Back in the early 2000s, the corporate landscape looked a lot different: eight out of ten leading American companies were primarily engaged in manufacturing.
However, the last two decades have witnessed a remarkable transformation on this front. The contemporary business hierarchy is now dominated by technology giants such as Microsoft, Google, Amazon, and Meta, highlighting a pivotal shift from traditional manufacturing to organizations centered on data acquisition and processing. This evolution emphasizes the growing importance of data-centric operations in defining the competitive landscape.
In today’s digital age, companies collect enormous volumes of data from various sources, including customer interactions, transactions, and operational processes. But it is not just numbers and graphs. This is a real opportunity to turn data assets into additional revenue, and the size of the data monetization market is enormous.
If your company collects data, the real question is: Is it truly capitalizing on the potential value of that data? Join us as we delve into data monetization strategies, unlocking the secrets of transforming raw information into financial profit.
What is data monetization?
Data monetization is the process of generating revenue by leveraging and capitalizing on the value of data. Data monetization involves analyzing, packaging, and selling this data to external parties or utilizing it internally to create new revenue streams. Businesses can extract insights, patterns, and trends from their data, offering valuable information to partners, advertisers, or other stakeholders.
Data monetization strategies
Although there is no one-size-fits-all solution, decision-makers in companies engaged in data collection usually decide upon one of the following data monetization strategies:
Direct data monetization
Direct data monetization entails selling or licensing the actual data that a company collects. Companies can generate revenue by directly selling raw datasets to external organizations, providing valuable information for business operations, research, analysis, or other purposes.
For instance, a market research firm may sell its collected consumer data to other companies for targeted marketing.
Indirect data monetization
In contrast to direct data monetization, the indirect strategy derives value from data in ways that may not involve selling it directly but using it to improve products, services, efficiency, innovation, or customer experiences. For example, an e-commerce platform can use customer behavior data to personalize recommendations, leading to increased sales.
Companies analyze raw data to extract meaningful insights, patterns, trends, or predictions, which are then offered to clients. By transforming data into actionable intelligence, companies not only generate revenue but also provide valuable decision-making support to businesses seeking a competitive edge.
10 real-world data monetization use cases and examples
Below, we present 10 real-world data monetization examples. We will look at companies mastering the art of monetization who transformed raw data into strategic assets, not just unlocking value but redefining entire business models.
Zalando, a leading e-commerce fashion platform, launched the ZMS Insights service, which offers actionable recommendations based on customer, onsite and consumer feedback analysis and publications, allowing clients to understand market and consumer developments and trends.
DTN is a private company based in Minnesota that specializes in subscription-based analysis and delivery of real-time weather, agricultural, energy, and commodity market data. Agriculture giants such as Monsanto or John Deere utilize this information on a regular basis.
Zendesk is a customer service solution used by more than 40,000 companies worldwide. With Zendesk UI, an advanced data analytics tool, the company managed to create an additional revenue stream from Plus and Enterprise plans.
Vodafone has all it takes to turn data into insights, and insights into money. Their commercial analytics tool delivers heatmaps of phone usage and customer traffic. In doing so, they help to plan infrastructure projects, reduce traffic, and enhance the shopping experience.
Uber Eats for Merchants provides detailed information: net sales, average order size, most popular times etc., and performance insights that help businesses grow and stay ahead of the competition.
Michelin is a car tire manufacturer that offers the Mobility Insights tool. The algorithms, built over decades of research and development, help companies and public authorities analyze driving behavior.
Dunnhumby claims it possesses the power to “unlock the power of data and create engaging experiences that put the Consumer first.” This global leader in customer data provides insights to the world’s largest retailers, such as Coca-Cola, Tesco, L’Oréal, and Procter & Gamble.
Mastercard Advisors enables organizations to make more informed decisions by providing trends and insights derived from billions of transactions, and aggregated and anonymous consumer spending patterns.
Spotify for Artists provides in-depth analytics to better understand audience segments and develop fanbases with insights powered by research into fan behavior around the world. Spotify for Brands for Brands offers valuable demographics, interests and behaviors for music labels and advertisers.
IBM has developed a robust business intelligence and data-driven architecture that “can connect the right people to the right data sets at the right time to promote agility, predict outcomes and personalize experiences.”
“Why monetize my data?”
Data monetization has numerous benefits. The potential to generate new sources of income is undoubtedly the main advantage. By selling data, companies can generate additional income and gain a competitive edge. According to the Delloite Insights report, the global data monetization market had achieved a value of USD 2 billion by the year 2020.
Monetization also enables businesses to extract insights and trends that support better decision-making processes with respect to data-driven advertising, products, and strategies. Using customer data for personalization allows businesses to tailor products and services, providing a more personalized and satisfying experience for their customers.
Data analytics has the power to boost operational efficiency by providing valuable insights into organizational processes. By streamlining operations based on data analysis, businesses can optimize resource allocation, enhance overall performance, and reduce cost.
Furthermore, the integration of data analytics fosters innovation by identifying opportunities for new products and business models.
Finally, data can be a valuable asset in forming partnerships and collaborations. Businesses can share data with other organizations, leading to mutually beneficial arrangements and strategic alliances.
“How to monetize my data?”
This paragraph will provide you with a data monetization framework. We will walk you through a comprehensive seven-step process on how to effectively monetize your information assets. From assessing your data landscape to implementing monetization strategies, these steps offer a roadmap for turning data into dollars.
Step 1: Assess existing data and determine future collection
Before you start making money from your data, it is crucial to conduct a comprehensive assessment of the data landscape within your organization. This involves a precise examination of the data you currently possess, understanding its scope, quality, and potential value. Simultaneously, determine the data you may need to acquire or enhance to meet market demands.
Step 2: Choose a target audience
Defining your target audience becomes a pivotal step in maximizing the value and impact of data monetization efforts, contributing to a successful and targeted outcome for your business.
Market researchers, marketing agencies, financial institutions, healthcare organizations, e-commerce platforms, and technology companies are driven by data sold by their partners. Rate the value your data holds for different industries and try to provide them with a customized offer.
Remember about your internal client as well. Through the proper use of data and analytics, organizations can improve products, processes, customer experiences, etc. The addressee of the data may be your own marketing department, on their quest to learn customer behavior patterns and consequently boost sales.
Step 3: Select a data monetization strategy
Identifying the appropriate data monetization strategy is paramount to success. Choose between a direct or indirect strategy. Decide whether you will sell raw data or insights, and establish partnerships, if necessary. A well-chosen strategy aligns with business goals, maximizes revenue potential, and ensures ethical and legal considerations are met.
Step 4: Collect, centralize, process and analyze your data
To effectively monetize data, it is imperative to orchestrate a seamless and systematic approach encompassing the collection, centralization, processing, and analysis of relevant datasets.
The initial step involves data collection from diverse sources, ensuring a comprehensive and representative dataset. Centralizing this data into a unified repository, such as Snowflake, streamlines accessibility and facilitates efficient processing. Employing advanced analytics and processing techniques is essential to uncovering meaningful patterns, trends, and insights within the data. The analysis enhances the intrinsic value of the information and unlocks the full potential of data as a valuable organizational asset.
Develop or buy a software platform that facilitates the storage and processing of centralized data for the benefit of all stakeholders and clients.
Step 5: Adhere to data privacy governance, compliance, and cybersecurity best practices
When it comes to the sale of data, compliance with data privacy governance, adherence to regulatory requirements, and the implementation of robust cybersecurity best practices are essential. If abbreviations like CCPA, HIPAA, or GDRP sound unfamiliar to you, read our must-read guide to SaaS compliance.
Uphold rigorous data protection policies as they ensure that data collection, storage, and processing comply with legal requirements, safeguarding the organization, stakeholders, and customers. Prioritize cybersecurity best practices, such as encryption and secure access controls, and establish a defense against potential breaches.
Step 6: Decide on licensing and unauthorized usage detection
Strategically determining the licensing framework for data monetization is crucial to ensuring that the value derived from data is maximized. Several licensing models can be employed, each tailored to the specific needs and goals of the organization, for example, perpetual, subscription-based, usage-based, exclusive, and more.
Implementing robust mechanisms for unauthorized usage detection is imperative to safeguard against potential misuse or breaches. It fortifies the integrity of the data and preserves the trust of stakeholders by demonstrating a commitment to ethical and responsible data handling practices.
Step 7: Prepare and keep up-to-date data documentation
Proper and up-to-date data documentation is an integral component of responsible data management that contributes to the success and sustainability of data monetization. This approach facilitates a smooth data monetization process and reinforces accountability, compliance, and trust in the integrity of the data being leveraged for financial gain.
Establishing a comprehensive documentation system involves detailing the origin, structure, and attributes of the data, ensuring transparency and clarity for all stakeholders involved. Regular updates to this documentation are essential to reflect any changes in data sources, processing methods, or analytical techniques.
Challenges of data monetization
Though there are many benefits to data monetization, it also comes with challenges.
Customers and regulatory bodies are increasingly concerned about how personal information is handled and used, leading to potential legal and reputational risks. Compliance with laws can be challenging, and failure to comply may result in severe penalties.
According to the BI Survey, 56% of decision-makers are concerned about data quality. The success of data monetization relies on data quality and accuracy. If the information is inaccurate, inconsistent, or outdated, it can lead to flawed insights.
The successful execution of data monetization strategies frequently requires advanced technological infrastructure and capabilities. Companies encounter challenges in integrating different data sources and implementing analytics tools. Moreover, the risk of data breaches, unauthorized access, or cyberattacks poses a significant challenge, requiring appropriate security measures to protect sensitive information.
Last but not least, there are ethical considerations related to how data is collected, used, and shared by third parties.
Need help with your data?
RST Software offers strategic consulting to help you define your data monetization goals, identify valuable data assets, and turn them into a strategy.
We build robust data infrastructures on a daily basis: databases, warehouses, and processing systems—to ensure that the data is efficiently collected, stored, and processed. We design and implement Application Programming Interfaces (APIs) that allow third parties to access specific datasets, facilitating data sharing and monetization.
Our custom platforms and tools enable our clients to package, sell, and manage access to data. This includes features for licensing, subscription management, and analytics that allow users to derive meaningful insights from the data, enhancing their value.
Last but not least, we are experts in implementing security measures to protect sensitive data and ensure compliance with data protection regulations.
If your organization collects data, there is money for the taking. Get in touch with us if you need assistance putting all the pieces together.
