What is data monetization?

Why monetize data?

There are several reasons why organizations monetize data, including the following:

• Optimize the value that can be extracted from it. Institutional data sets, especially accumulated over time, might contain significant value that has never been discovered and acted upon. For example, this might include passive long-term trends, customer profile features that reflect purchasing behaviors, even subtle indicators of competitor strategies. Discoveries such as these that can be unearthed using data analytics can justify an overt data monetization effort.
• Stimulate innovation. Deep analysis of transactional, historical and analytical data can yield actionable business intelligence (BI). This informs C-level decision-making and has the potential to improve the operational efficiency of the enterprise, customer relationship management and even product development -- all of which can profoundly impact the bottom line.

Benefits of data monetization

Data monetization offers organizations numerous benefits, including the following:

• Improvements in operations. The motivations listed above all serve to make the organization better, to bolster performance both internally and externally.
• Competitive advantage. Improving operations, enhancing the customer experience and increasing product and service offerings gives the enterprise a significant edge in the marketplace.
• Enhanced partnerships. When data is monetized, it might be possible to offer data sets for sale to partner companies or other interested, non-competitive parties for use in their own data analytics and BI, leading to additional revenue streams and even possible strategic partnerships.

Challenges of data monetization

Along with its potential, data monetization introduces challenges businesses can't ignore. These challenges fall into the following categories:

• Technological challenges. To analyze data to find useful patterns for data analytics and BI, the organization's storage and configuration might need to be modified extensively, and the business might need to acquire new software and in-house skill sets. The costs could be considerable. In addition, the scalability of the resulting new resources could be a concern, becoming a critical -- and possibly difficult -- design challenge.
• Institutional challenges. Moving to data monetization, for all its benefits, could alter the organization's business model, as it might cause major changes to how business is done. Becoming a data-driven organization often requires extensive in-house cultural change, which is sometimes met with resistance and often takes considerable time. Moreover, the handling of data takes on new legal and ethical nuances, with the need to comply with legal and regulatory constraints. There's also data privacy to consider, as more data moves across a broader digital terrain.
• Strategic challenges. The discoveries yielded by data monetization efforts can greatly impact high-level decision-making, but they tend to inform what should change in the organization's processes, performance and strategy, not how they should change. Often, data analytics might suggest a range of possibilities for operational improvements, new products and strategic partnerships, but choosing from among those possibilities can prove daunting.

How to monetize data

Experts describe data monetization strategies as being either direct or indirect. Former Gartner vice president and distinguished analyst Doug Laney coined the term infonomics, a portmanteau of economics and information. Laney, who also wrote the book Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage, described direct data monetization methods as data that's sold or traded. Early examples include Walmart's Retail Link System, a data portal for sellers, and Alibaba's targeted personal finance services. Indirect methods, on the other hand, use data to improve business processes, such as identifying waste or improving safety, which companies should strive to measure to show a monetary benefit.

Barbara H. Wixom, principal research scientist at the MIT Sloan Center for Information Systems Research, also differentiates between direct and indirect methods for monetizing data.

In her 2019 research briefing "Building data monetization capabilities that pay off," Wixom listed three data monetization strategies: selling data, wrapping analytics around products and services, or improving business processes.

Selling data outright, something retailers have been doing for years with point-of-sale or customer loyalty data, introduces new revenue streams to a company. When wrapping analytics around an offering, companies are introducing new products based on data such as dashboards that provide metrics on customer sentiment or product use. Both are described as direct data monetization methods.

Wixom also highlighted improving business processes as an indirect strategy, which she described in her 2017 article "How to Monetize Your Data" as a perhaps unglamorous but immediate path to data monetization. She pointed to Microsoft's sales team and its push over the last decade to make processes more efficient and decisions more data-driven as an example of an indirect data monetization success.

In their 2023 book Data is Everybody's Business: The Fundamentals of Data Monetization, Wixom and coauthors Cynthia M. Beath and Leslie Owens address how data assets can benefit organizations by enhancing customer experience and worker productivity, cutting costs, driving change, innovating with new products and services, and creating shareholder value.

To summarize, organizations can benefit from data monetization by taking the following steps:

1. Improving business processes. Deep analysis of past performance data inspires insights into process improvements that can lead to shorter lead time, more efficient order fulfillment, more efficient services -- all of which impact the bottom line.
2. Leveraging analytics with products and services. Data analytics yield BI, which is actionable for product and service improvement -- adding delivery notifications, service expansion notifications and customer-targeted new product offerings.
3. Selling data. Data sets can become a product, resulting in new revenue streams and new value that can be offered in the context of strategic partnerships, to improve collaborative operations and initiatives.

Data monetization use cases and examples

Data monetization has been embraced across a wide range of industries, from retail sales to manufacturing to agriculture. The following are examples of organizations that have maximized value from their data:

• Starbucks. Data collected via Starbucks' loyalty card program and app was used to analyze the behaviors of regular customers and predict buying behaviors, resulting in individualized offers, increased sales and successful customer re-engagement.
• IBM and Salesforce. Both tech giants have used in-house data sharing to bolster employee retention and talent management.
• Dollar General. By analyzing supply chain data, Dollar General improved its sorting processes and packaging operations to optimize stocking and on-shelf product availability.
• Apple. Apple gathered vast amounts of user behavior data from its proprietary apps and used it to systematically improve customer experience and satisfaction, as well as app ease of use.
• AB InBev. The global brewing company greatly improved its operations by consolidating its disparate enterprise resource planning systems into a single cloud-based data hub, which enabled more accurate forecasting.

The future of data monetization

Data monetization is shaping the future of business by providing organizations with additional revenue opportunities. According to Mordor Intelligence, the largest market for data monetization is the U.S., but Asia-Pacific is the fastest-growing market. From 2024 to 2029, the market is predicted to grow almost 20% from $4.17 billion to $10.35 billion.

Advances in AI and machine learning are making data analytics more accessible to a larger audience. In turn, more organizations will be able to use advanced AI technologies to improve operational efficiency, opening doors to more data monetization opportunities.

Learn how organizations are using AI in business applications and operations to address industry-specific needs.