‍Definition

Data Monetization refers to the generation of financial returns from data assets. It encompasses transforming raw data into actionable insights that drive economic value for the organization.

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The Two-Step Process:

1. Value Creation:
   • Goals: Streamlined processes, optimized supply chains, enhanced employee satisfaction, and products that delight customers.
   • Data-Insight-Action Process: Data is used to develop insights, informing actions that generate value.
2. Value Realization:
   • This involves transforming created value into financial returns, ensuring that every initiative contributes to the bottom line.

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Research Insights:
‍High-performing organizations generate 10% more revenue from data monetization compared to their low-performing peers.

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Creating Value from Data:

‍The data-insight-action process occurs when people or systems use data to develop insights that inform actions, generating value.

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Challenges in Value Realization:

‍The second step can be tricky, often requiring the elimination of "slack" that arises from data monetization initiatives that do not contribute directly to the bottom line.

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Three Approaches to Data Monetization
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1. Improving:
   • Value-Creating Process: Enhances internal processes and efficiencies.
   • Value Realization Process: Cost savings and productivity gains.
   • Accountable for Outcomes: Operational managers.
   • Key Risks: Process improvements may not lead to significant financial gains if not well-executed.
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2. Wrapping:
   • Value-Creating Process: Adds data-driven features and experiences to existing products.
   • Value Realization Process: Increases product attractiveness and customer satisfaction.
   • Accountable for Outcomes: Product managers.
   • Key Risks: Added features may not translate into higher sales or could increase complexity and costs.
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3. Selling:
   • Value-Creating Process: Develops new data-based products or services.
   • Value Realization Process: Generates direct revenue from selling information solutions.
   • Accountable for Outcomes: Sales and marketing teams.
   • Key Risks: Market demand might be lower than expected, leading to lower sales than projected.

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Research Findings:

• Improving: 50%
• Wrapping: 33%
• Selling: 19%

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Warning

Data investments can inadvertently make an organization more costly to run if there is no one ensuring that the value is both created and realized. It's crucial to have a clear strategy and accountable leadership to ensure that data initiatives contribute to financial returns.

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Questions to Ask Yourself
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What kind of value do you frequently create with data?
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Do you know if it made it to the bottom line?
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Can you think of an opportunity for each of the three methods in your organization? (Improve, Wrap, Sell)
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Is the term "data monetization" acceptable to use in your organization, or does it have some unethical associations?
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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.

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