Data monetization is the process of creating and extracting value from data and artificial intelligence (AI) assets. It involves building a system that enables the exchange of data products to drive business growth and gain a competitive advantage. This growth can be achieved through various means such as cost effectiveness, risk compliance, economic value increase in partner ecosystems, or generating new revenue streams. By utilizing advanced data management software and generative AI, organizations can accelerate the development of a platform capable of delivering scalable enterprise-ready data and AI products.
The importance of data monetization lies in its ability to improve work processes and enhance business performance by leveraging data. Internally, data monetization initiatives can lead to improvements in process design, task guidance, and optimization of data used in the organization’s products or services. Externally, data monetization opens up opportunities to sell data or record its value when used in different formats and for various purposes. The key to creating value from data is taking action on the data, and realizing that value involves ensuring there is an economic benefit that contributes to the organization’s bottom line.
A data monetization strategy involves managing data as a product. Every organization has the potential to monetize its data, but many have yet to tap into this resource for new capabilities. Data-as-a-Service and data marketplaces have already established ways to create value from data analytics, big data, and business intelligence initiatives. However, few organizations have adopted the strategic approach of treating data as a product. This approach involves applying product development practices to data and has been proven to contribute over 20% to a company’s profitability. By treating data as a strategic asset and using a user-centric product approach, organizations can build trust in their data and AI by demonstrating transparency, adhering to regulations, and prioritizing data privacy and security.
Data products are assembled from various sources and can serve a range of functional needs. Each data product has its own lifecycle environment where its data and AI assets are managed. Flexibility in data collection is achieved by connecting product lakehouses to multiple sources using different technology protocols. By managing data products as isolated units, they can be located in private or public clouds based on sensitivity and privacy controls. IBM watsonx.data provides connectivity flexibility and hosting of data product lakehouses built on Red Hat OpenShift for an open hybrid cloud deployment.
Data mesh architectures have emerged as a cost-effective way to serve data products to different endpoint types. They enable detailed usage tracking, risk and compliance measurements, and security. Multiple data products are served over the mesh and consumed by end-user applications as auditable transactions. For example, a financial markets business might offer a real-time market data feed and finance-related news as separate data products, which can be consumed together in a decision-making application.
Developing a data management capability requires connecting the dots for stakeholders, creating a supply chain from raw data sources to the exchange of value when a data asset is consumed. This can be achieved by developing a solution framework for data monetization that includes three stages: create, serve, and realize. The raw data can come from enterprise systems, external sources, and personal data, which need to be managed correctly. Data products can take the form of datasets, programs, or AI models and are packaged and deployed for consumption as services. Depending on the user’s business model, they can consume the service for their own use or create downstream products or customer experiences using the data product.
To achieve scale, a platform approach is recommended. Traditional distribution methods of large datasets or APIs into multiple marketplaces can become complex and less cost-effective. By adopting a data monetization solution framework, organizations can maximize value by becoming data Software-as-a-Service (SaaS) businesses. This framework integrates technologies and products, including IBM Data and AI products, to create a reference architecture that covers the entire lifecycle of data monetization.
Artificial intelligence plays a crucial role in enterprise data management. Many organizations have already built mature software systems with machine learning and deep learning capabilities to power their business processes and customer offerings. Generative AI has further accelerated the design, delivery, and management of data products. AI models can be used to build tools for platform builders and operators, who can then use those tools to discover and learn about data. Code generation tools can also automate processes and create natural language-driven experiences. These advancements, combined with established practices such as AIOps and advanced analytics, contribute to the overall success of data monetization efforts.
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