Data monetization, the process of generating financial returns from data assets, is becoming a strategic imperative for organizations across industries. To effectively monetize data, companies need to develop capabilities that produce data assets that are accurate, available, combinable, relevant, and secure. This blog post explores the five key capabilities necessary for successful data monetization and provides insights on how to build and enhance these capabilities within your organization.
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1. Data Management

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Definition:
Data management involves the creation and maintenance of master data, integrated data, and curated data.

• Master Data: Consistent and accurate data that is shared across the organization.
• Integrated Data: Data that is harmonized from different sources to provide a unified view.
• Curated Data: Data that is refined and enhanced for specific business purposes.
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Key Questions:

• Are your data management practices ensuring the accuracy and consistency of data?
• Do you have systems in place for integrating data from diverse sources?

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2. Data Platform
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Definition:
A robust data platform includes advanced technology that provides internal and external access to data.

• Advanced Technology: Utilization of cutting-edge tools and infrastructure to handle large volumes of data.
• Internal Access: Ensuring that employees can easily access the data they need.
• External Access: Providing secure access to data for partners and stakeholders.
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‍Key Questions:

• Is your data platform scalable and capable of handling the organization’s data needs?
• Do you facilitate secure access to data for both internal and external users?

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3. Acceptable Data Use
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Definition:
Acceptable data use ensures data is used ethically and in compliance with regulations.

• Internal Oversight: Governance policies to monitor and control data usage within the organization.
• External Oversight: Compliance with legal and regulatory standards.
• Automation: Implementing automated systems to ensure consistent enforcement of data policies.

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‍Key Questions:

• Are your data governance policies comprehensive and up-to-date?
• How do you ensure compliance with external regulations?

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4. Data Science
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Definition:
Data science involves using reporting, statistics, and machine learning to extract insights from data.

• Reporting: Creating reports that provide valuable insights for decision-making.
• Statistics: Using statistical methods to analyze and interpret data.
• Machine Learning: Implementing machine learning models to predict trends and outcomes.

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‍Key Questions:

• Are your data science capabilities sufficient to generate actionable insights?
• Do you have the necessary tools and expertise to implement advanced analytics?

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5. Customer Understanding
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Definition:
Customer understanding involves making sense of customer data, co-creating solutions with customers, and experimenting to find what works best.

• Sense Making: Analyzing customer data to understand behavior and preferences.
• Co-Creation: Collaborating with customers to develop products and services.
• Experimentation: Testing and iterating to refine offerings based on customer feedback

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‍Key Questions:

• How well do you understand your customers’ needs and behaviors?
• Are you engaging customers in the co-creation process?

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Assessing and Enhancing Your Capabilities
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To effectively assess your data monetization capabilities, consider using a capability assessment worksheet. This tool can help identify strengths and weaknesses in your current practices and highlight areas for improvement. Organizations that excel in data monetization typically exhibit stronger capabilities across these five areas and achieve significantly better outcomes. Research indicates that top-performing organizations in data monetization have 1.5 times stronger capabilities and achieve 2.5 times better outcomes compared to their low-performing peers.

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Questions to Ask Yourself:
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• Value Creation: What kind of value do you frequently create with data? Do you know if it made it to the bottom line?
• Opportunities: Can you identify an opportunity for each of the three methods (improving, wrapping, selling) in your organization?
• Ethics: Is the term data monetization acceptable in your organization, or does it have some unethical associations?
• Improvement: What is your weakest capability? What practices do you need to adopt to strengthen it?
• Enterprise Impact: Which capability is most enterprise-wide, and how can you leverage it?
• Sustainability: What policies, habits, or norms ensure that initiatives underway find and use capabilities?
• Data vs. Data Assets: Do you distinguish between data and data assets?

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Conclusion
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Building robust data monetization capabilities is not a simple task, but it presents an incredible opportunity for organizations to unlock new revenue streams and achieve better business outcomes. By focusing on data management, data platforms, acceptable data use, data science, and customer understanding, companies can transform raw data into valuable, reusable assets. As you develop these capabilities, continually assess and refine your practices to ensure sustained success in data monetization.

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Book Reference

For a deeper understanding, refer to Data is Everybody’s Business: The Fundamentals of Data Monetization by Barbara Wixom, Cynthia Beath, and Leslie Owens.