Data monetization empowers organizations to use their data assets and artificial intelligence (AI) capabilities to create tangible economic value. This value exchange system uses data products to enhance business performance, gain a competitive advantage, and address industry challenges in response to market demand.

Financial benefits include increased revenue through the creation of adjacent industry business models, accessing new markets to establish more revenue streams, and growing existing revenue. Cost optimization can be achieved through a combination of productivity enhancements, infrastructure savings and reductions in operating expenses.

In 2023, the global data monetization market was valued at USD 3.5 billion, and experts project it to reach USD 14.4 billion by 2032, demonstrating a compound annual growth rate of 16.6% from 2024 to 2032.

Treating data as a strategic asset

Data is one of the most valuable intangible assets for organizations. Therefore, adopting a holistic approach that prioritizes data-driven business transformation helps optimize value extraction. This transformation harnesses the power of data within the organization, enabling enterprise-wide cost optimization and unlocking net new direct revenue opportunities.

When it comes to data optimization, most organizations focus solely on infrastructure cost reduction. However, those that embrace data-driven business transformation strategies can multiply the benefits by considering revenue growth potential, optimizing costs across infrastructure, development, maintenance and enhancing data security and compliance.

Critical aspects of data-driven business transformation are the overall data monetization strategy and how data products are used. Data insight and AI automation drive cost optimization with predictive maintenance, process automation and workforce optimization. AI automation substantially reduces data security and compliance risks by proactively identifying and analyzing the severity, scope and root cause of threats before they impact the business.

The net effect of data-driven business transformation is increased compliance, productivity and effectiveness via automation across different business units, such as sales, marketing and services. This leads to revenue uplift through opportunities to create new services and channels.

Identifying data products

Industries across the board are experiencing a surge in enterprise data volume, presenting both challenges and opportunities. These challenges, along with specific industry needs and use cases, influence the types of data products organizations or markets require.

Data products are assets developed from a company’s internal data sources or by combining internal and public data, augmented with AI to extract unique insights that help drive business decisions. Managed as products, these data assets come with defined service contracts, repeatable delivery methods and a clear value proposition.

The banking industry, for example, faces the following challenges:

• Competition from agile and innovative financial technology and challenger banks.
• High degree of regulatory control.
• Need to protect sensitive information.
• Organizational data silos that impede a unified customer experience.
• Pressure to increase margins and identify new revenue streams.

To address these challenges, organizations create relevant use cases that address their specific needs, as well as the needs of the market at large. The following sample use cases show associated data products and corresponding financial benefits.

Data products can be created for internal use across various functions or business units. When an organization shares its data internally and consistently to improve efficiency and achieve qualitative or quantitative benefits, it is referred to as internal data monetization.

Data products can also be created for wider external consumption across multiple organizations and ecosystems. When data is shared externally to achieve strategic and financial benefits, it is referred to as external data monetization.

AI-driven data platform economics

An AI-driven organization is one where AI technology is fundamental to both value creation and value capture within the business model. A data monetization capability built on platform economics can reach its maximum potential when data is recognized as a product that is either built or powered by AI.

In the collection-led model, data from external and internal sources, such as data warehouses and data stores, is fed into analytical tools for enterprise-wide consumption. At the enterprise level, business units identify the data they need from source systems and create data sets tailored exclusively to their specific solutions. This leads to a proliferation of organizational data and added pipeline complexity, which can pose challenges in upkeep and use for new solutions, directly affecting costs and timeliness.

As enterprises shift from collection-led to product-led models, data products are created by using external and internal data sources, along with analytical tools. Once developed, these data products can be made available to business units within the organization for real-time data sharing and analytics. Also, these data products offer opportunities for monetization through ecosystem partnerships.

In a platform-driven approach, business units build solutions by using standardized data products and combining technologies to reduce work, simplify the enterprise data architecture and decrease time to value.

The data platform offers data-enriched data products that use machine learning, deep learning and generative AI. Those AI-driven data products can virtualize and integrate disparate data sources to create domain-specific AI models using proprietary enterprise data. Data platform services enable data products to be provided as SaaS services, a single data mesh deployed across the hybrid cloud and authenticated, secure and audited data product delivery.

When organizations connect their valuable data and AI assets to wider user groups, they can use the multiplier effect from the consumption and evolution of data products, as well as the market reach from scalable cloud distribution.

The economic impact of data monetization

Organizations usually develop a business case spanning 3 to 5 years to gain a comprehensive view of short-, mid- and long-term economic benefits. Successful cases address market demands to remain competitive, foster scalability, and constantly pursue cost optimization and revenue enhancement opportunities.

The graph above shows the incremental revenue potential from data monetization over a 5-year period. In an example organization with USD 2 billion in revenue, the baseline revenue from data is USD 5 million (0.25% of the overall revenue). If the organization follows the traditional approach, revenue from data might grow by 10% year-on-year, from USD 5 million to USD 6.7 million in three years, just 1.34 times the baseline revenue.

In contrast, data monetization can act as a force multiplier and contribute to upwards of a 1% increase in a company’s revenue. With data monetization capabilities, revenue from data could potentially grow from USD 5 million to USD 20 million in 3 years, representing a fourfold increase compared to the baseline revenue.

According to recent economic impact reports, the cost of building a data monetization capability is less than the baseline revenue from data. Therefore, an organization might allocate a portion of its existing data revenue in the first year to build a data monetization capability.

Getting started with data monetization

Organizations can start by defining their data monetization strategy and identifying the data products. Then, they can create their data monetization capability by developing an integrated AI-driven data platform. IBM Cloud Pak® for Data, IBM Cloud Pak® for Integration, IBM® watsonx.data™ and IBM® watsonx.ai™ provide them with that holistic platform.

We recommend a discovery workshop where you’ll explore your data and AI ambitions to determine your first data product. In a 4 to 6-week sprint, we’ll collaborate to craft a vision for your platform architecture and develop a proof of concept for the first data product design. This comprehensive process includes the development of the initial data product, the creation of a roadmap for future products, and the establishment of a supporting business case.