Data Monetization: Value Realization and Data Sharing

To effectively participate in the data sharing economy, companies can form partnerships with organizations that require specific data to train AI models. The demand for real-world data to enhance AI capabilities is on the rise. As foundational general intelligence models mature, firms are increasingly focusing on industry-specific or company-specific applications. This specialization is where the majority of AI’s value will be realized.^[3] Venture capital firms have already shifted their investments toward companies that concentrate on these specialized applications. The ability to tailor a model to a specific use case will become the most critical challenge in the AI value chain, prompting foundational model companies to compete on ease of use and integration.

However, they will need context-specific data to train for those use cases. Synthetic or manufactured data provide a starting point but will only get so far. A good example of a company that is active in this space is Appen.^[4] The firm provides high-quality datasets that power some of the world’s leading AI models. Its datasets are acquired through the purchase of raw data and through a broad network of human-in-the-loop trainers that refine data to be context-specific. A real differentiator for Appen is its ability to work with companies that have the foundations of their AI models figured out but need to tune it to be more effective.

Companies interested in going down this path need to be mindful of the challenges they may encounter:

• Complying with data sharing regulations
• Providing well-curated, complete datasets
• Preparing complex datasets for external use
• Protecting consumer privacy
• Finding buyers and setting fair prices for unique datasets

Overcoming these challenges will yield rewards:

• New revenue from selling unused data
• Potential long-term partnerships with AI companies
• Improved internal data quality through preparation for sale
• Establishing your firm as a leader in your industry by providing quality data

Some companies may seek a more proactive approach by establishing a dedicated data sharing capability. This can involve participating in data sharing marketplaces or creating a specialized entity to sell enterprise data. The healthcare data sharing market serves as a prime example. Firms like Datavant and Iqvia^[5] facilitate the transfer and transformation of healthcare data, selling it to pharmaceutical companies and researchers. These aggregators offer clean, standardized data and a robust platform for analysis. Data is available for licensing as a onetime or continuous feed. Additionally, these companies provide consumers with a technology platform featuring a user-friendly interface for data analysis. Beyond healthcare, industry-agnostic marketplaces such as the AWS Data Exchange and the Databricks Data Marketplace also exist.

To establish a formal data sharing capability, consider:

• Building trust with strong risk management and clear data policies
• Ensuring data quality and standardization for ease of use
• Developing secure infrastructure for data exchange
• Creating customer-centric services and offerings.

This can involve participating in data sharing marketplaces or creating a specialized entity to sell enterprise data.

In financial services, data is crucial for personalized services and fraud prevention. However, privacy concerns have historically limited data sharing. The need to balance data security and value extraction has prevented institutions from fully leveraging their data. Newcomers like Yodlee and Plaid^[6] have addressed this with open finance platforms and take several measures to ensure data privacy and protect sensitive financial information.

Yodlee

Yodlee employs robust security measures to safeguard data during transmission and storage. Here are some key practices:^[7]

• Encryption: Yodlee uses industry-standard encryption protocols to protect data during transmission and storage, ensuring that financial information remains confidential and protected from unauthorized access.
• Multifactor authentication: Yodlee utilizes multifactor authentication to verify the identity of users before granting access to their financial data. This additional layer of security helps prevent unauthorized logins and protects against fraud.
• Bank-level security: Yodlee adheres to the same high security standards as banks, ensuring that financial data is handled with the utmost care and protection.

Plaid

Plaid also prioritizes data privacy and security through several measures:^[8]

• Encryption: Plaid encrypts financial data and only shares what is needed to power the app and protect consumers from fraud.
• Consumer control: Plaid ensures that consumers have informed control over their private financial data. It provides a resource called the Plaid Portal, which allows users to manage their connections and control the sharing of their personal data.
• Compliance with regulations: Plaid’s security practices are designed to meet or exceed industry standards and comply with regulatory requirements.

In a landmark regulatory move, the Consumer Financial Protection Bureau (CFPB) finalized a rule in October that significantly bolsters consumer rights, privacy and security over personal financial data.^[9] This rule mandates that financial institutions, credit card issuers and other providers must unlock and transfer personal financial data to another provider at the consumer’s request, free of charge. By promoting competition and consumer choice, the rule aims to lower loan prices and enhance customer service across payments, credit and banking markets. Consumers will gain access to transaction information, account balances, payment initiation data, upcoming bill information and basic account verification details. This is expected to create numerous data sharing opportunities within the industry.

The CFPB’s introduction of the Personal Financial Data Rights rule marks a significant step toward open banking. This rule empowers consumers by granting them control over their financial data and improving access to products while safeguarding against potential misuse. Initially impacting larger financial entities, the rule is pivotal to the industry’s growth and sets the stage for open banking, a concept familiar in Europe for well over a decade. Overall, the rule is expected to create numerous data sharing opportunities within the industry, but it will also require financial institutions to invest in new technologies and processes to meet the regulatory demands. This shift toward open banking will likely lead to increased competition and innovation in the financial services sector.