ABSTRACT: A strategic approach to data monetization covering key processes, risks, and organizational shifts needed for success.

Read time: 4 mins.

In today’s rapidly evolving business landscape, data monetization stands as a strategic initiative to not only generate revenue but also to fortify a company’s market position. In this article, I’ll discuss a comprehensive, end-to-end approach for companies interested in unlocking the value of their data for external sale. 

Why Monetize Data?

Data monetization offers a wealth of opportunities. Companies that monetize data not only create new revenue streams but also enhance their market position, future-proof their operations, formalize data handling, and create new enterprise value. By transforming data into a marketable asset, organizations can realize significant financial gains. According to recent industry studies, nearly one-third of businesses are already earning revenue from data, while many more plan to follow suit in the coming years. This trend underscores the growing importance of data as a strategic business asset.

Moreover, organizations with monetizable data often see a ripple effect within their operations. Structured, accessible data can optimize internal efficiencies, support AI-driven decision-making, and foster a data-centric culture that empowers employees to make informed choices based on data insights.

Understanding Data Value, Demand, and Buyers

To successfully monetize data, a company must start by assessing the value of its data and understanding who might want to purchase it. Here’s a breakdown of key steps to this process:

• Evaluate Data’s Unique Value: Identify what makes the data unique, timely, or relevant to potential buyers. For instance, consumer transactional data, geo-location data, or industry-specific metrics might have substantial appeal to investors, market analysts, or retail businesses.

• Identify Data Demand: Analyze current trends and demand signals in various sectors. Corporations, Hedge funds, private equity firms, and financial analysts frequently purchase third-party data for real-time insights. 

• Define Use Cases: Use cases determine the practical applications of data for potential buyers. For example, investment firms might leverage transaction data to forecast economic trends, while retailers could use consumer behavior data to optimize marketing campaigns.

• Understand the Buyer Profile: Different types of buyers will value data differently. Quantitative hedge funds, for example, use data to fuel trading algorithms, whereas discretionary managers use it for supplementary insights in traditional research and corporations use data to plan development (e.g. store locations), and to understand consumer and competitor behavior and actions.

Navigating Risks and Handling Internal Objections

Data monetization is not without its risks, and companies often face internal resistance. Here’s how to address both:

• Assess Legal and Compliance Risks: Privacy laws and regulations surrounding data sharing and sales are complex, especially in sectors like finance. It’s critical to review data ownership, privacy policies, and contractual terms for compliance.

• Manage Reputational Risks: Selling data might prompt concerns about customer trust. Establish transparency with clear policies to avoid misunderstandings. Financial buyers, for example, have no interest in personally identifiable information (PII); anonymized data is often sufficient.

• Overcome Internal Blockers: Data sales may meet resistance from those who fear potential liabilities or operational burdens. Address these concerns by illustrating the security protocols, legal protections, and additional revenue that data sales bring to the company.

• Communicate! There is often very strong resistance to selling data at the board level and in the C-Suite.  These concerns can and should be addressed head on with a strong communication program. A communication program for your internal stakeholders, partners in your value chain, and especially customers is critical to get in front of the issues, reduce uncertainty and legal exposure.

Building a Data-Optimized Organization

Creating a data-centric organization is essential for sustained data monetization success. Here are key capabilities that make data monetization work and also makes  your organization more effective in leveraging data for internal use:

• Enhance Data Literacy: Data literacy is critical for developing a data-driven organization. Companies that boost data literacy at all levels, enable employees to interpret and leverage data confidently for tactical and strategic needs.

• Establish Data Operations: Data operations, from collection and cleansing to integration, lay the groundwork for a streamlined data monetization pipeline. Establishing efficient data operations ensures that data is organized, maintained, and ready for market.

• Optimize for Data Productization: For data to be valuable to buyers, it needs to be accessible, relevant, and easily interpreted. Data products with consistent metadata and detailed documentation creates ready-for-market assets that buyers (and internal users) need to make informed purchasing choices.

Productizing and Bringing Data Products to Market

Turning raw data into a valuable product involves several key steps:

• Refine Data Product Features: Consider how your data can be enriched to increase its appeal. This may involve tagging data (aka ticker mapping) for easier integration into buyers’ systems or mapping data to industry-specific metrics.

• Set a Pricing Strategy: Data pricing can vary widely. Do your market research to set competitive and profitable pricing based on the unique characteristics of your organization’s data.

• Develop Proof of Value: Data buyers, especially in finance, often require proof of data effectiveness. Consider working with a third party, like AltHub, to create back tests or white papers. These validations demonstrate data’s historical impact and provide a compelling case for its value.

• Create Sales and Marketing Materials: Go to market prepared with contracts, licensing terms, and targeted sales materials like product decks that appeal to different types of data procurement teams. This approach streamlines the onboarding process and positions data products effectively within the market.

Conclusion

Some companies with data products may be ready to talk to buyers. But more commonly, companies need to take some time to explore the opportunity before leaping forward. Understanding demand and pricing, navigating risks, and transforming raw data into marketable products can be overwhelming for organizations that are not used to treating their data as an asset. However, the comprehensive approach described above enables organizations to not only unlock new revenue streams, but also drive sustainable growth in a data-centric world. 

If you want expert advice tailored for where you are in your data journey, reach out to [email protected].