January 2, 2024

How to Monetize Financial Data in Snowflake

By Justin Delisi

The financial data market is a booming industry currently estimated to be worth over $30 billion, with much of this growth being driven by the increasing demand for data-driven insights in the financial sector. 

As a result, financial institutions are sitting on a goldmine of valuable data that can be monetized.

The Snowflake Data Cloud is a leading cloud-based data platform that provides a powerful and secure environment for storing, analyzing, and sharing financial data. Snowflake has created a Marketplace entirely to share data between organizations for free or for profit. 

In this blog, we’ll walk you through how the Snowflake Marketplace works, the different pricing models available for selling your data, and strategies you can employ to monetize your financial data.

What is the Snowflake Marketplace and How Does it Work?

The Snowflake Marketplace is a platform within the Snowflake Data Cloud that allows users to discover, try, buy, and integrate third-party data and solutions directly into their Snowflake environment. 

Users can explore a variety of data sets, connectors, and applications within the marketplace, acquire them seamlessly through Data Shares, and incorporate them into their Snowflake data workflows. This marketplace makes it easy to find and integrate external data sources, enhancing the versatility and capabilities of Snowflake and, ultimately, helping businesses get more value from their Snowflake investment. 

There are two types of listings in the marketplace: public and private. 

Private allows you to create a share only seen by specific customers you designate. Private listings let you take advantage of the capabilities of listings to share data and other information directly with other Snowflake accounts in any Snowflake region. Conversely, public listings are visible to any Snowflake account in the Marketplace.

Pricing Models for Financial Data in Snowflake Marketplace

In the Snowflake Marketplace, data providers can leverage two flexible pricing models to monetize their offerings: usage-based and subscription-based.


This pricing allows providers to charge consumers based on their actual data usage. This can be implemented in three ways:

Monthly Base Fee

Charges a fixed price per calendar month in which at least one query that accesses paid data is run. The fixed price is not charged if no query is run against data in the listing. The monthly fee is not prorated. For example, here’s a list of Sentiment Indicators for US Stocks being sold for $1,500 a month.

Per Query

Charges a fixed fee for each query executed against the data. This can be implemented with or without a monthly fixed fee. Here is an example of US Equities Short Share Volume Data being sold on the Marketplace for $1/ query. 

Billable Events

Only listings that share an application can use Custom Event Billing, which charges based on billable events. With Custom Event Billing, you can charge a price for specific types of usage of your application. For example, you can charge:

  • Per row of data modified by the application

  • Per procedure call made by the application

  • Per row of data used by the application

  • Per unique row of data updated in a month by your application (monthly active rows)

You can also charge for other events that you define in your application code. Note that this feature is currently in public preview.


For this pricing plan, consumers are charged upfront for access to the data product for a specified term. You can offer the listing with recurring billing for a subscription that auto-renews (preview) or non-recurring billing for access for a fixed term. The provider determines the term, which can be anywhere between 1 and 36 months. 

The choice between these models depends on the specific data product and its target audience. Usage-based pricing offers flexibility for consumers and can be beneficial for providers with unpredictable usage patterns. Subscription-based pricing provides predictable revenue streams and can be attractive for consumers who need consistent access to the data.

Strategies for Monetizing Financial Data with Snowflake

Raw Data

The first and most straightforward strategy to monetize your business’s financial data is to simply make raw data (stripped of Personally Identifiable Information (PII)) available in the Marketplace. This allows other organizations to use the data to create insights. 

Some examples of in-demand financial data include:

  • Financial Statements

    • Income statements

    • Balance sheets

    • Cash flow statements

    • Statement of retained earnings

  • Credit and Lending Data

    • Credit scores

    • Loan performance data

    • Credit card transaction data

    • Mortgage data

  • Market Research Data

    • Consumer spending patterns

    • Market trends and analysis

    • Industry-specific financial data

Here’s an actual example of credit trends being sold by a financial institution in the Snowflake Marketplace:

Enriched Data for Business Intelligence

The second strategy is to enrich your raw data by deriving your insights and selling that data on the Marketplace. This can be even more enticing to potential data consumers as they won’t have to employ their own data analysts. 

Several key steps are involved in enhancing your raw financial data:

  • Standardization and Cleaning

    • Cleanse the raw financial data to ensure accuracy and consistency. Standardize formats, correct errors, and remove duplicate entries. This step is crucial for maintaining data integrity and reliability.

  • Normalization and Aggregation

    • Normalize financial data by standardizing units, currencies, and formats. Aggregating data to different time intervals (e.g., daily, monthly) or grouping by relevant categories helps users analyze trends and make comparisons more effectively.

  • Data Integration

    • Integrate financial data from various sources to provide a comprehensive overview. This may involve merging datasets from different markets, exchanges, or financial instruments to offer a holistic perspective to potential buyers.

  • Geospatial and Temporal Enrichment

    • Add geospatial information if applicable, especially for financial data with geographic relevance. Additionally, enrich data with temporal information, such as timestamps and date ranges, to facilitate time-based analysis.

  • Derived Metrics and Ratios

    • Create derived metrics and financial ratios to provide additional insights. For example, calculate profitability ratios, liquidity ratios, or financial performance indicators to offer a deeper understanding of the financial data.

  • Text Analysis for Financial Documents

    • If the dataset includes financial documents or reports, perform text analysis to extract valuable information. This could involve sentiment analysis, keyword extraction, or identifying key financial indicators mentioned in textual content.

For example, here’s a curated dataset of market-derived signals and credit default swaps on the Marketplace:

Data Clean Rooms

The Marketplace can also be utilized to market your data for use in a data clean room. A data clean room is a secure and controlled environment that allows multiple companies, or divisions of a company, to bring data together for joint analysis while adhering to certain guidelines and regulations. 

Data clean rooms control what data comes in, how the data in the clean room can be joined to other data in the clean room, what types of analytics each party can perform on the data, and what data (if any) can leave. 

Any PII data loaded into the clean room is secured and encrypted. The data owner has full control over the clean room, while approved partners can get a feed with anonymized data. 

Utilizing private listings in the Marketplace, data providers and consumers can share data with each other to combine them and provide an enriched outcome for all parties.

Legal and Ethical Considerations for Selling Data

Selling financial data (or any type of data, really) raises several legal and ethical concerns that businesses must navigate to ensure compliance and maintain public trust. From a legal standpoint, data privacy regulations such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States impose strict requirements on collecting, processing, and transferring personal data. 

Businesses must obtain informed consent from individuals before selling their data, provide transparency about how the data will be used, and implement robust security measures to safeguard against data breaches. Failure to comply with these regulations can result in significant fines and legal consequences.

Ethically, selling data introduces considerations related to user consent, fairness, and the responsible use of information. Businesses must be transparent about their data practices and ensure that individuals know how their data will be used. 

Additionally, there is a responsibility to avoid discriminatory practices or the exploitation of vulnerable populations through the sale of sensitive information. Striking a balance between business interests and ethical considerations is crucial for maintaining a positive reputation and establishing customer trust. 

To navigate these complexities, companies should adopt ethical data practices, prioritize transparency, and stay informed about evolving legal frameworks surrounding data protection and privacy.

If the data you plan to sell on the Marketplace contains Personally Identifiable Information (PII), Snowflake has many features to mask or omit that information before it gets to the Marketplace. This is an essential step in ensuring your customer’s information is not compromised.


In summary, the financial data market’s impressive $30 billion valuation highlights the increasing importance of using data-driven insights in the financial sector. Companies in this industry are realizing the immense value of the data they have, and that’s where platforms like Snowflake step in to play a crucial role in securely storing, analyzing, and sharing this valuable data.

The introduction of the Snowflake Marketplace is another sign of how data monetization is evolving. It provides a dedicated platform where organizations can share financial data, whether it’s for free or for profit, marking a significant shift in how data is exchanged within the industry.

If your organization is interested in monetizing your financial data in the Marketplace, the Snowflake experts at phData can help! Reach out to us today for advice, best practices, actionable strategies, and more!


Within your Snowflake account, navigate to the Marketplace and click the Become a Provider button at the bottom of the page. Users with the CREATE DATA EXCHANGE LISTING privilege can create a listing for data they’d like to sell within their Snowflake account.

Yes, you can modify, unpublish or delete a listing from Provider Studio in Snowsight. When you unpublish a listing, existing consumers can still access the data product associated with it unless you remove them from the share. To remove a listing and access to the listing for all consumers using the listing, delete the listing.

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