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Data monetization is a lucrative strategy for organizations to increase their revenue, boost innovation and productivity, make better decisions, and provide valuable products and services to their customers.

Since data has become a valuable asset for organizations, using it correctly can transform your organization and bring greater profits.

Data monetization is a great way to do that. 

Companies like Google, Facebook, and Amazon are probably among the first to monetize their high-value data and grow their trillion-dollar business.

The global market for data monetization is expected to grow at 17.2% CAGR during the period 2023-2030, reaching US$ 9.10 billion from 2.99 billion.

In this article, I’ll discuss what data monetization is, its benefits, methods, and types, and how it can help you.

Let’s start!

What Is Data Monetization?

Data monetization is a monetization strategy wherein an organization leverages its raw data and converts it into valuable products, services, or insights in order to generate additional revenue streams.

As a result of monetizing data, businesses can experience increased revenues and reduced expenses. Businesses also utilize their data and share it with third-party companies to generate other benefits like improving supplier terms and making new partnerships.

Type of Data you can Monetize

The types of data that you can monetize include:

  • Demographic data
  • Historical data
  • Contact information
  • Location data
  • Internal metrics
  • User data
  • Weather patterns
  • Market research
  • Transportation routes
  • Future projections
  • Customer insights

All these data types must be measurable so you can generate quantifiable revenue from them. The above data must be used to create value for the end users, partners, etc. by understanding their problems and finding solutions using that data.

Moreover, data can also be categorized into three types based on the structure.

  • Structured: This type of data is fully organized, complete, accurate, and up-to-date. The company aggregates its data for monetization, refines it, organizes it in a specific manner, and then presents it in a given format. This makes it easier for others to understand the data and use it to generate value.
  • Unstructured: Companies can also leverage their data for monetization in its raw, actual form, without refining or pre-processing it. This type of data will have no particular structure, arrangement, or format.
  • Semi-structured data: This type of data might have undergone some pre-processing to make it up-to-date or complete. Since raw data might be incomplete, outdated, or difficult to understand, a company might make some arrangements to make it more useful. 

Type of Data Monetization

Data monetization is primarily of two types:

#1. Indirect/Internal Data Monetization

This is a common type of data monetization in which an organization uses its data internally to produce measurable benefits in its economy. It aims to improve business performance and make informed decisions.

Here, you can use data analytics to discover insights and increase your profits, save costs, and minimize risks. It requires fewer intellectual property, legal precautions, and security. However, a company can generate a limited amount of profit from this type since it depends on its current scenarios and internal structure.

#2. Direct/External Data Monetization

This involves an organization to share their information in order to gain benefits.

You can monetize from your business partners or by selling your data independently or directly via a broker to third parties. You can also generate revenue by utilizing data to create and offer data products and services.

This monetization type is not that prevalent. Besides, it may require several methods for data distribution to customers, third parties, or other potential buyers. It may also involve certain risks, expenses, and security issues.

Benefits of Data Monetization

Better Decision-Making

Making profitable business decisions is crucial for every organization. You must gauge the positives and negatives of a decision and then conclude whether to implement it or not.

For this, you need quality data that is complete, accurate, up-to-date, consistent, and relevant. You can extract your business data and process it to gain useful insights that can help you make better business decisions in real-time.

Targeted Marketing

With internal data monetization using analytics, you will know the pain points of your customers and their preferences and expectations. This will help you shape your products and services and target your marketing efforts in a direction that the customers want.

Increased Revenue

Data monetization can help increase revenue directly or indirectly. You can leverage data analytics to measure your business performance, detect issues, find solutions, and make decisions. This indirectly helps increase your profits.

Similarly, data monetization generates more revenue streams. You can directly sell your data to third parties and boost your profits.

New Partnerships

Healthy partnerships are important for an organization to grow, stay relevant, and create a stronger impression in the market. You can partner with third-party companies and supply them with valuable data. This will create mutually beneficial partnerships.

For example, you can partner with a bank to provide them with valuable business data and receive certain favors in return. This type of arrangement is beneficial for both parties.

Operational Efficiency

Data monetization not only can impact your bottom line but also help streamline your operations and make the process more efficient. You can leverage data analytics to find faults in your operations, reach the root cause, and come up with a solution. This improves your operational efficiency and minimizes waste and expenses.

Competitive Advantage

With data monetization, you will gain access to useful business data and insights like trends, customer behavior, preferences, etc.

When you use these insights to create a better strategy or product, it will give you an upper hand over your competitors since you are providing customers with what they actually need by measuring their demands, lies, and dislikes.

In addition, this creates a wonderful customer experience and satisfaction and enhances your market reputation.

How to Do Data Monetization

Here are some steps you can follow in your data monetization journey.

#1. Planning

Before you directly jump into data monetization, you must plan it. For this, it’s important to gain buy-in. The need to perform data monetization must be felt by everyone in your organization, from the leadership level to executives and other employees, who must work together and make the initiative successful.

In addition, you must set up your objectives for performing data monetization. Many organizations do it only to improve their business performance, reduce expenses, sell data to third parties for partnerships, or simply to create targeted products and services.

Also, consider your audience and do some market research and data analysis as well to know whether your plan or project will work. Your audience could be your customers, partners, a third party, or stakeholders from your own company.

#2. Identify and Collect Data

In this process, you will need to identify quality data that are currently available for you to monetize. It can also include data from external sources that can enhance the currently available data. 

You can take it from your database, computer files, registers, tech stack, and other sources. Once identified, you will need to collect all this data and work on it.

#3. Data Processing

After collecting data from multiple sources, you will need to process and refine it to increase its value. For this, you will need to ensure it is complete, consistent, accurate, up-to-date, and relevant.

However, in many cases, raw data is directly used for data monetization without processing or refining it.

Next, you will need to aggregate this and centralize it, meaning keeping it in a single location. This will make it easier for you to perform data analysis and produce quality data. This data also needs to be organized in a definite order so anyone can understand it easily. 

In addition, you must validate and authenticate data to ensure your organization’s integrity. Once everything is complete, store it in a secure, easy-to-access place that you can quickly share with your internal team or external companies.

#4. Choose the Monetisation Type and Model

In this stage, you will need to choose the type and model of data monetization based on your goals. It’s a good practice to start with internal or indirect monetization first before you go ahead with external or direct monetization.

Furthermore, choose a monetization model based on the chosen type. If you choose external data monetization to sell your data to a third party or create a product or service using your data, you can choose a model – pay-per-use, subscription-based, or freemium.

#5. Set Pricing and Terms

Set the monetization terms and pricing for the data you sell or the product/service you are creating. This creates realistic expectations instead of going vague and creating conflicts.

When fixing the price for your products and services, you must analyze the industry and competitor pricing. It will help you set competitive pricing and increase your chances of conversions.

#6. Monetizing

Once everything is in place, you can start monetizing your data. Here are different methods to do that:


You can provide your data in the form of a subscription or one-time product. Coming under direct data monetization, this is the simplest model to implement and applicable to a business-to-customer (B2C) model. It can take raw, unstructured data to obtain an overview on a higher level. If the data has personal user information, you may need to anonymize it.


As opposed to data-as-a-service with raw data, insight-as-a-service deals with summarized, analytical data like customer behavior, competitive insights, etc. This also comes under direct data monetization.

An organization can sell this data in the form of embedded analytics apps or a one-off report. It needs more work for data generation and visualization and must align with the buyer’s business requirements. It also offers greater buyer value but also more revenue generation for the seller.


In this approach, customers can easily access data and insights by paying for it. This is similar to insights as a service but differs in the scope of analytics functionality and data access. It comes under direct monetization and involves zero maintenance and setup for the buying party.

Data providers can expect the maximum returns from this method, but it incurs more management and maintenance loads for them.

Data-driven data monetization

It comes under indirect data monetization and allows an organization to utilize every data source available, analyze it, and use it to enhance productivity and efficiency.

#7. Ensure Data Security and Compliance

Since cyberattacks and data privacy issues are on the rise, it’s necessary to enable advanced security mechanisms in your processes. 

This helps you provide quality and safe data to your partners, third parties, and internal data analysis team. They will also find you trustworthy to continue business with you and use your data, products, and services.

You must protect your data with technologies and techniques like multifactor-factor authentication (MFA), antivirus software, VPNs, single sign-on (SSO), and more. In addition, you must adhere to regulatory bodies like GDPR, HIPAA, etc. to ensure data privacy.

Different Monetization Models

You can sell data through a data web store or data marketplace. Data monetization models are of different types that you can choose based on your goals and needs. 

  • Freemium: This is where an organization offers its products or services for free. This is the best way to enter a competitive market by offering a product or service enriched with useful features that users can access for free. You can also create some pricing tiers if a user wants to unlock premium features.
  • Pay-as-you-go: This requires users to pay some money only for the services or products they use. It incurs no commitments or contracts, thus, more flexibility in payments and usage. Whenever the user needs to use the product or service, they can pay for it and use it till it lasts. They can decide whether to come back at it or not.
  • Subscription-Based: In this model, an organization can set a monthly or annual price for a product or service. So, once you pay for it, you will have the entire month to use it. You can also set different tiers and offer features based on that. It could be offered either in individual subscriptions or for a group.

Challenges in Data Monetization

Some challenges associated with data monetization are:

  • Selling or sharing data with third parties can involve security, privacy, and compliance risks. There are many strict data governance laws around how a company manages its business and customer data, and may penalize you if found guilty. 
  • The process requires new, modern skills and expertise that not every organization has.
  • Monetization utilizes data analytics and predictions to make data-driven decisions, which might not always be correct. 
  • Many businesses could be overwhelmed with a massive amount of data and figuring out how to make the best use of it. 

Case Studies 

Businesses from multiple industries leverage data monetization, from IT, digital marketing, cybersecurity, and e-commerce to finance, agriculture, education, and much more.

Infosys: Infosys helped a multinational aircraft manufacturing company generate more revenue by sharing event data and aircraft maintenance information with airlines.

Facebook: This social media company has leveraged data monetization to build its complete business model. It collects massive data volumes about the users and their online behavior, location, etc., to offer targeted ads to companies.

American Express: The American Express financial service provider does data monetization to offer tailored marketing services to merchants by analyzing transaction info, consumer behavior, etc. 

Data Monetization Tools

Now, let’s look at some of the data monetization tools:

#1. Tasil

Tasil will help you monetize customer data via a secure, effective and real-time platform. With this powerful platform, you can leverage real-time data, convert it into actionable insights, and clock higher revenues.

Tasil accelerates time-to-market, personalized communications and syncs easily with your enterprise data. It also secures your data and its privacy.

#2. Carto

Drive your data monetization efforts with Carto, which provides a location-based compelling solution to all your clients. It also provides a channel where you can resell your business data.

Carto is a geospatial, full-stack platform on which you can host and build your data monetization process.

#3. Privitar

Use Privitar and overcome your trust barriers so you can confidently share data with others and collaborate.

The platform’s provenance and privacy capabilities provide you with the tools to find rich insights and convert them into lucrative revenue streams. It also helps mitigate data exposure risks.  


Data monetization can create opportunities for businesses to generate more revenue, make new partnerships, and grow their business. There are many types and methods of data monetization that you can choose based on your needs and objectives. You can also leverage the above tools to ease your data monetization efforts.  

You may also explore some Subscription billing software to asssist and manage recurring payments.

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  • Amrita Pathak
    Amrita is a senior Technology Content Writer and Copywriter with a keen interest in delving deep into Cybersecurity, AI, ML, Cloud Computing, Web Hosting, Project Management, Software Development, and other topics on evolving technology….
  • Narendra Mohan Mittal

    Narendra Mohan Mittal is a versatile and experienced digital branding strategist and content editor with over 12 years of experience. He is a Gold Medalist in M-Tech and B-Tech in Computer Science & Engineering.


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