In today's digital age, businesses can utilize the vast amounts of data they collect and generate to create new revenue streams, develop innovative products, forge partnerships, and drive informed decision-making. This blog post seeks to clarify the various terms associated with the practice of buying and selling data, such as Data Sharing, Data Marketplace, Data Exchange, Data Commercialization, Data Brokerage, and Data Monetization.
TLDR;
Data Sharing is a broad term, including both free and paid transactions, private or public catalogs, and proprietary or open datasets.
Data Exchange Platforms facilitate data sharing among multiple parties, and lean towards being more private and B2B-oriented than their marketplace counterparts.
Data Marketplaces are the online stores for data commerce, offer various datasets to businesses and data consumers, often white-labeled or specific to industries or data types.
Data Commercialization turns data into data-driven products or services.
Data Monetization involves revenue generation from zero-party (0P) and first-party (1P) data acquired through users, applications, algorithms, and more, which could be sold through advertising, subscriptions, or other avenues.
Data Brokers consolidate and enhance data from various sources, selling the enriched data to third parties. Among all data commerce practices, data brokerage draws criticism for its opaque operation and lack of privacy considerations.
Data Sharing: A Catalyst for Economic Benefits
"Data and analytics leaders who share data externally generate three times more measurable economic benefit than those who do not." - Gartner
The practice of data sharing encompasses the exchange of data without necessarily involving monetary transactions. Predominantly used in government and academic circles, data sharing has now been adopted by small and large corporations, consulting firms, market intelligence agencies, hedge funds, analysts, and other enterprises for competitive advantages.
Data Marketplaces & Data Exchanges
A Data Marketplace is an online store that facilitates buying and selling data, and can be branded for specific verticals or companies selling specific types of data. They may be generic, or white-labeled for a particular brand. Data marketplaces are sometimes referred to as Data Exchanges. Ocean Protocol for example offers a template to brand (white-label) a Data Marketplace for bespoke purposes, for buying and selling data powered by its underlying data marketplace infrastructure.
Data Exchange refers to the broader practice of exchanging data. Snowflake differentiates between their Data Marketplace and Data Exchange services with a Data Marketplace that is more public and open, and a Data Exchange that is more private and B2B centric. In the wild, these services are named interchangeably although overall the two terms tend towards the Snowflake differentiation. Data Exchanges can include longer term relationships in the form of Data Subscriptions, where Data Marketplaces can tend to be one-off sales of datasets without prior or future relationship to the data supplier.
Data Exchanges and Marketplaces both facilitate the transfer of information between multiple parties and allow companies to access a wider range of data sources beyond their first-party (1P) data, providing critical information that is not available to them internally. This leads to deeper insights into business operations, customers, and market trends, enabling better decision-making, optimized execution of business initiatives. It also enables enhanced accuracy of statistical and machine learning models through larger and more diverse datasets, allowing for improved predictions and forecasting. Hedge funds use information sold on these marketplaces to make investment decisions and predict macro and micro trends. By leveraging multiple sources of data, companies are able to improve analytics capabilities and gain competitive advantages.
"[Informatica] B2B Data Exchange is a modern inter-company exchange to securely and collaboratively integrate any data with partner networks. It securely integrate any data with partners with agility, flexibility and productivity to drive growth and profitability." - Informatica
Examples:
Data Exchanges and Marketplaces
Snowflake Data Marketplace - Publicly accessible datasets. Snowflake Data Exchange - Secure and private data collaboration amongst business partners. Google Analytics Hub - Identifies as a Data Exchange that uniquely offers proprietary datasets such as Google Trends in addition to open and third party datasets. Offers tight integration with Google Big Query. Dawex Data Exchange Platform - Provides data exchange as infrastructure, offering white-labeled SaaS solutions for selling and exchanging data. Open:FactSet Marketplace - Open data, specializing in providing investment professionals with high-quality, easy-to-integrate data feeds. Informatica B2B Data Exchange - Offers secure data collaboration with trusted partners. AWS Data Exchange - Offers to increase speed to value for third-party datasets in the cloud. Carto Data Observatory - Specializes in high-quality location data. Canada's Open Data Exchange - Open Data Institute support collaboration with the private sector, civil society, academia, and other levels of government to promote the commercialization of open data. Data Republic - Chiefly identifies as a "Data Sharing" platform. Narrative.io - Chiefly identifies as a "Data Collaboration" platform that also offers Data Monetization services. Microsoft Azure Data Share - Chiefly identifies as a "Data Sharing" platform. IOTA Data Marketplace - A decentralized data marketplace. Ocean Protocol - Also a decentralized marketplace.
In addition to using multiple terms to describe similar services, each of the aforementioned services vary in terms of the data type, selling method, accessibility, and licensing. Although the distinction between Data Marketplace and Data Exchange is becoming less clear, differences can be observed by examining factors such as the data openness and accessibility of data catalogs, method of integration and data delivery to end-users, the nature and duration of the relationship between parties, and virtual branding capabilities such as white-labeling. Moreover, several of the above services also offer more finely tuned Data Commercialization and Data Monetization features.
Data Commercialization & Monetization
While arguably distinct from Data Monetization, Data Commercialization and Data Monetization are also frequently interchanged.
"Data commercialization can be defined as taking existing data obtained from business operations and turning it into a new revenue stream, while Data monetization is the process of using existing data to generate revenue." - AIMultiple.com
Data Monetization typically refers to the direct selling or leasing of data. This strategy often involves taking existing data collected as a byproduct of a company’s operations and selling it to other businesses or entities. For example, a company might monetize its user behavior data by selling it to market research firms. The key characteristic of data monetization is that it involves deriving value from data that already exists within the company’s ecosystem.
On the other hand, Data Commercialization is a broader, more innovative approach to data utilization. It involves creating data-driven products or services to generate new revenue streams. This could mean developing an analytics platform, offering data consulting services, or building a data-driven software product. In contrast to data monetization, data commercialization involves adding value to the data and packaging it in a way that can be sold as a standalone product or service.
Both data monetization and data commercialization aim to generate revenue from data, but they do so in different ways. Data monetization involves direct selling or leasing of existing data, while data commercialization involves the creation of data-driven products or services for new revenue streams.
See also: Fueling Growth Through Data Monetization - McKinsey & Demystifying Data Monetization - MIT Sloan.
Data Brokerage
Data brokers play an integral role in the data ecosystem, acting as intermediaries in the collection and sale of information from a variety of sources. They service businesses seeking to gain a deeper understanding of their customer base, identify emerging market trends, and decode intricate patterns in consumer behavior. Additionally, data brokers offer data enrichment services, where existing data sets are supplemented with additional information, thereby augmenting their accuracy and completeness.
Despite the value they bring, data brokers have faced criticism for their lack of transparency and privacy considerations. More often than not, consumers are in the dark about how their personal data is being used and who the end-users are. To counter these concerns and enhance their services, data brokers could adopt more transparent practices, offer more control to consumers over their personal data, and ensure that their operations are under appropriate regulatory scrutiny. In doing so, they can strike a balance between respecting individual privacy and continuing to provide their indispensable services, ultimately contributing to the creation of innovative, data-driven products and services.
Data Brokerage Use Cases:
Targeted Advertising: Data brokerage companies collect and analyze vast amounts of data to create detailed consumer profiles that can be used to target specific audiences with tailored advertisements. By purchasing data from a brokerage, businesses can gain access to information that can be used to create more effective marketing campaigns.
Risk Assessment: Insurance companies can use data brokerage services to assess risk and determine appropriate pricing for policies. By analyzing data on factors such as demographics, credit history, and past insurance claims, brokers can provide insurers with valuable insights that can help them make more accurate underwriting decisions.
Market Research: Data brokers can provide businesses with information on market trends, competitor activity, and consumer preferences. This information can be used to inform product development, pricing strategies, and marketing campaigns. By purchasing data from brokers, businesses can gain a competitive edge by having access to insights that their competitors may not have.
Hedge Funds: Hedge funds use Dat Brokerage services for Data-Driven investing, enabling them to make more informed investment decisions.
In Summary
While the terms associated with buying and selling data may seem distinct, they have significant overlap. Data Exchanges and Data Marketplaces, Data Monetization and Commercialization, and Data Brokering fundamentally rely on data sharing, exchange, and marketplaces to achieve their goals.