benefits through data monetization – both financially and through captured insights. But there are only a few companies who truly have reached this stage. And the process is never completed, as it needs to become optimized and monitored with data and insights from stakeholders.
These points should clarify that it is impossible to change a longstanding data culture overnight. Data is not a static asset that depreciates over time but is evolving and growing within an organization. Taking this into account, data monetizing assets need to be framed by the right strategy, leadership, culture, and change management efforts, which include every important stakeholder and the right technology. This is what it is all about now.
There are a lot of tools on the market promising to help you establish a data culture or even data monetization. But only a few can truly deliver on this promise. Accurity is one of them.
Accurity’s metadata management features are good for combining technical and business information so people can have a clear view of their data, from which they can decide in which direction the initiatives should be heading. That is important to provide you with a clear view of business and technical insights so you can decide where the data monetization initiatives should be heading.
But you also need to be sure that your company possesses data of sufficient quality. It would not make sense to base a strategy based on bad data, let alone establish a data monetization strategy on it. Because who would want to buy bad-quality data? But the good news is that Accurity also offers data quality and observability capabilities, which you can combine with the other metadata features to create a solid, scalable, and future-proofed platform, And from where you can take on your adventure of monetizing data.
Feel free to contact Simplity for more information on how to use data strategically. Or contact Accurity if you want to know more about the technology behind this reemerging trend. You can try the Accurity Data Quality and Data Observability solution for free. We appreciate you reading this blog until the end, and we hope we have provided you with some valuable information.