High-Performance Data Monetization

MIT CISR research found that top-performing organizations attributed 11 percent of revenues to data monetization, more than five times the 2 percent reported by bottom-performing organizations.[foot]MIT CISR 2024 Data Monetization Survey (N=349); survey respondents were senior leaders with an understanding of their organization’s data investments and outcomes. Organizations split into distinct top (N=76), middle (N=170), and bottom (N=75) performers based on a composite score of the organization’s performance that combined measures of profitability, revenue growth, time to market, innovativeness, agility, customer experience, employee experience, and talent attractiveness. Incomplete responses on performance measures were excluded from the segment analysis, which produced the difference between total N and the combined N of the segments. ANOVA analysis comparing top and bottom performers found that the means of data monetization impact composite scores were significantly higher for top performers, p<.001.[/foot] Why do top performers realize so much more revenue from data monetization? Top performers are ahead for two key reasons. For one, their acumen in data monetization capabilities such as data management and regulatory, legal, and ethical oversight as part of acceptable data use is almost two times better.[foot]MIT CISR 2024 Data Monetization Survey (N=349). ANOVA analysis comparing top (N=76) and bottom (N=75) performers found that means of a composite score of five data monetization capabilities and AI explanation capability were significantly higher for top performers, p<.001.[/foot] But additionally, top performers invest in three factors that amplify the financial impact of data monetization: CEO-level data leadership, data value management, and data lifecycle measurement (see table 1). This briefing defines these high-performance factors and then illustrates them using a case study of Wolters Kluwer, a €5.6 billion technology and services company based in the Netherlands that today attributes 58 percent of revenues to expert solutions.

High-Performance Factors

CEO-level data leadership. When it comes to data, organizational leaders tend to overly rely on their enterprise data strategy and data office to drive success. That is not enough. Succeeding in data monetization also requires clear and compelling internal and external messaging by the organization’s top executives about data goals and outcomes. It also requires formal reporting of those goals and outcomes, especially externally to financial markets. Organizations with effective CEO-level data leadership have executives who direct organizational attention and resources to strategic data initiatives and data outcomes.

Data value realization. The value of a data monetization product[foot]A data product creates value by providing data, insight, or action to employees, customers, systems, or other data consumers. Data products are developed through initiatives or purchased. Increasingly, data product owners manage and sustain data products.[/foot] is realized when associated financial value appears in the organization’s bottom line in the form of reduced expenses, higher sales, or a new revenue line item. While many organizations are good at generating desirable non-financial benefits from data, like more efficient processes, happier employees, greener work tasks, and delighted customers, far too few are good at value realization: converting those benefits to an income statement line item.[foot]Or a budget line item, in the case of non-commercial organizations.[/foot] Data value realization entails the hard work of reducing salary expenses when work gets faster and raising prices or opening up new markets when products are enhanced with analytical bells and whistles. Data value realization requires knowing costs, risks, market size, and customer willingness to pay in order to price new solutions so they are profitable. Organizations with effective data value realization know whether their data initiatives are paying off, and by how much.

Data resource lifecycle measurement. Measuring the bottom-line impact of data is great, but that is not enough to tell you that future bottom-line impacts will be sustained. To know that, organizations need to set up data resource lifecycle measurement. This means tracking data across its lifecycle from the development of data capabilities and data assets, through their use and reuse in data products, through the creation of financial and non-financial benefits, and to the recording of financial returns on the organization’s income statement, P&L statement, or some equivalent financial instrument. Data resource lifecycle measurement creates transparency into how well the organization converts data assets and data capabilities into data products that make money for the organization. Organizations with effective data resource lifecycle measurement know whether their investments in data resources are paying off.

Wolters Kluwer

Wolters Kluwer provides information, software, and services for accountants, doctors, lawyers, and other professionals in 180 countries from offices in 50 countries.[foot]This case study on Wolters Kluwer draws from B. H. Wixom, C. M. Beath, J. Duane, and N. van der Meulen, “Wolters Kluwer’s Expert Solutions Journey,” MIT CISR Working Paper No. 465, November 2024, https://cisr.mit.edu/publication/MIT_CISRwp465_WoltersKluwerExpertSolutions_WixomBeathDuaneVanderMeulen.[/foot] In particular, Wolters Kluwer serves professionals with expert solutions, offerings that combine “deep domain knowledge with technology to deliver both content and workflow automation to drive improved outcomes and productivity for our customers.”[foot]Wolters Kluwer NV, 2023 Annual Report: When You Have To Be Right, n.d., 9, https://assets.contenthub.wolterskluwer.com/api/public/content/2190891-wolters-kluwer-2023-annual-report-3ff32c52c4?v=f6d1aeee.[/foot] An example of an expert solution is the company’s LegalVIEW® BillAnalyzer, a tool that reviews a legal services invoice and determines whether its charges are likely to be accurate or should be disputed.

Expert solutions are just one of three forms of data monetization at Wolters Kluwer. The company’s leaders view data monetization broadly, to include using data to generate both top-line and bottom-line returns. Besides selling expert solutions, Wolters Kluwer uses data to improve work, such as by streamlining and automating the delivery of transactional services and content, and to enhance products, such as by offering distinctive analytical features and experiences.

CEO-Level Data Leadership at Wolters Kluwer

In 2008, Wolters Kluwer CEO Nancy McKinstry began investing eight to ten percent of group revenues in new and enhanced product development to stimulate organic growth, including in new expert solutions. McKinstry concurrently encouraged her leadership team to pursue consolidation and standardization opportunities, which prompted significant rationalization and reengineering activity across the company. Divisions increasingly pursued new business opportunities using data and analytics, thanks partly to the availability of data and analytics technology, services, and advisory that shared services groups at the corporate level were providing.

McKinstry and her leadership team communicated the company’s data goals and accomplishments clearly in various ways. For one, they spoke widely and regularly about the importance of expert solutions to Wolters Kluwer’s corporate strategy and articulated expert solutions as a key strategic pillar in the company’s annual report.

Employees heard McKinstry’s messaging firsthand in town hall forums, and they learned about data and analytics innovations via corporate programs such as an annual hackathon and corporate awards for innovation and change. Division leaders nominated promising data and analytics initiatives for Wolters Kluwer awards, including their best expert solutions; LegalVIEW® BillAnalyzer won an award in 2017, the year it was launched.

Data Value Realization at Wolters Kluwer

Wolters Kluwer leaders consider expert solutions key to the company’s revenue growth strategy. They also view data as an enabler of business consolidation and standardization goals. They have proactively worked to recognize on the company’s income statement the value they realize from these forms of data monetization.

Expert solutions like LegalVIEW® BillAnalyzer have an owner, their own P&L, and dedicated sales teams. In some cases, divisional leaders assign new, promising expert solutions a shadow P&L, giving the solutions’ owners responsibility for an assigned amount of the division’s revenue lift or expense reduction goals.

And as new products move through the product innovation process, product teams rely on sales pipeline estimates that assess the potential market for new products or features. Finance leaders with data expertise insist that new products promise to generate a financial return for Wolters Kluwer in order to be released.

One division established a New Product Innovation (NPI) process to ensure that new innovations were broadly applicable, technically feasible, and likely to be lucrative. The NPI process favors ideas that will create top- or bottom-line value. It also encourages filing for patents to protect intellectual property in order to sustain the future market value of innovations.

Data Lifecycle Measurement at Wolters Kluwer

Wolters Kluwer measures its data assets from creation to value realization. Specialized teams and roles at both corporate and divisional levels provide measurement expertise across the data resource lifecycle.

Corporate data and analytics groups manage investments in data capabilities that are required to convert data into assets. These groups stay on top of the company’s cost-to-serve data and analytics. They identify shared services opportunities and offer divisions standard resources such as cloud platforms, analytical tools, and AI policies and frameworks.

Divisional units measure data products regarding, for example, their expected benefits, usage, and created customer value. In the Financial & Corporate Compliance (FCC) division, a Voice of the Customer (VOC) group regularly conducts surveys and market research to capture customer insights and measure value creation from its products. It asks questions like “What if we did this for you?” to align products with customer needs. A Customer Insights group further supports data lifecycle and value creation measurements by analyzing customer segmentation and behavior to guide strategic product decisions. FCC’s Customer Analytics team interacts with customers to assess feature success and recommend changes to products. Over time, as Wolters Kluwer has moved software products to the cloud, it has become straightforward for the company to instrument products and get detailed feedback on their usage.

Wolters Kluwer takes the realization of financial value very seriously. Product teams across Wolters Kluwer track the profitability of their solutions and manage solution pricing. For instance, the LegalVIEW® BillAnalyzer team implemented a shared-savings model, tracking customer cost savings from the tool’s recommendations. In FCC, product teams receive help from centers of excellence for sales operations and pricing, both of which have been important for driving measured revenue increases.

In sum, McKinstry and her leadership team set clear business goals for the divisions, which work hard to measure and report both value creation and realized financial value, thereby stimulating purposeful investment. By 2023, Wolters Kluwer generated 94 percent of its revenues from digital products and services, up from 10 percent reported in 2003.

Conclusion

Data monetization is increasingly important to the financial health of all organizations. Leaders need to move beyond simply establishing high-quality data assets and begin fostering a culture of high-performance data monetization to maximize financial returns from data. Leaders can shape culture by establishing CEO-level data leadership, data value realization, and data lifecycle measurement, all of which keep the organization’s data monetization engine humming.

To strengthen the impact of your CEO’s data leadership, build the data savvy of the leadership team and feed your CEO data points that can be shared with investors. To get started on data value realization, appoint a team (including some financial experts) to assess the bottom-line impact of any three recent initiatives that you believe created valuable benefits. To choose your next new data lifecycle KPI, appoint a team (including some measurement experts) to assess the quality of your current data lifecycle measures, from asset quality, through reuse, to value realization. Be sure to share the results with your top management team.

© 2024 MIT Center for Information Systems Research, Wixom, Beath, and Duane. MIT CISR Research Briefings are published monthly to update the center’s member organizations on current research projects.