3 principles to bring your data monetization initiatives to life

In 2024, a team of researchers from the MIT Center for Information System Research developed a model that links effective data monetization strategies to strong financial performance. Now, in a new research brief, they explain why artificial intelligence has made robust data monetization capabilities not just a competitive advantage but a precondition for success.

The model was most recently validated by a CISR global survey of 349 executives in organizations across a range of industries, sizes, and geographic regions that revealed that top-performing firms attributed 11% of their revenues to data monetization. That percentage was more than five times what was reported by the bottom performers, at 2% of total revenue.

Research scientist Nick van der Meulen, senior lecturer Barbara Wixom, and Cynthia Beath, an academic research fellow at MIT, identified six competencies that determine whether data monetization efforts will drive financial performance:

• Data management
• Data platform
• Data science
• Customer understanding
• Acceptable data use practices
• AI explanation

The first five competencies represent data monetization capabilities; AI explanation is an organizational capability that centers on building trust in AI models.

Top-performing firms use all six to develop reusable and recombinable data assets. That, in turn, spurs the development of a data democracy, where a wide swath of employees have the access, skills, motivation, and guidance to build and capitalize on data assets for profitable business outcomes.

“With good capabilities and a data democracy, organizations can activate effective data monetization initiatives that improve internal business processes, wrap core products with features and experiences, and sell information offerings to new and existing markets,” the researchers write.

A formula for data monetization success

A decade of collaboration between MIT CISR researchers and companies serving on its Data Research Advisory Board produced the research model that explains how organizations translate data assets into financial performance.

Most recently, CISR researchers have identified three principles that enable those organizations to put the model into action: Manage data assets with a product mindset, treat data monetization as a team sport, and realize the value you create.

Together, the capabilities and principles can help other organizations elevate their data monetization competencies and better execute on strategy.

1. Manage data assets with a product mindset

Data assets require upkeep to remain relevant and deliver value. That means they need to be managed like any other revenue-generating product or service, with ownership, ongoing maintenance, and continuous improvement across their full life cycles. A product approach ensures that data assets are reused instead of rebuilt, with returns compounding with each additional use.

One company embracing a data product mindset showed what’s possible with such an approach. Its product portfolio of over 120 data assets is feeding more than 600 AI initiatives, and the firm has garnered multiple awards for its AI strategy.

Takeaway: The data work comes first; the AI results follow.

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2. Treat data monetization as a team sport

A data team can’t own sole responsibility for data monetization success. It’s important to cast a wide net for individuals who have deep knowledge of the organization’s processes, systems, and offerings and who possess the expertise, motivation, access, and guidance to use data assets strategically. Another important factor in the mix: cultivating a shared understanding of the strategy, using a common language that cuts across all employee levels and functions.

Shared understanding emerges more quickly when a data monetization initiative is couched in business language rather than technical terms. Phrases like “infrastructure cost” and “platform capabilities” can be off-putting to senior executives. Recasting a data monetization strategy as a matter of data liquidity — whether data assets can be reused and combined easily — rather than a data cleanup exercise, for example, may make it resonate more clearly with business leaders.

Takeaway: Recruit and cultivate employees who are bilingual — fluent in the languages of both business and data.

3. Realize the value that’s created

Creating value and realizing value are different concepts. A successful data initiative might produce benefits such as saving time, improving a process, or delivering better service to customers. Yet that benefit doesn’t always translate into money. Data monetization happens when there’s a direct action to extract the financial gain — such as cutting a budget that has too much slack or charging a customer for a specific data-driven feature that enhances the value of a product.

Takeaway: Organizations frequently forget to go after the money, assuming that it will show up on its own. Be intentional about realizing value, and measure results to keep efforts ongoing.

The ways in which data monetization efforts can fail to create or realize value have grown with the proliferation of AI, and leaders must recognize that sexy pilot projects and AI theatre don’t necessarily translate into financial results. “A core activity that separates a real return from applause is the practice of measurement: routinely tracing created value to a specific line item on the income statement,” the researchers write.

Nick van der Meulen is a research scientist at the MIT Center for Information Systems Research. He conducts academic research that targets the challenges of senior-level executives, with a specific interest in how companies need to organize themselves differently in the face of continuous technological change. He is one of the faculty members who teaches the MIT Sloan Executive Education course Global Executive Academy.

Barbara Wixom is a senior lecturer at the MIT Sloan School of Management and former principal research scientist at MIT CISR. Since 1994, her research has explored how organizations generate business value from data assets. She teaches the MIT Sloan Executive Education course Data Monetization Strategy: Creating Value Through Data.

Cynthia Beath is an academic research fellow at MIT CISR and professor emerita at the University of Texas at Austin. Her research interests include organization redesign for the digital era, the management of data assets, and the organizational impacts of AI.