Data monetization enables organizations to leverage their data assets and AI capabilities to create real economic value. This system of value exchange uses data products to improve business performance, gain a competitive edge, and address industry challenges in response to market demand. Financial benefits include increased revenue through new business models, accessing new markets for additional revenue streams, and optimizing costs through productivity enhancements, infrastructure savings, and reduced operating expenses. In 2023, the global data monetization market was valued at USD 3.5 billion, with projections to reach USD 14.4 billion by 2032, showcasing a compound annual growth rate of 16.6% from 2024 to 2032.
Data is a crucial asset for organizations, and taking a holistic approach to prioritize data-driven business transformation helps maximize value extraction. By harnessing the power of data within the organization, cost optimization can be achieved at an enterprise level, unlocking new revenue opportunities. Focusing on revenue growth potential, cost optimization, data security, and compliance can multiply the benefits of data optimization. Critical aspects of data-driven business transformation include the overall data monetization strategy and the utilization of data products. Data insight and AI automation drive cost optimization by enabling predictive maintenance, process automation, and workforce optimization. AI automation also reduces data security and compliance risks by proactively identifying and analyzing threats before they impact the business.
Industries are facing challenges and opportunities due to the surge in enterprise data volume. Data products, developed from internal data sources or a combination of internal and public data, augmented with AI, help drive business decisions. These data assets are managed as products with defined service contracts, repeatable delivery methods, and clear value propositions. Organizations can create internal data products for various functions or units, referred to as internal data monetization, or external data products for wider consumption across organizations and ecosystems, known as external data monetization.
An AI-driven organization incorporates AI technology in both value creation and value capture within the business model. Data monetization capabilities built on platform economics reach their full potential when data is recognized as a product built or powered by AI. Moving from a collection-led to a product-led model, organizations can create standardized data products for real-time sharing and analytics within the organization, as well as for monetization through ecosystem partnerships. AI-driven data platform services enable data products to be provided as SaaS services, enhancing scalability, security, and delivery.
Developing a comprehensive business case for data monetization over 3 to 5 years can help organizations realize short, mid, and long-term economic benefits. Data monetization can act as a force multiplier, contributing to significant revenue growth over time. Implementing a data monetization capability can lead to a substantial increase in revenue compared to traditional approaches, making it a worthwhile investment for organizations.
To get started with data monetization, organizations can define their strategy, identify data products, and develop an AI-driven data platform. IBM Cloud Pak® for Data, IBM Cloud Pak® for Integration, IBM® watsonx.data™, and IBM® watsonx.ai™ provide a holistic platform for creating and implementing a data monetization strategy. A discovery workshop can help organizations explore their data and AI ambitions, develop a vision for platform architecture, and create a roadmap for future data products. This process includes developing the initial data product, establishing a business case, and setting the foundation for a successful data monetization capability.
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