Realize financial benefits through data monetization

Data monetization allows organizations to use their data assets and artificial intelligence (AI) capabilities to create real economic value. This value exchange system uses data products to improve business performance, gain competitive advantage, and address industry challenges by responding to market demands.

Financial benefits include increasing revenue by creating business models for adjacent industries, accessing new markets for more revenue streams, and increasing existing revenues. Cost optimization can be achieved through increased productivity, infrastructure savings, and reduced operating costs.

The global data monetization market size was $3.5 billion in 2023, and experts expect it to reach $14.4 billion by 2032, recording a CAGR of 16.6% from 2024 to 2032.

Treat data as a strategic asset

Data is one of the most valuable intangible assets for an organization. Therefore, adopting a holistic approach that prioritizes data-driven business transformation will help you optimize value extraction. These innovations leverage the power of data within your organization to support enterprise-wide cost optimization and open new direct revenue opportunities.

When it comes to data optimization, most organizations focus solely on reducing infrastructure costs. However, companies that embrace a data-driven business transformation strategy can multiply the benefits by considering revenue growth potential, optimizing costs across infrastructure, development, and maintenance, and strengthening data security and compliance.

An important aspect of data-driven business transformation is the overall data monetization strategy and how data products are used. Data insights and AI automation drive cost optimization through predictive maintenance, process automation, and workforce optimization. AI automation significantly reduces data security and compliance risks by proactively identifying and analyzing the severity, scope, and root cause of threats before they impact your business.

The end result of data-driven business transformation is improved compliance, productivity, and efficiency through automation across business units such as sales, marketing, and service. This leads to increased revenue through the opportunity to create new services and channels.

Data product identification

Across industries, we are experiencing a surge in enterprise data volumes, which presents both challenges and opportunities. These challenges, along with specific industry requirements and use cases, will impact the types of data products your organization or market requires.

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

For example, the banking industry faces the following challenges:

• Competition between agile and innovative financial technologies and challenger banks.
• High level of regulatory control.
• Sensitive information must be protected.
• Organizational data silos hinder a unified customer experience.
• Pressure to increase margins and find new revenue streams.

To address these challenges, organizations create relevant use cases that address specific needs as well as market-wide needs. The following sample use cases demonstrate relevant data products and their financial benefits.

Data products can be created for internal use across different functions or business units. When organizations continuously share data internally to improve efficiency and achieve qualitative or quantitative benefits, it is called internal data monetization.

Data products can also be created for broader external consumption across multiple organizations and ecosystems. Sharing data externally to achieve strategic or financial benefit is called external data monetization.

AI-based data platform economics

An AI-driven organization is one where AI technologies are fundamental to both value creation and value capture within their business model. Data monetization capabilities built on platform economics can reach their greatest potential when data is seen as a product built or powered by AI.

In an ingestion-driven model, data from external and internal sources, such as data warehouses and data repositories, is fed into analytics tools for enterprise-wide consumption. At the enterprise level, business units identify the data they need from source systems and create data sets tailored only to their specific solution. This can lead to a proliferation of organizational data and added pipeline complexity, making it difficult to maintain and use new solutions, directly impacting costs and timeliness.

As companies move from a collection-centric model to a product-centric model, data products are created using external and internal data sources along with analytics tools. Once developed, these data products can be used by business units within an organization for real-time data sharing and analysis. These data products also offer monetization opportunities through ecosystem partnerships.

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

The data platform provides data-rich data products using machine learning, deep learning, and generative AI. These AI-based data products can virtualize and integrate disparate data sources to create domain-specific AI models using proprietary enterprise data. Data platform services enable you to deliver your data products as a SaaS service, a single data mesh distributed across your hybrid cloud, and certified, secure, and audited data product offerings.

As organizations connect their valuable data and AI assets to a broader group of users, they can leverage the multiplier effect of the consumption and advancement of data products, as well as market reach through scalable cloud deployments.

Economic Impact of Data Monetization

Organizations typically develop business cases spanning three to five years to gain a comprehensive view of short-, medium-, and long-term economic benefits. Successful practices address market needs to remain competitive, drive scalability, and continuously pursue cost optimization and revenue enhancement opportunities.

The graph above shows the revenue growth potential from data monetization over a five-year period. In our example organization with $2 billion in revenue, the underlying revenue from data is $5 million (0.25% of total revenue). If an organization follows a traditional approach, data revenue could grow from $5 million to $6.7 million in three years, a 10% year-over-year increase. That’s just 1.34 times baseline earnings.

In contrast, data monetization can act as a force multiplier and contribute to a 1% or more increase in company revenue. Data monetization capabilities can potentially increase data revenue from $5 million to $20 million in three years, a four-fold increase over baseline revenue.

According to a recent economic impact report, the cost of building a data monetization function is less than the baseline revenue from data. Therefore, organizations can allocate a portion of their existing data revenue in the first year to build a data monetization capability.

Get started monetizing your data

Organizations can start by defining their data monetization strategy and identifying their data products. You can then build data monetization capabilities by developing an integrated AI-based data platform. IBM Cloud Pak® for Data, IBM Cloud Pak® for Integration, IBM® watsonx.data™ and IBM® watsonx.ai™ provide a holistic platform.

We recommend a discovery workshop to explore your data and AI ambitions to determine your first data product. Over a 4-6 week sprint, we will work together to create a vision for the platform architecture and develop a proof of concept for our first data product design. This comprehensive process includes developing the initial data product, creating a roadmap for future products, and establishing a supporting business case.

Explore AI-based data platform architecture