3 ways to build a culture of data monetization – MIT Sloan News
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Ideas Made to Matter
Data
By
Kristin Burnham
CEO-level data leadership, data value management, and data life-cycle measurement can boost the financial impact of data monetization.
Data monetization — the use of data assets to create new revenue streams, reduce costs, or enhance products and offerings — is a growing priority for organizations looking to gain a strategic advantage.
In an MIT Center for Information Systems Research survey, top-performing organizations (in terms of profitability, revenue growth, customer experience, and other factors) attributed 11% of their revenues to data monetization. That’s more than five times the 2% reported by bottom-performing organizations.
Top performers realize more revenue from data monetization for two key reasons, according to a new research briefing from MIT CISR researcher Cynthia M. Beath, and Ja-Naé Duane. First, high-performing organizations are almost two times better at building data monetization capabilities, such as data management and the oversight of acceptable data use.
The second reason is that these organizations build a culture conducive to data monetization. Top-performing companies invest in three factors that can boost the financial impact of data monetization:
CEO-level data leadership is the organizational ability to consistently communicate the CEO’s vision for data, which motivates investment in data resources and data products. Among low performers, organizational leaders tend to rely too much on their enterprise data strategy and data office to drive success. To succeed in data monetization, organizations need clear and compelling messaging — directed both internally and externally — from top executives about goals and outcomes. Formal reporting of those goals and outcomes is also important, particularly in external reporting to financial markets.
Data value realization is the organizational ability to move benefits created from data products to the organization’s bottom line, resulting in realized financial value.
Data products create value by providing data, insights, or suggested actions to employees, customers, systems, or other data consumers. The associated financial value appears in the form of reduced expenses, higher sales, or a new revenue line item. Though many organizations are adept at generating nonfinancial benefits from data, such as process efficiencies, happier employees and customers, and improved sustainability, fewer have success converting those intangible benefits into tangible financial impacts on the bottom line, the researchers write.
Data value realization requires hard work and a deep understanding of costs, risks, market size, and customer willingness to pay. Organizations can achieve data value realization by removing or redirecting the slack created by efficiencies, raising prices or opening new markets when the customer value proposition increases, and pricing information solutions so they stay profitable.
Data resource life-cycle measurement is the organizational ability to track and manage data assets and other data resources across their life cycles, from the development of data capabilities and data assets through the recording of financial returns.
This process helps organizations understand whether future bottom-line impacts are sustainable and creates transparency regarding an organization’s ability to convert data assets and data capabilities into products that make money. Businesses can measure data resource life cycles by tracking data asset quality, use, and reuse; tracking the financial impact of data assets on the organization’s key financial instruments; and tracking the buildout of enterprise data capabilities. Organizations that do this successfully know whether their investments in data are paying off, the researchers write.
Read the research briefing: High-Performance Data Monetization
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