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Priyanka Bhardwaj
Priyanka Bhardwaj

Data is the greatest resource for any modern business. It helps in providing new insights that can unlock new revenue streams and cost-saving measures, making your business more profitable and competitive.

What is data monetization?

According to Gartner, data monetization refers to using data to obtain quantifiable economic benefits. This could be by analyzing data to improve business outcomes or to discover new opportunities. Internal or indirect methods include using data to make measurable business performance improvements and inform decisions.

Direct data monetization involves selling direct access to your data to third parties.

Once data is refined, it can provide valuable insights and hold tremendous value for each organization's business's profitability.

Data monetization aims to generate revenue from data sources such as IoT devices, social media, products used by customers, etc., and give businesses a competitive edge.


Data monetization leads to the following benefits:

  • Improved and impactful business decisions: Data insights lead to a timely and granular decision-making process, resulting in an increased revenue stream for the business.
  • Increased productivity and profitability: Insights from data lead to discovering new potential customers and understanding customer churn and demand patterns. All this increases overall productivity and profitability.
  • Increased data value: Data becomes more valuable as data insights help to understand current market demand, customer behavior, competition, etc.
  • Improved customer experience: By using data insights, a business can better understand the customer. Organizations can improve their products and services for a better and optimized user experience.
  • Increased targeted marketing: Customer behavior and sentiments can be leveraged to generate personalized advertisements, thereby taking a step back on mass marketing. This also results in reduced marketing costs.

Strategies for effective data monetization

An effective data monetization strategy should focus on generating insights from data that could improve your business. Unconventional data monetization strategies can make the data more prone to cybersecurity attacks which can hamper a business's reputation. Hence, data privacy rules should be followed diligently while using and selling user data.

The following are the strategies for monetizing data:

  1. Indirect/internal data monetization entails incorporating data insights into your business processes to forecast demand and churn, slash waste, identify customers, optimize pricing and supply chain, and so on. Organizations can benefit by sharing data internally within departments, which can lead to measurable business process improvements. This would help in:
    •    Enhancing product development process: Using data to discover new customer trends can lead to the development of new products to meet demands.
    •    Identifying problems and bottlenecks: Using data insights (especially in the manufacturing industry) to help make decisions for creating by-products from reduced waste and, in turn, decrease unnecessary expenditure.
    •    Empowering sales: Identifying customer problems through data to help make decisions that can improve customer service and target new customers, thereby increasing the overall business sales.

  2. Direct/external data monetization entails trading data-derived knowledge, forecasts, and insights for money, converting them directly into revenue-generating assets. Data can be sold to third parties either in raw or transformed form. For example, selling data to the industry value chain. This includes selling industry insights like reports and surveys to vendors & suppliers, retailers, resellers, and partners.

Prerequisites for data monetization

The following parameters should be considered before trying to use data as an asset for generating revenue streams using data monetization:

  • Evaluate your data: Through assessment and evaluation, determine the potential value of your data in terms of revenue and whether it is worth selling in the market.
  • Organize your data: To monetize data, the data needs to be aligned in a way that it is easily available and well-organized for the end customer. This involves building metadata, including a data dictionary, tags, notes, etc.
  • Assess the technology stack: As data is growing rapidly, current infrastructure stack assessment is required to understand if it is future-proofed to handle data in the future.
  • Identify potential data buyers: Organizations need to identify potential data buyers to understand what and why the data is required.
  • Selling and pricing decisions: A business needs to decide where it shall sell the data in the marketplace and at what competitive prices.

Data monetization facilitated by data marketplace

The data marketplace segmentizes the audience and provides the right kind of data to the right set of customers.
A data marketplace is an online market where anyone can buy or sell the data or can exchange the data for mutual benefits. The person who provides or sells the data is known as a provider, and the person who buys the data is known as a consumer or buyer. Research, demographic, firmographics, market data, business intelligence, and public data can all be found in the data marketplace. For data providers, the data marketplace is the place where they can generate money that encourages individuals and businesses as well.

Traditional methods for collecting and sharing data using email, FTP, ETL pipelines, APIs, etc., may not be optimized enough with the rapidly increasing data volumes. Also, these do not provide real-time data access. To overcome this issue, cloud-based solutions like Snowflake, AWS, etc., provide real-time data that can be used directly for analysis and BI dashboard.

Data Monetization facilitated by Data MarketplaceData monetization facilitated by data marketplace

Data Monetization using Snowflake’s data marketplace

Snowflake is one of the fastest-growing data marketplaces providing real-time data access to consumers. One of the primary advantages of using this marketplace is that the data is continuously refreshed, providing up-to-date data to the end customers.

The Snowflake marketplace utilizes Snowflake’s Secure Data Sharing feature to connect data providers with consumers.

Snowflake marketplace readily provides varied datasets related to multiple business units like healthcare, retail, banking, etc. One can become a data provider by publishing data into the marketplace, making it an ideal proposition for monetization.

How do data providers and data consumers leverage the Snowflake Marketplace?

As a data provider, you can:

  • Share real-time data without creating copies of the data or adding any data integration tools to the customer
  • Eliminate the costs of building and managing data pipelines to deliver data to the customer
  • Publish data listings for datasets that can be customized for the consumer
  • Publish data listings for free-to-use datasets to generate interest and new opportunities among the Snowflake customer base

As a data consumer, you can:

  • Combine new datasets with your existing data in Snowflake to derive new business insights
  • Get quick and easy access to raw data from vendors
  • Have datasets available instantly and updated continually for users
  • Eliminate the costs of building and managing data pipelines to deliver data to load and update data
  • Use BI tools of your choice to visualize the data

Type of Data Listings

To share and consume data, Snowflake marketplace provides two types of data listings:

Free Listings

A free listing provides instant access to a published data set. This type of listing is best for providing generic, aggregated, or non-customer-specific data. Free listings can be free to access, or you pay for off the Snowflake platform, or paid off-platform listings.

To access data using free listing, the consumer needs to agree to the provider’s terms of use and Snowflake’s consumer terms.

Personalized Data Listings

A personalized listing allows customers to request specific data sets. This can be premium data that a provider charges for or data that is specific to each consumer.

Consumers must submit a request and provide their contact information to access data from a personalized listing. Once a request is submitted, the data provider is notified. The provider then contacts the consumer.
Each data provider can have different commercial terms. Once those are agreed to, then the personalized data set is created and shared with the consumer.

The following are some other examples of a data marketplace:

AWS Marketplace

A curated digital catalog that makes it easy for organizations to discover, procure, entitle, provision, and govern third-party software. You can find thousands of software listings from popular categories like security, business applications, and data & analytics, and across specific industries, such as healthcare, financial services, and the public sector.

With AWS Marketplace, you can shorten procurement times, implement the controls you need to operate confidently, and enable your organization to unlock innovation.

Oracle Data Marketplace
More than 30,000 data attributes are available from Oracle Data Marketplace data providers. Around 80% of the top 20 advertising networks, trading desks, portals, and creative optimizers run high-performance campaigns and use data from this platform.
Microsoft Azure Data Share
Data providers have complete control over the data, deciding what to share and with whom. Data sharing can be automated, scheduled, as well as stopped at any moment. Each data share can have its own set of terms of service, which can be changed as needed.

Considerations and responsibilities for ethical data monetization

  • Ensure necessary data governance framework: It is important that the security and privacy of the monetized data are part of the data governance activities to identify and mitigate any risks and challenges.
  • Protect data ownership: While collating and selling data to 3rd party buyers, data should be taken with the owner’s consent.
  • Understand regulations: Regulatory restrictions pertaining to collecting, analyzing, and selling data should be considered diligently.
  • Respect data privacy: Data privacy and security practices should be easy to follow and accessible.


Because of the Big Data revolution, we now have access to a variety of data categories. A data marketplace provides data products to address any user query. It also provides a means to generate revenue out of the data for data providers and convert the data as an asset. Data that aids in developing and delivering new insights can help identify emerging business winners by increasing profits and defining internal value .

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