The global data monetization market size was valued at USD 3.0 billion in 2023 and is projected to reach USD 22.5 billion by 2032, registering a CAGR of 25.1% during the forecast period (2024-2032). The increasing volume and variety of data generated by various sources, such as social media, IoT devices, cloud computing, mobile applications, and e-commerce platforms, drives the Data Monetization market growth.
Data monetization is obtaining financial gain or value from data assets. Data assets are unprocessed, consolidated, or inferred data that organizations collect, store, analyze, and disseminate. Data monetization can take several forms, including selling data to third parties, developing data-based products or services, enriching existing offers with data, and reducing costs or risks through data use. Data monetization can be divided into two approaches: direct and indirect. Direct data monetization involves trading data for monetary remuneration, whereas indirect data monetization encompasses using data to improve corporate performance or consumer pleasure.
The market is being driven by the continuous rise in the volume of enterprise data, technological advancements in big data and analytics solutions, and the growing importance of generating new revenue streams from organizations' data volumes. However, security and privacy concerns are expected to slow market growth. Furthermore, increased adoption of data monetization among telecom service providers and increased awareness of the potential benefits of data monetization represent a significant opportunity for market growth.
With the proliferation of digital devices and platforms, massive amounts of data are generated every second. This wealth of data allows organizations to extract and monetize value effectively. The proliferation of Internet of Things (IoT) devices has significantly contributed to increased data generation. These devices range from smart thermostats and wearable fitness trackers to industrial sensors and connected vehicles. For example, an intelligent factory may contain thousands of sensors that collect real-time data on machinery performance, environmental conditions, and production metrics. According to a Statista survey, IoT-connected devices will exceed 50 billion by 2023 and 75 billion by 2025. However, Techjury estimates there will be 27 billion connected IoT devices by 2025 and 25.4 billion by 2030.
Furthermore, social media platforms such as Facebook, Twitter, and Instagram collect massive amounts of data through user interactions, content creation, and advertising activities. Every like, share, comment, and click generates valuable data that can be used for targeted advertising and analytics. For example, in 2023, Facebook generated 120 zettabytes of data, which is expected to increase to 181 zettabytes by 2025.
Moreover, the rise of e-commerce has resulted in a massive increase in data generated by online transactions, product searches, customer reviews, and website interactions. Retailers can use this information to personalize marketing campaigns, optimize pricing strategies, and enhance the overall customer experience. Global e-commerce sales are expected to reach USD 5.8 trillion by 2023, up 7.6% from 2022. This will account for 15.4% of total retail sales in 2023, up from 14.7% in 2022. By 2027, e-commerce sales are expected to exceed USD 8 trillion, up 39% from 2023.
Heightened concerns about data privacy and security and stringent regulatory requirements such as GDPR and CCPA present significant challenges to data monetization efforts. Organizations must navigate complex legal frameworks to ensure compliance with data protection regulations, which may limit the collection, use, and sharing of personal data for commercial purposes. The General Data Protection Regulation (GDPR), enacted by the European Union (EU) in 2018, imposes stringent requirements for collecting, processing, and protecting EU residents' data. GDPR requires organizations to obtain individuals' explicit consent for data processing activities, place appropriate security measures to protect personal data, and follow data minimization and purpose limitation principles.
Similarly, in 2023, India passed the Digital Personal Data Protection Act to protect personal data while promoting privacy and cybersecurity. This Act establishes regulations and rules for entities operating in India to collect, store, process, and transfer personal data. The act also establishes strict data collection, processing, storage, and transmission guidelines.
Moreover, according to a Cisco survey, 58% of organizations worldwide reported increased customer or user privacy concerns due to GDPR and similar regulations. Also, Gartner predicted that by the end of 2023, 65% of the world's population would have their data protected by modern privacy regulations. This represents an increase from 10% in 2020 and is expected to rise to 75% by 2024.
The proliferation of cloud computing, big data technologies, and data management platforms makes it easier to collect, store, process, and analyze massive amounts of data on a large scale. Cloud-based data monetization platforms, data marketplaces, and analytics solutions enable organizations to monetize their data assets effectively by providing the necessary infrastructure and tools.
Additionally, cloud computing platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud offer organizations scalable and cost-effective infrastructure for storing, processing, and analyzing massive amounts of data. These cloud platforms provide various data services, such as data lakes, warehouses, and analytics tools, allowing organizations to create adaptable and agile data monetization solutions. For example, Amazon Web Services (AWS) generated USD 90.8 billion in revenue from cloud services in 2023, a 13% increase from the previous year. AWS reported USD 24.2 billion in revenue for the fourth quarter of 2023, up 13% year on year.
Furthermore, big data technologies such as Hadoop, Spark, and Apache Kafka enable massive datasets' real-time ingestion, processing, and analysis. These technologies enable organizations to handle various data types, perform complex analytics, and extract actionable insights from structured and unstructured data sources. For example, a retail company can use big data analytics to analyze customer purchasing patterns and optimize pricing strategies.
Study Period | 2020-2032 | CAGR | 25.1% |
Historical Period | 2020-2022 | Forecast Period | 2024-2032 |
Base Year | 2023 | Base Year Market Size | USD 3.0 billion |
Forecast Year | 2032 | Forecast Year Market Size | USD 22.5 billion |
Largest Market | North America | Fastest Growing Market | Asia-Pacific |
The global data monetization market analysis is conducted in North America, Europe, Asia-Pacific, the Middle East and Africa, and Latin America.
North America is the most significant global data monetization market shareholder and is estimated to grow at a CAGR of 24.9% over the forecast period. North America dominates the data monetization market, owing to the large and diverse amount of data generated by expanding several sources, including social media, IoT devices, cloud computing, mobile applications, and e-commerce platforms.
Furthermore, the North American region is seeing an increase in data breaches. The average number of violations in the United States has been increasing in recent years, according to data from the Identity Theft Resource Center (ITRC). For example, there will be 3,205 data breaches in the United States in 2023, affecting 353 million people. Over 80% of these breaches involved data stored in the cloud, a common target for hackers due to misconfiguration.
Asia-Pacific is anticipated to exhibit a CAGR of 25.5% over the forecast period. This is because telecom service providers have recognized the value of collected data and have begun to monetize the information to improve their profitability-per-user ratio through data monetization, which is the primary driver of the market.
Additionally, various regional financial service institutions (FSIs) have valuable and extensive data that can be leveraged to generate a significant return. For example, converting data to digital will enable FSIs to compete digitally and reap the benefits of digital transformation. Furthermore, once converted from data to digital, these assets have the potential to generate significant new revenue streams. FSIs can also monetize data by improving data analytics, developing data-driven solutions, and creating platforms for insights and services.
Europe is critical to the global market because it relies heavily on data-driven innovation and competitiveness across various industries and sectors, including BFSI, retail, telecom, healthcare, and manufacturing. Furthermore, market participants are engaged in multiple strategic initiatives to increase their market share. For example, in July 2023, Yandex, a prominent European technology company, began testing a novel advertisement placement solution for Telegram conversations. Over 350,000 advertisers use the Yandex Advertising Network, resulting in an impressive average of 4.5 billion ad placements daily. Yandex offers extensive advertising solutions in 50 countries around the world. Yandex successfully merged its efficient advertising platform with Telegram, a popular messaging app, allowing channel owners to monetize their content. These factors help to expand the regional market.
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The global data monetization market is segmented based on organization size, method, and vertical.
The market is further segmented by organization size into Large Enterprises and SMEs.
The large enterprises segment has the largest market share. Large enterprises are organizations with a significant scale of operations, typically distinguished by extensive resources, diverse business units, and a global presence. These companies have a lot of money, a lot of customers, and a lot of market power. Due to their size and resources, large enterprises can often invest in advanced technologies, strategic partnerships, and talent acquisition to drive innovation and growth. They may also employ complex organizational structures and hierarchical decision-making processes. Large corporations are critical in driving economic growth, industry trends, and market dynamics across multiple industries.
Small and medium-sized enterprises (SMEs) operate on a smaller scale than large enterprises. While the definition of SMEs varies by country and industry, they are typically distinguished by limited resources, fewer employees, and a smaller market reach. Small and medium-sized enterprises (SMEs) are essential in driving innovation, fostering entrepreneurship, and fueling job creation in economies worldwide. Regardless of size, SMEs can be highly agile, adaptable, and innovative, leveraging technology, niche markets, and strategic partnerships to compete effectively in their respective industries. They may face unique challenges such as limited capital, resource constraints, and scalability issues, but they also present opportunities for growth, innovation, and market disruption.
Based on method, the market is fragmented into Data as a Service, Insight as a Service, Analytics-enabled Platform as a Service, and Embedded Analytics.
The analytics-enabled Platform as a service segment currently dominates the global market. Analytics-enabled Platform as a Service (PaaS) is a method in which organizations provide cloud-based platforms that include analytics capabilities for developing, deploying, and managing data-driven applications and solutions. Analytics-enabled PaaS offerings give developers and data scientists a comprehensive set of tools, libraries, and APIs for creating and integrating analytics features into their applications without the need to manage infrastructure or software stacks. These platforms may include features like data ingestion, data preparation, data modeling, and visualization, allowing businesses to create scalable, intelligent applications that use real-time insights and predictive analytics. Analytics-enabled PaaS providers may provide flexible pricing models based on usage or subscription, catering to organizations looking to accelerate innovation and time-to-market with data-driven solutions.
Data as a Service (DaaS) is a method by which organizations provide customers or partners with on-demand access to their data via the Internet. Data as a service (DaaS) offerings typically involve providing data in a standard format via APIs or web services, allowing users to access, consume, and integrate the data into their applications, processes, or analytics workflows. DaaS providers may provide diverse datasets, such as market, demographic, and industry-specific data, to cater to various use cases, including market research, business intelligence, and predictive analytics. DaaS allows businesses to monetize their data assets by providing subscription-based access or pay-per-use pricing models, which generate revenue through data licensing agreements and partnerships.
The market can be further bifurcated by vertical type into BFSI, E-commerce and Retail, Telecommunications and IT, Manufacturing, Healthcare, Energy, and Utilities.
The IT and telecommunications segment dominated the overall data monetization industry in 2023 and is expected to continue this trend throughout the forecast period. The telecommunications and IT sector includes companies that provide telecommunications services, internet service providers (ISPs), software vendors, and technology firms. Data monetization opportunities in this sector revolve around using network data, customer usage data, and device data to improve network performance, optimize service delivery, and provide value-added services.
For example, telecommunications companies can monetize network data by providing advertisers or content providers insights into network traffic patterns. In contrast, software vendors can use usage data to improve product features and user experiences. Massive data growth in the telecommunications and IT sectors and an increased need to generate additional revenue streams through data monetization services drive market growth.
The retail and e-commerce segments are expected to grow the most in the data monetization market. E-commerce and retail businesses include online retailers, physical stores, e-commerce platforms, and retail chains. In this industry, data monetization entails using customer data, purchase history, browsing behavior, and inventory data to tailor marketing campaigns, optimize pricing strategies, and improve supply chain management. For example, e-commerce platforms can monetize customer data by providing targeted advertising opportunities to third-party sellers. In contrast, retail chains can use location data to optimize store layouts and product placements for maximum sales.
E-commerce players increasingly use data monetization solutions to sell or collaborate with brands to analyze data and generate additional revenue. Furthermore, the increased focus of small and medium-sized businesses on data monetization strategies to create revenue streams drives market growth. For example, the Chinese e-commerce behemoth focuses on collecting consumer data. Furthermore, companies like Google, Amazon, LinkedIn, and Netflix are well-known for monetizing information to increase revenue and market share.