Data monetization pertains to generating revenue benefits from the available data residing in databases within an organization. According to the latest studies, data marketplaces are expected to be valued at over $3 trillion by 2030.
Most of the companies operating today are data-driven as they have started identifying the value of data. The value of data and data marketplaces are evolving with the growth of big data and data requirements of companies.
In simple terms, these are online marketplaces where we can buy and sell data of any sort. Data marketplaces offer several kinds of data from a wide range of different data sources. These data include Business Intelligence, demographics, research, and marketing data.
Data types are structured and offered to clients by data providers. Providing buyers with more choice of high-quality data generates more engagement and encourages fair pricing between the sellers.
Every company has the potential to earn revenue from the information it generates. In a recent study of more than 400 organizations, only 1 in 12 were monetizing their data to its fullest extent. Modern data monetization strategies can help you open brand new revenue streams.
There are 3 key steps to monetize your data and drive new revenue streams.
- Understand your Data Offerings – What types of data or data services are you producing that would be valuable to other organizations?
- Decide How to Price Data Products – What should be the pricing strategy followed, and how easy would it be to put into action?
- Know About Data Sharing Methods – Which distribution channel should be selected for data sharing that adheres to GDPR and data governance policies?
Once you have decided on the data offerings and priced the data products, the key to successfully monetize your data is by sharing the data with the clients/ prospects. The customers and business partners would be eager to get data from you, especially if you can make the process easy and secure. Traditional data sharing technologies often use FTP, Cloud Storage, or API. These methods generally do have few disadvantages.
- Storage costs for both vendors and buyers
- ETL costs and effort
- Security vulnerabilities
- Service and support costs
- Latency and potential errors leading to poor customer experience
How do we overcome these challenges? There are platforms that make revenue generation easier with secure data sharing methodologies. Snowflake is one of the most secure and innovative platforms.
Snowflake & Data Monetization
Snowflake is an analytic data warehouse provided as Software-as-a-Service (SaaS). It provides a data warehouse that is faster, easier to use, and far more flexible than traditional data warehouse offerings.
Snowflake allows companies to easily publish a variety of data sets that become immediately available for use or purchase for clients. Snowflake Data Exchange, a modern data sharing method, reduces the time to market and significantly influences customer success.
Data Exchange is your own data hub for securely collaborating around data between a selected group of members that you invite. It enables providers to publish data that can then be discovered by consumers.
The benefits of Snowflake Data Exchange over Traditional Data sharing Methods are:
- Secure Data Sharing
- Exchange data within your organization between different business units. Collaborate with external parties such as vendors, suppliers, partners, and customers.
- Reduce Time to Market
- Break down data silos and reduce time to market.
- Interchange data with third-party vendors to help augment internal datasets.
- Break down data silos by scaling multiple data sets from different sources within your organization.
- Find and consume data on other Data Exchanges to get business insights.
- Speed of Processing
- Snowflake’s multi-cluster shared data architecture is designed to process enormous quantities of data with maximum speed and efficiency.
- All data processing horsepower within Snowflake is performed by one or more clusters of computing resources.
- Data is cached locally within computing resources, along with the caching of query results, to improve the performance of future queries.
- Cost Benefits
- The costs for sharing data with Snowflake are minimal and straightforward.
- Simply pay for the data you store, i.e., you only pay for what you use.
- Reduce extract, transform, load (ETL), and data pipeline maintenance costs.
- Control and Govern Access
- Managing membership
- Granting and revoking access to data through standard and personalized listings
- Auditing data usage
- Applying security controls to your data
Snowflake promises to accelerate the data exchanges by reducing the barriers to access data and time to market. It increases the intrinsic value of data by allowing to share and access of data between providers and consumers.
A famous telecom organization in Europe was sitting on large silos of data that they could not monetize properly because of the complex architecture of the data warehouse operations and data security challenges involved in the data sharing process. The company has Customer Daily Records (CDR) of its subscribers that contains location data of the users. This data can be used to identify the places people visit and help with building consumer profiles. The gathered data allows advertisers to target messages to specific users while tracking whether they visited a retail store after seeing a mobile ad. This helps them plan personalized marketing strategies and business goals based on demography profiles for targeted users.
However, due to the data privacy policies of the European Union like GDPR, organizations were struggling to share data with their potential clients. The GDPR policy makes it mandatory for organizations to ensure that the customer’s personal information is not shared with third parties without the customer’s consent and involves hefty fines and penalties for the data breach. Even the data sharing process was a source of concern as the data was often shared in text/excel files because of the different database architecture of the clients. With growing data privacy concerns and challenges in creating datasets adhering to the GDPR policies, organizations are strictly asked not to share customer data with third parties. The current system architecture forced the organizations to employ a large number of resources to extract the data from the database system and ensure that customer data is not compromised at any point.
The companies were evaluating the possibilities of a potential system that would help them monetize the data they currently hold. The introduction of Snowflake into the organizational architecture solved the data monetization problem and improved the overall data culture in the organization. The unique architecture of Snowflake separates the data storage and computation layer to enhance organizational productivity. The pay as you use policy of the Snowflake and the zero maintenance of infrastructure helped the organization phase out the complex on-premise solutions required to handle the huge data volume. Easy connectivity with the existing solutions used for data analytics practice and on the fly scalability of the computation layer helped the organization increase productivity. It also paves the way for seamless integration to the organization’s architecture.
The Data Marketplace of Snowflake ensured secure data sharing with third parties adhering to the GDPR policies. The in-built data security policies and features minimize the role of organizations to provide data privacy as well. This enables the organizations to make only those data points visible to end-users that they seemed apt for sharing. It also ensures that the data always resides in the organizational Snowflake database rather than on third-party databases. Moreover, the organizations could reach out to thousands of potential clients through Snowflake Data Marketplace without relying on any intermediatory sources.
All this ultimately brings out the scope of using the existing data to drive revenue to the organization and highlights the importance of having a complete environment like Snowflake to capture, preserve, access, and transform data.
Authors: Rahul Vijayan, Firdous Maqbool