ABSTRACT: All data management activities—whether internally or externally focused—should either reduce costs or grow earnings.

Read time: 6 mins.

Data monetization is the act of turning data into money. That’s a matter-of-fact definition offered by Barbara Wixom and co-authors in their book Data is Everyone’s Business: The Fundamentals of Data Monetization (MIT Press 2023). 

But the book is not a manual about how to turn data into dollars in a data marketplace, although that’s part of message. Rather Wixom provides a more nuanced view of data monetization. Her assertion is that all data management activities—whether internally or externally focused--should either reduce costs or grow earnings. Companies don’t realize value from their data investments until they accrue one of these two benefits.

This is strong stuff!

The value that most data management activities create is hard to quantify, let alone monetize. How do you quantify the value of streamlined processes? Smoother supply chains? Higher employee satisfaction? Faster root cause analyses? Greater line-of-sight visibility into operations? Most data leaders struggle to prove that the data used to deliver these enhancements accrued to their organization’s top or bottom lines.

The role of slack. According to Wixom, when an organization uses data to improve internal efficiencies, it creates “slack” in the form of excess resources required to run the process. This “slack” needs to be removed to monetize data. This can be done by cutting headcount, reducing budgets, increasing prices, among other things.

She writes, “But if the slack that arises from a data monetization initiative isn’t removed or if the additional value of a product is not extracted from customers, the data monetization initiative will not contribute to the organization’s bottom line. The initiative cannot claim to have monetized data.”

There is no better way to prove data’s value than to monetize data.

Presumably, the whole reason an organization invests in data—spending millions of dollars annually for medium- and large-sized companies—is to drive business value. There is no better way to prove data’s value than to monetize data. Call it what you want, Wixom writes, but “organizations should generate more money from their data assets than they invest in producing and managing them.”

Three Data Monetization Initiatives

The book provides a simple framework for classifying data monetization activities, making it easy for business and technical executives to identify opportunities to realize value from data. There are three approaches to data monetization: improving, wrapping, and selling.

• Improving. This approach is the most common: it’s what all organizations do with data. They capture, clean, transform, model, and deliver data for internal consumption. Monetizing this data activity is challenging, as described above and is not what most people define as data monetization. But Wixom entreats her readers to do so.
• Wrapping. This method “wraps” data or analytics around an existing product or service to enrich the customer experience. Companies can either raise prices to reflect the increased value, sell the data enhancement as a value-added feature, or keep prices the same to gain market share (and thereby grow revenues by attracting more customers).
• Selling. The selling method involves creating a stand-alone data- or analytics-driven product or application that the company sells on its website, through data brokers, or in data marketplaces. Here, the data product acts like any other product or service the company sells.

Capabilities and Connections

Wixom travels the globe interviewing and consulting with prominent data leaders. She knows that it’s easy to identify data monetization opportunities, but harder to actualize them. Consequently, the bulk of the book offers practical advice on how to build technical and organizational capabilities to support data monetization using real-world examples from her research.

• Capabilities. She defines five data management capabilities required to generate value for the data monetization methods described above:
• Data management. The act of producing trustworthy, easily accessible data assets that drive insights.
• Data platform. The infrastructure to support data management activities, including databases, tools, interfaces, cloud servers, and so on.
• Data science. The knowledge, processes, tools, and applications that enable people to extract insights and value from data.
• Customer understanding. The ability to derive insights about customers using the above capabilities.  
• Acceptable data use. Data and system designs that enable compliance with legal requirements and regulations.

For the data cognoscenti, the above data management capabilities are standard fare, described in hundreds of books and articles. But Wixom offers a unique twist. She says the purpose of these capabilities is to “decontextualize data, divorcing it from a specific condition or context and turning it into reusable data assets.”

As any data leader knows, creating reusable data assets is a herculean, and often thankless, task that is too easy to shirk.

As any data leader knows, creating reusable data assets is a herculean, and often thankless, task that is too easy to shirk. It’s hard to keep up with business demands and technology innovations while delivering value at a reasonable cost. Some are tempted to take shortcuts and others hope that AI will shorten the data-to-insight lifecycle, enabling data leaders to stay true to their mission.

Connections. Anyone who has run a data organization knows that designing and implementing technology is the easy part; the hard part is organizing and managing the people and processes. Most data leaders are not skilled in the “soft stuff” so it’s heartwarming to see Wixom emphasize this point.

According to Wixom, connections represent how an organization aligns data experts with business people to optimize the delivery of insights. She offers a variety of approaches to create “purple people” or “purple teams” that blend the business and data skills needed to bring data to life:

• Embedding. Embedding data experts in business functions to work closely with users to solve problems.
• Multidisciplinary teams. Assign a data expert to a cross-functional team created to carry out an initiative.
• Advisory services. Centers of excellence where experts from one or more areas transfer knowledge to experts in other domains.
• Shared services. Where a pool of data experts provide services to various business functions.
• Social networks. These are like communities of practice where data and business experts share tips and tricks on various topics.

Sequencing

Far-sighted data leaders will use the book’s framework to develop a data monetization strategy and roadmap. The logical sequence is to start with “improving” to build a strong data foundation, then move to “wrapping” to dabble with data as a product, and then finish with “selling” to monetize data products directly. (See the figure below.)

Not so fast. Yet, Wixom writes that most companies don’t follow this sequence. “The reality is that most [data monetization] practices are adopted by initiative teams, so most data monetization capabilities are developed in the context of initiatives. When enterprise capabilities do not exist, the initiative owner must unearth the capabilities need to meet her objectives.”

The result of independent data monetization initiatives are data silos.

The result of independent data monetization initiatives are data silos, which are the bane of every enterprise data leader who wants to cohesive, holistic approach to data. Therefore, Wixom implies that it’s imperative for data leaders to get ahead of data monetization initiatives, if possible, to avoid long-term pain.

Different folks. Part of the challenge in coordinating data monetization initiatives is that each approach requires different people, teams, and capabilities. For instance, improving requires “process owners” and enterprise data teams; wrapping requires “data product owners” and product development teams; and selling requires “entrepreneurial leaders” and a cross-functional team of sales, legal, marketing, development, and operations folks to build, enrich, market, and sell the data products.

Different strokes. On the capability side, data monetization approaches use different platforms and tools. For example, improving focuses largely ERP and CRM data and requires “a searchable data catalog” to help employees find data assets; wrapping focuses on voluminous product usage data which is often stored and accessed in specialized repositories; and selling requires ample metadata to describe data products, a marketplace to market and sell data products, and APIs to distribute data assets.

Data leaders need to understand the unique characteristics of each data monetization approach and work hard to leverage and blend capabilities, people, and processes that maximizes return on data investments.

Conclusion

The book “Data Is Everyone’s Business” not only provides a practical framework for understanding data monetization and developing a data strategy, it’s also a good primer on data management practices that will deliver value whether an organization succeeds in “turning data into money.” You can purchase the book here.