How to Create a Data Monetization Strategy for Business Growth [+Framework]

How to Create a Data Monetization Strategy for Business Growth [+Framework]

By Marta Teneva

September 29, 2023

Today, businesses are increasingly recognizing the vast potential of their data, not only for insights but also as a viable product to drive growth and open up new revenue channels. But how prevalent is this trend? 

Deloitte’s 2023 Global Technology Leadership Study offers some insights: 

  • 36% of executives are already generating revenue by selling data, technology, or tech-centric services. 
  • An additional 16% plan to venture into this space within the next two years. 
  • The market stood at USD 2.1 billion in 2020. 
  • By 2030, it’s predicted to soar to USD 15.5 billion, marking an impressive CAGR of 22.1%. 

The benefits of developing a solid data monetization strategy are manifold.  

In saturated sectors, differentiating becomes challenging. A robust data monetization strategy offers businesses a chance to outpace competitors still trying to leverage their data.  

Beyond directly selling data, monetization can reveal previously unseen revenue opportunities. For instance, insights from data can lead to the development of innovative products tailored to emerging consumer needs. Moreover, in domains like manufacturing, analyzing production data can lead to enhanced efficiency, resulting in reduced waste and cost savings. 

But data monetization isn’t just about monetary gains. Sharing valuable data insights with sectors, like the financial industry, can lead to mutually beneficial partnerships. 

However, diving into this opportunity requires businesses to adapt and evolve. Implementing data monetization means reimagining traditional processes, reshaping practices, and often redefining operational models. It’s a shift tech leaders should spearhead given the stakes involved. 

This guide will help you: 

  • Understand the dynamics of monetizing data, considering both its rewards and challenges. 
  • Get familiar with proven data monetization strategy frameworks and tactics designed to maximize the economic value of your data assets. 
  • Take a page from the leaders’ book with success stories of companies that have transformed their data monetization efforts into tangible financial gains. 

Table of Contents

What Is Data Monetization?

Data Monetization Methods

Types of Data Monetization

How to Prepare Your Company for Data Monetization in 6 Key Steps

A Framework for Effective Data Monetization Strategy

 Data Monetization Use Cases

 Data Monetization in Action: Real-World Examples 

 Data Monetization FAQs 

 Enhance Your Data Monetization Strategy with B EYE 

What Is Data Monetization? 

At its core, data monetization describes the art and science of leveraging company-generated data to produce a distinct economic advantage. Whether it’s boosting revenue, slashing costs, or both, businesses are increasingly harnessing their data to drive tangible benefits.  

But it doesn’t stop at just the tangible. Data’s potential stretches further, enabling organizations to forge valuable alliances or secure better terms with suppliers by sharing insights in mutually rewarding exchanges. Recognizing the vast potential of their data troves, some firms have even ventured into offering data-centric services to other businesses on a broader scale. Giants like Facebook and Google spearheaded this wave, leveraging their sprawling platforms to amass and market extensive data assets globally. 

Drive Profitable Insights with Our Data Analytics Services 

Data Monetization Methods 

Internal Data Monetization 

Think of internal data monetization as turning insights into actionable strategies. It’s all about harnessing data and analytics to inform decisions that can bring tangible operational enhancements. Modernizing how a business approaches its data can lead to many benefits. From sharpening marketing efforts and spotting opportunities for upselling to refining the overall customer journey, the goal is clear: elevate profitability. 

External Data Monetization 

Here, the focus shifts from internal optimization to crafting data-driven products or services for third parties. Whether it’s specialized reports, survey outcomes, unique datasets, or insights from customer interactions, the potential is boundless. Essentially, if there’s value in your data that can be quantified and packaged, there’s an opportunity to monetize. Take, for instance, a firm that gathers detailed consumer metrics. Such data can be transformed into insightful packages, granting premium users access to benchmarking insights or guided analytics. 

Types of Data Monetization 

When it comes to monetizing data, organizations must adopt a strategy that grants them the agility and flexibility to maximize the value from vast data resources. Here are some of the most prevalent types of data monetization to help you do just that. 

Direct Data Monetization 

This type hinges on collecting and preserving company data, which can then be marketed to third parties either in its raw form or processed for immediate insights. Examples include: 

  • An e-commerce platform selling shopping behavior metrics to market researchers. 
  • Environmental sensors providing climate-related data for weather forecasting. 
  • Fitness apps sharing aggregated user health stats with health researchers. 

There are three main direct monetization approaches: data-as-a-service, insight-as-a-service, and analytics-as-a-service. Let’s explore them in more detail. 


Essentially a B2C model, this strategy is about delivering raw data. Although it’s simpler to implement and requires minimal preparation, it offers lower revenue potential as buyers only see value once they analyze the data. 


Unlike providing just the raw data, this method offers analytical conclusions. For example, a market research firm might deliver an in-depth report on emerging consumer electronics trends based on multi-source data. As these insights are readily actionable, they generally fetch a higher price. 


A deeper dive than Insight-as-a-Service, this approach gives customers direct access to dynamic analytics and visualization tools. An example might be a finance research firm offering real-time stock market prediction tools based on vast datasets. This model promises substantial revenue but demands rigorous IT and cybersecurity management. 

Indirect Data Monetization 

Here, data is used internally to amplify business performance. For instance, a retailer might analyze purchase data to spot emerging trends and then adapt its inventory accordingly, which leads to greater efficiency and, by extension, potentially enhanced revenue.  

A data-driven business model is a paradigm of indirect data monetization that leverages data across all operational fronts for better efficiency. For instance: 

  • Mining system logs post a server outage to bolster IT responsiveness. 
  • Examining online shopper data to preemptively adjust inventory before festive seasons. 
  • Utilizing sales data to tailor marketing campaigns for maximum engagement. 

The bigger picture? Direct data monetization concerns the selling of data in various forms – raw, insightful, or platform-accessible. As embodied by data-driven business models, indirect monetization optimizes internal operations for heightened profitability. The higher the complexity of the monetization approach, the higher the revenue potential. However, remember that these also demand greater IT and security diligence. 

How to Prepare Your Company for Data Monetization in 6 Key Steps 

For a company to fully leverage the power of data monetization, merely having the right tools is insufficient. Leaders must strategically use and optimize data, considering it a fundamental business component. To steer the company in the right direction, here are some crucial steps: 

The 6 steps you need to take to prepare your company for data monetization

1. Grasp and Evaluate Data

Begin by comprehensively understanding the data your company generates. Conduct an inventory of business processes that naturally produce data and evaluate which datasets are being overlooked or discarded. Once this inventory is complete, study how other corporations utilize similar data. For instance, Netflix extensively uses its viewer data. By analyzing viewership patterns, show preferences, and pause/rewind/replay behaviors, Netflix curates its content and even produces new series and movies tailored to observed audience preferences. 

Moreover, examining data collection trends in the public domain can offer insights into possible industry advancements. When platforms like Facebook or Instagram introduce new features or change their algorithms, they often refine based on vast amounts of user data to increase engagement and serve targeted ads more effectively.  

In addition, observe the data other corporations are collecting publicly. Google’s CAPTCHA images, for example, can hint at their data needs for refining autonomous vehicle algorithms. 

2. Learn from the Trailblazers

Observe how cutting-edge tech startups utilize data, as they can provide valuable insights. Engage with these startups through proof-of-concept contracts or data-sharing agreements to tap into their innovation. 

3. Leverage Established Solutions

Companies often reinvent the wheel by creating in-house data management tools. This approach can lead to inefficiencies and potential missteps. Instead, when a market solution exists and fits your needs, opt for it. Buying established solutions can save time and resources and can ensure a focus on the core data-driven product. 

 Explore Our Business Solutions 

4. Use Data as an Operational Advantage

Storing and collecting vast amounts of data can lead to a competitive edge. Consider the competition between Amazon and traditional retailers. Amazon’s immense data on customer preferences, buying habits, and search patterns allows them to offer personalized recommendations, dynamic pricing, and optimized inventory management. This vast repository of real-time customer data gives them a competitive advantage that many traditional retailers find challenging to bridge.  

Reflecting on a historical example, when the Ford Motor Company integrated the assembly line into its production process, it revolutionized manufacturing by leveraging an innovative method to optimize output. Companies that failed to adapt quickly were left behind. 

5. Ensure Leadership Support and Alignment 

  • Gain support: The push for data monetization should be a top-down initiative. Once top leadership recognizes its potential, it’s crucial to communicate its significance to all stakeholders to ensure cohesive execution.  
  • Set clear goals: Clearly define your goals. Whether you aim for in-house cost reductions or intend to launch a new data-focused business segment, set clear objectives at the outset to avoid misalignment later. 

6. Strategize Data Collection and Audience Targeting 

  • Assess and Extend Data Collection: Examine the data you currently gather, determine its value, and identify gaps. Recognize areas where more data can be captured to serve potential monetization opportunities.  
  • Identify the Audience: Determine who will benefit most from your data. Internal departments, such as sales, can leverage consumer behavior insights. If targeting external entities, ensure that your data provides them with tangible value, potentially through in-depth analysis. 

To succeed in data monetization, you need solid preparation. By following these steps, you can position your organization to maximize the benefits of its data assets while maintaining agility and a competitive edge. 

A Framework for Effective Data Monetization Strategy 

Crafting an effective strategy for data monetization is not just a technological endeavor; it’s a holistic approach that intertwines with a company’s very DNA. From understanding the essence of data monetization to the intricacies of execution and commitment, a clear roadmap is essential. As we explore the framework below, remember that data is your asset; with the right strategy, it can be your powerhouse. 

An Effective Data Monetization Framework

1. The Essence of Data Monetization 

Understand that data monetization is about converting data assets into tangible value in currency, a competitive edge, or other strategic advantages.  

Recognize that perfect data doesn’t exist. The goal is to optimize what you have and capitalize on its potential. 

2. Technology Challenges 

Overcome the challenges presented by technology. 

  • Data Overload & Distribution 

Instead of waiting for perfect infrastructure, use what you have and continuously improve to avoid missing opportunities.  

  • Data Access & Formatting 

Ensure consistency and usefulness in your data. These might require ongoing cleansing, especially with unstructured data.  

  • Data Scalability 

As data volume and complexity grow, ensure your systems scale in terms of storage, processing, and delivery. 

3. Strategic Focus 

Data is dynamic and should be treated as a valuable, evolving asset. Your strategy should be evolutionary, not static.  

Avoid seeing data monetization merely as an IT project; it’s a comprehensive business initiative.  

It’s not just about having a revolutionary plan; execution is equally, if not more, vital. Focus on value incrementally while guided by a broad strategy. 

4. Non-technology-driven Factors 

Non-technology-driven factors include: 

  • Enrichment Roadmap 

Ensure data remains relevant and valuable by updating and analyzing it regularly. 

  • Capture & Dissemination Economics 

Understand the total cost and potential return of your data activities, from raw acquisition to ultimate monetization.  

  • Network Effect & Barriers 

Recognize that data sources adopted extensively might naturally deter competitors or clients from seeking alternatives.  

  • Asset Value & Legal Implications 

Data might have financial, tax, and other implications. Consider its asset value and understand any associated complexities. 

5. Execution & Commitment 

Execution is as crucial as strategy. Even the best strategy will falter without commitment and the right organizational culture.  

To assess where you stand, engage in reflective questions like:  

  • Are you leveraging your data for a competitive edge?  
  • Do you have a systematic approach to data monetization?  
  • Why are you waiting for perfect data when other corporate decisions don’t wait for perfection? 

In conclusion, data monetization isn’t just about leveraging data; it’s about strategic planning, effective execution, and continuous evolution. Start with a clear understanding of where you are, then forge a path forward, ensuring that both strategy and execution align with your business’s overarching goals. 

Data Monetization Use Cases 

In today’s data-driven world, organizations across various industries are quickly realizing the potential of using their data assets to gain a competitive edge. By monetizing data, these organizations are not only tapping into a new revenue stream but also enhancing operational efficiency, boosting innovation, and forging stronger customer relationships. Below, we delve into specific use cases across diverse industries and functions to highlight the transformative power of data monetization. 

Data Monetization Use Cases by Industry 


In the fast-paced world of retail, understanding the customer is paramount. Data monetization in retail primarily revolves around harnessing customer data. By utilizing purchase histories, browsing patterns, and even in-store movements (thanks to IoT), retailers can offer highly personalized promotions tailored to individual customer preferences. And, as we know, personalization boosts sales and customer loyalty. On the back-end, by analyzing this data, retailers can optimize inventory levels, ensuring that popular products are always in stock. Furthermore, sales trend forecasting becomes more accurate, allowing for better strategic planning. Beyond internal benefits, there’s potential in syndicating shopping behavior data to vendors or third-party advertisers, offering them invaluable insights into customer behavior. 

You May Also Like: What Is Retail Analytic? [Executive Guide + Glossary] 


The manufacturing sector has been transforming with the advent of the Internet of Things (IoT) and Industry 4.0. These innovations generate vast amounts of data. By monetizing this data, manufacturers can predict maintenance needs, reducing downtime and ensuring production efficiency. Insights about equipment performance and production methodologies can be so valuable that manufacturers may even find competitors willing to pay for them. Moreover, sharing data with equipment vendors can lead to better machinery designs in the future, creating a feedback loop that benefits the entire industry. 

Keep Exploring: Data-Driven Giants: How Big Players Optimize with Manufacturing Analytics 


In an industry that thrives on assessing and pricing risk, data is invaluable. Modern insurance companies are turning to data analytics to refine their risk assessment models. By analyzing vast historical datasets, insurers can craft more tailored policy pricing, ensuring profitability while also offering competitive rates. Beyond internal analytics, there’s a burgeoning market for aggregated, anonymized insurance data. Research institutions, market analysts, or even city planners could benefit from insights into trends in policy claims or customer demographics. 

Life Sciences 

The life sciences sector, particularly pharmaceuticals and biotech, produces a wealth of data through patient trials, research, and development. Monetizing this data can drive innovations in drug development, speeding up the time-to-market for new drugs. Moreover, insights about treatment efficacies, side effects, and long-term outcomes can be shared (with due considerations to privacy) with healthcare providers, helping them make informed treatment decisions. This collaborative approach can lead to improved patient outcomes and even groundbreaking treatments. 

Discover more: 7 Key Benefits of Data Analytics in the Life Science Industry 


The healthcare system generates an enormous amount of patient data. While patient privacy is of utmost importance, anonymized data can be shared with research institutions or pharmaceutical companies, providing them with invaluable insights and, in turn, generating revenue for healthcare providers. Internally, data analytics is revolutionizing patient care. By analyzing patient outcomes against treatment methodologies, healthcare providers can refine their approaches, leading to better patient outcomes and reduced operational costs. 

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Data Monetization Use Cases by Function 

Sales & Marketing 

Sales and marketing teams have access to an unprecedented amount of customer data. This data can be harnessed to refine marketing strategies, targeting potential customers with pinpoint precision. Predictive analytics can be employed to foresee future buying trends, allowing businesses to be proactive rather than reactive. Additionally, there’s a burgeoning market for customer behavior insights. Partners, advertisers, or even competitors may be interested in purchasing this data, seeing it as a way to refine their own strategies. 

Supply Chain 

Optimal supply chain management is all about having the right products in the right quantities, places, and times. Data from logistical operations can be used to achieve this, optimizing the flow of goods and predicting stock needs with high accuracy. Furthermore, partners in a supply chain can benefit from shared insights about demand fluctuations, ensuring the entire chain operates seamlessly. 

Discover How B EYE empowers businesses to have full visibility on their Supply Chain 


Financial data has always been closely guarded, but with the right privacy measures in place, there’s potential for monetization. Trends in financial data can be sold to market analysts, providing them with insights into market movements. Internally, this data can predict market shifts, drive investment decisions, and ensure financial stability. 

Human Resources 

The realm of human resources is more data-driven than one might think. Data on employee performance, talent acquisition, and workplace satisfaction can be analyzed to predict hiring trends and optimize recruitment processes. Insights about the workforce, such as emerging skills or workplace preferences, can be invaluable to recruitment agencies or even educational institutions, making it a potential revenue stream. 


Everyday operational tasks generate a lot of data. By analyzing it, businesses can streamline processes, predict resource needs, and enhance overall efficiency. Such insights can also be valuable to industry peers or partners, who might be willing to pay for knowledge that can help them optimize their operations. 

Across each of these sectors and functions, the common thread remains the same: in a data-rich environment, those who can effectively leverage and monetize this data stand to gain immensely, both in terms of revenue and operational efficiency. 

Data Monetization in Action: Real-World Examples 

Whether tailoring user experiences, predicting market trends, or unveiling new revenue streams, data monetization is a testament to the transformative power of information in the modern era. Let’s explore some stellar examples of how global giants and innovative startups alike are capitalizing on their data, turning bytes into tangible business outcomes. 

Netflix’s Content Strategy 

One of the most recognized data monetization successes is Netflix’s approach to content. By analyzing viewership data, including watching patterns, pausing, rewinding, and user ratings, Netflix crafts and purchases content tailored to their audience’s desires. This meticulous data-driven approach led to the creation of hit shows like „House of Cards,“ a series greenlit based on the confidence derived from user data, showcasing how data can be monetized indirectly through content investment and increased subscriptions. 

Walmart’s Data-Driven Commerce 

As one of the world’s largest retailers, Walmart generates an enormous amount of data daily, from transactions, online activity, and more. They’ve leveraged this data mountain to make more informed decisions on inventory management, supply chain operations, and pricing strategies. Beyond internal use, Walmart established its own media arm, Walmart Connect, which uses the company’s vast troves of shopper data to provide targeted advertising services to brands. The latter not only optimizes the shopping experience for customers but also creates a significant revenue stream for the company by monetizing its shopper data. 

Waze and Traffic Data 

Waze, the community-based traffic and navigation app, is a prime example of monetizing user-generated data. By collecting real-time location data from its users, Waze provides real-time traffic conditions and optimal routing for drivers. This traffic data also becomes invaluable for local businesses and advertisers. Using Waze’s advertising platform, businesses can send location-based promotions or ads to drivers nearby, turning data into direct advertising revenue. 

Under Armour’s Connected Fitness 

By acquiring multiple fitness apps, Under Armour amassed a vast trove of health and fitness data. Beyond just tracking workouts or meals, they monetized this by offering premium insights, personalized health recommendations, and partnering with health-focused companies. This data-driven approach didn’t just improve athletic performance but also drove sales for Under Armour’s core products by deeply embedding them in users’ health routines. 

GE’s Predix Platform 

General Electric’s Predix platform illustrates how industrial data can be transformed into a business. Predix is an application platform for the Industrial Internet, collecting and analyzing data from industrial machines. Manufacturers, airlines, or utilities can use the platform to predict maintenance needs, optimize machine performance, and even develop new business models based on machine-as-a-service, all showcasing the vast potential of data monetization in the industrial sector. 

Data Monetization FAQs 

1. What is data monetization? 

Data monetization refers to the act of converting raw, unused data into tangible economic value. This could mean using collected data to gain insights, make more informed decisions, improve operations, or even sell that data to interested third parties. It’s about harnessing the potential of data to drive revenue, efficiencies, or other measurable benefits. 

2. How to estimate market size for data monetization? 

Estimating the market size for data monetization involves understanding the potential demand for your data in a specific industry, its competitive landscape, and the value it can offer. Begin by analyzing the scope of data users and potential applications. Then, review existing market research, competitor benchmarking, and the willingness of potential customers to pay for such data. It’s also vital to keep an eye on regulatory restrictions and industry standards, which can influence market size. 

3. How to monetize my data? 

Monetizing your data starts by understanding its unique value proposition. Identify who would benefit from your data, then consider how to package or present it. This might involve refining or analyzing data to create actionable insights. Methods include: 

  • Selling raw data. 
  • Licensing your dataset. 
  • Offering data analytics services. 
  • Even creating data-powered applications. 

Ensure data privacy regulations are met and user consent is appropriately managed. 

4. What is an example of data monetization strategy? 

A classic example of a data monetization strategy is the way airlines utilize passenger data. By analyzing travel patterns, airlines can offer personalized promotions or bundles. They can also partner with hotels or car rental services to provide targeted packages, generating additional revenue streams. This strategy, centered around understanding customer behavior and preferences, enables airlines to enhance customer experience while driving additional revenues. 

5. What are the three data monetization models? 

The three primary data monetization models are: 

  • Selling Data — directly selling raw or refined data to interested parties. 
  • Data-As-A-Service (DaaS) — providing data on a subscription basis, often through APIs, where customers can access up-to-date data when required. 
  • Insight-As-A-Service — rather than selling raw data, businesses analyze it to provide actionable insights or consultancy services to their clients, offering a solution rather than just information. 

Enhance Your Data Monetization Strategy with B EYE 

Crafting a successful data monetization strategy demands more than just understanding your data; it requires actionable insights, advanced analytics, and industry-specific solutions. At B EYE, we specialize in delivering precise analytics solutions that directly impact your bottom line. If you’re looking to turn your data into a genuine asset that drives revenue and competitive advantage, let B EYE be your guide. Schedule a consultation with us, and let’s get started on your tailored data monetization roadmap. 

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