Data Monetization Explained: How to Turn Data into Profitable Assets
Data monetization is the process of strategic analysis and practical implementation that allows you to use data as a source of income. This process allows companies to convert collected data into a monetizable value, just like other assets. Information capitalization is becoming a strategy that allows companies to recover their investments in IoT and IIoT infrastructures, systems, and platforms, accelerate their development path, and develop new business scenarios.
Think of data monetization as transforming unmined data into a treasure trove of valuable insights, where knowledge is the currency, and the process is like a journey of discovery. In this article, we dive deeper into the growing importance of data-driven monetization strategies.
Why Implement Data Monetization?
So, what makes many businesses increasingly turn to data as a source of revenue? Let’s discuss a few reasons for developing and testing various data monetization models.
- Diversifying income streams. Businesses recognize the importance of diversifying revenue sources to mitigate market volatility and economic uncertainties. Leveraging data as a revenue source allows them to reduce dependency on a single source of income.
- Competitive edge. In a rapidly evolving business landscape, data-enhanced products provide a competitive advantage. Companies that harness data effectively can make more informed decisions and deliver tailored products and services, ultimately outperforming competitors and driving long-term customer loyalty by data-driven personalization .
- Addressing operational challenges. Data monetization helps businesses tackle operational challenges, such as managing and storing large volumes of data which can be expensive. By monetizing data, companies can recover some of these costs or potentially fund more efficient data storage solutions.
Many data types can be monetized, depending on their relevance to specific industries and use cases. Here are some common data types that organizations can monetize:
- Customer data, such as customer demographics, preferences, and behavior.
- IoT and sensor data, including data generated by the Internet of Things (IoT) devices and sensors in various industries, such as manufacturing, agriculture, and healthcare
- Financial institutions can monetize transaction data, credit scores, and investment insights to offer personalized financial services and investment advice.
- Healthcare providers can monetize patient health records, medical imaging data, and wearable device data to improve patient care, develop new treatments, and offer telemedicine services.
- Location-based data, including geospatial and GPS data, can be used for location-based marketing, logistics optimization, and urban planning.
- Social media platforms can monetize user-generated content, user interactions, and sentiment analysis to provide advertisers with targeted advertising and audience insights.
- Data related to supply chain operations, including inventory levels, shipping data, and demand forecasts, can be monetized to optimize logistics and improve supply chain efficiency.
These are just a few examples, and the potential for data monetization extends to virtually any data type that holds value and relevance within specific industries and business contexts.
But it is not enough to just have data. Data valuation lies in the revenue-generating insights it provides, the processes it optimizes, and its ability to enable better decision-making. Data and analytics can provide a valuable business asset that improves business decisions, drives digital business transformation, and generates new revenue for your organization.
To work it to your advantage do it right, you should move beyond false assumptions about the data monetization ecosystem and address the barriers that cause some companies to fail. Check the table for a few tips:
|Data silos||Break down data silos by implementing integrated data management solutions and fostering a culture of data sharing across the organization|
|Privacy and compliance||Address privacy and regulatory concerns by implementing robust data governance and compliance frameworks and by obtaining explicit consent for data usage.|
|Lack of expertise||Overcome the shortage of data expertise by investing in data education and training for your team or collaborating with external experts and partners.|
|Security concerns||Enhance data security through encryption, access controls, and regular security audits to build trust with data partners and customers.|
Revenue Streams in Data Monetization
Revenue streams in data monetization refer to how organizations generate income by monetizing data assets. Here are three commonly used ones:
1. Data licensing and sales
Companies with valuable datasets can license or sell them to third parties, including businesses, researchers, or data brokers. Data can be offered as raw datasets, aggregated information, or through subscription services. For instance, a weather data company licenses real-time weather data to agricultural firms for informed decision-making.
2. Data-driven products and services
Firms can develop revenue-generating data-driven products or services, leveraging internal data to provide unique insights or solutions. An example is a healthcare analytics company creating a software-as-a-service (SaaS) platform for hospitals to optimize patient scheduling, charging a subscription fee.
3. Targeted advertising and marketing
Companies use their data to provide targeted advertising and marketing services. This involves analyzing customer behavior and demographics to help advertisers reach their intended audience more effectively. Social media platforms like Facebook employ user data for targeted advertising, maximizing data monetization ROI.
4. Subscription model
Companies can create subscription-based data services, where customers pay a recurring fee to access specific datasets, analytics, or insights. Subscriptions can be tailored to various user needs and include tiered pricing for different access levels or features.
Additionally, companies can participate in data marketplaces, connecting data providers with buyers and facilitating secure transactions.
Real-World Data Monetization Examples
Let’s look at what strategies for data profits industry leaders use.
Case Study 1: Waze – Location-based advertising
Waze, the navigation app, effectively monetizes data by offering location-based advertising to businesses. Using real-time traffic and user location data, Waze provides targeted advertisements to drivers, such as nearby restaurants and gas stations.
Case Study 2: Airbnb – Personalized travel recommendations
Airbnb, the online travel marketplace, harnesses data monetization opportunities to enrich travelers’ experiences. This data-driven approach enhances user satisfaction and booking rates by analyzing guest booking history, destination preferences, and accommodation choices. Approximately 40% of bookings on Airbnb come from personalized recommendations, illustrating the significance of data monetization in the travel industry.
Case Study 3: New York Times – Revenue diversification
The New York Times launched a program called «NYT Data,» where they monetize their vast collection of articles, headlines, and metadata. This custom program provided valuable insights to businesses, researchers, and organizations. Moreover, it helped them reduce their reliance on traditional advertising revenue and subscriptions.
Exploring various data monetization examples can provide valuable insights into how businesses can extract value from their data assets, whether through personalized recommendations, marketplace insights, or targeted advertising.
How to Develop Data Monetization Strategy: 5 Essential Steps
To implement a data monetization strategy, a company must first ask itself what data it needs to grow. Also, it is beneficial to determine in advance which data will become economically viable if it is enriched into valuable information through analysis and processing.
We compiled a list of five tips for strengthening your strategy:
1. Define valuable data
First and foremost, it’s essential to identify which data holds real value for the company. While data diversity is valuable, not all data is equally useful. To determine whether data is valuable, assess its relevance to your business objectives, its potential to provide insights, and its capacity to drive informed decision-making, ensuring it aligns with the company’s strategic goals.data monetization tools
2. Streamline data processing
Often, analysts spend a significant amount of time cleaning data. Thus, it’s crucial to establish efficient data processing frameworks. Streamlining data processing can include automating data cleaning using scripts, adopting data quality tools, and implementing data validation checks. Evaluation parameters (KPIs) are frequently not optimized, which can compromise data quality or invalidate it — a critical aspect for monetizing data insights.
3. Leverage edge computing
To reduce unnecessary data movement to the cloud, consider enabling local processing through edge computing solutions. This approach ensures real-time data processing while reducing transmission costs and cloud analytics expenses.
4. Assess analytical maturity
Determine the company’s maturity level concerning analytics and professional skills. Skilled employees are essential for continually improving analytical processes and driving innovation in core business functions. According to a 2020 study, 39% of Italian companies reported lacking specific internal competencies and not experimenting with AI. Collaborating with experienced partners such as data analytics solution companies can expedite product development and benefit from higher expertise.
5. Embrace data sharing
Consider establishing a data-sharing policy not only internally but also externally, involving partners and suppliers. For instance, shared supply chain data can facilitate data monetization, as 87% of decision-makers express interest in utilizing external data to expand their business cases.
Leveraging Data Internally: Optimizing Operations and Growth
Utilizing internal data is critical for enhancing operations and driving business growth. You can use data monetiation platforms to achieve the objectives below:
- Informed decision-making. Internal data gives businesses valuable insights into their operations, customer behavior, and market trends. This data-driven decision-making enables companies to make informed choices rather than relying on intuition or historical practices.
- Process optimization. Analyzing internal data can help identify bottlenecks, inefficiencies, and areas for improvement within the organization. By optimizing processes based on data-driven insights, companies can streamline operations, reduce costs, and enhance productivity.
- Customer insights. Internal data helps companies gain a deeper understanding of their customers. By monetizing customer data, businesses can tailor their products, services, and marketing strategies to meet specific customer needs, increasing customer satisfaction and loyalty.
- Competitive advantage. Companies that effectively leverage internal data often gain a competitive edge. They can identify emerging market trends, respond swiftly to changes, and innovate more effectively than competitors without data-driven insights.
Let’s examine examples of how companies benefit from internal data utilization clearly by comparing before and after.
Before: Netflix began as a DVD rental service with limited data on customer preferences.
After: By analyzing viewing habits and user ratings, Netflix transitioned into a streaming giant. Its recommendation algorithms, driven by internal data, now play a vital role in user engagement and content selection.
Before: Amazon started as an online bookstore with limited product offerings.
After: By using internal data on customer preferences, browsing history, and buying habits, Amazon evolved into a global e-commerce giant that offers an extensive range of products. Internal data power its personalized recommendations and targeted marketing.
Before: UPS relied on manual route planning for its delivery trucks.
After: Utilizing internal data on package volume, traffic patterns, and weather conditions, UPS implemented advanced routing algorithms. This data-driven approach optimized delivery routes and reduced fuel consumption.
These drastic changes illustrate how companies have transformed and thrived by harnessing internal data based on a predefined data monetization strategy. By doing so, they have not only improved their operations but also achieved substantial business growth in their respective industries.
Data Monetization Challenges
Despite discovering data-enabled revenue streams, organizations must prioritize data security measures when building a data monetization roadmap. Unfortunately, there already exist multiple unsuccessful use cases listed below.
1. Data privacy and compliance
Ensuring data privacy compliance with regulations, such as GDPR and CCPA, while monetizing data can be complex. Mishandling personal or sensitive information can result in legal and financial penalties, eroding customer trust.
In 2018, British Airways suffered a major data breach that exposed the personal and financial information of over 400,000 customers. The breach occurred due to inadequate security measures. As a result, the company faced a substantial fine of £20 million (later reduced from the initial £183 million proposed) This incident impacted British Airways’ reputation and raised awareness about the importance of data protection compliance in data monetization metrics.
2. Data quality and governance
Poor data quality can undermine the value of monetized data. Inaccurate, incomplete, or inconsistent data can lead to erroneous insights and decisions, impacting the credibility of data-driven business models.
A global e-commerce platform relied on product data from various suppliers to populate its marketplace. However, product descriptions, pricing, and availability inconsistencies across different datasets led to customer complaints and decreased user trust. To address this issue, the company had to invest in data cleansing and standardization to restore customer confidence and maintain the credibility of its platform.
3. Security risks
Ongoing vigilance against data breaches and cybersecurity risks remains paramount. Only robust security measures can protect data assets from unauthorized access, cyberattacks, and breaches that could result in reputational damage and financial losses.
In 2017, Equifax, a major credit reporting agency, experienced a massive data breach that exposed sensitive personal and financial information of approximately 143 million Americans. The breach occurred due to a vulnerability in Equifax’s website software that went unpatched. This security lapse resulted in substantial financial losses and legal repercussions for Equifax, emphasizing the critical importance of robust cybersecurity measures when handling sensitive data.
These challenges underscore the importance of a comprehensive approach to data monetization strategy that prioritizes data privacy, quality, security, and strategic alignment to maximize the benefits while mitigating risks. Lightpoint offers data engineering services to ensure your informational assets are handled responsibly — and with benefits.
Future Trends in Data Monetization Strategies
The data monetization market size is expected to grow from USD 4.19 billion in 2023 to USD 10.41 billion in 2028, at a CAGR of 19.98% during the forecast period (2023-2028). Here are a few assumptions on how data monetization will evolve in the coming years.
- Empowering independent software vendors: Independent software vendors use self-service business intelligence to boost app revenue and stand out. Therefore, self-service BI can contribute to application ROI by increasing adoption and improving operational efficiency.
- Data processing accelerated by emerging technologies: Data growth has been accelerated by new technologies that are driven by and contributing to exponential data growth. These technologies are edge computing, 5G networks, the Internet of Things (IoT), AI/ML, augmented and virtual reality (AR/VR), and blockchain. Such an unprecedented increase in data volume and variety will fuel the adoption of data monetization innovations.
- Navigating post-pandemic business challenges: Due to the COVID-19 pandemic, almost every industry experienced volatility, risk, and changes in customer purchasing behavior. Companies worldwide are forced to change their operations significantly, such as downsizing and diversifying products and services. To quickly address these changes, companies will adopt or leverage new data monetization strategies through agile risk management, artificial intelligence systems, and data analytics to improve business operations amid the post-pandemic recession.
The increasing practice of data generation and data collection in enterprises is expected to drive market growth and shape new revenue streams among data monetization trends.
Data monetization is a transformative process that extracts value from untapped or unstructured enterprise data, enhancing cost reduction and revenue generation. This approach has gained momentum due to increased awareness of, technological advancements, and the pursuit of fresh growth opportunities. Businesses in multiple industries already possess the following benefits:
- Create new revenue streams by selling valuable data insights, products, or services
- Reduce operational costs by optimizing data management, storage, and analysis processes
- Harness the potential of their data assets
- Improve the overall customer experience by tailoring products and services to customer preferences
The continuous growth in enterprise data volume and the evolving landscape of big data and analytics solutions underscore the importance of leveraging data for revenue generation. To learn how to gain data-driven revenue in your organization, schedule a call with Lightpoint’s expert.