Amazon Launches AWS Data Exchange to Access Data in the Cloud

Tom Pringle
Tom Pringle  |  November 19, 2019

Last week, Amazon announced the launch of AWS Data Exchange, a new market for third-party data sets to power and enrich analytics and machine learning use cases. 

The Data Exchange integrates data from third-party vendors (such as Dun & Bradstreet, Reuters, and Change Healthcare) within the service, which covers data types such as firmographics, de-identified health care data, and weather, among others. 

Alongside paid-for options, the Data Exchange also offers free data sets for use. Adopters can use the Data Exchange’s API to load data directly into Amazon Web Services' (AWS) S3 before using the range of analytics and machine learning solutions available from the vendor.

AWS is also focusing heavily on the secure and private use of these data sets with vendors completing a qualification process, and the service prohibiting the sharing of sensitive personal data and any personal data that is not already publicly and lawfully available.

Data as a service (DaaS) as a means of delivering third-party data to enrich existing first-party (that is, an organization’s own data sets) data is a well-established market and use case mostly found in marketing, used for audience building and personalization. (Data management platforms that offer DaaS included examples such as Oracle's Data Cloud and Salesforce's Audience Builder.)

 

However, the potential reach of this data extends beyond marketing use cases. These are primarily found in analytics and machine learning projects, which may be modeling, for instance, future demand by enriching existing data using third-party firmographic data or weather data. There are examples of third-party data for these use cases already, including Qlik's DataMarket.

When it comes to analytics and machine learning, combinations of data are often the source of deeper insights and a useful addition to either incomplete or missing first party data. By providing these in one marketplace, supported by governance of those data to address privacy concerns, is a big step to making analytics and machine learning uses cases more effective. At G2, we expect to see more of these types of marketplaces become available over the coming year as enterprises continue on the journey to being data driven, and machine learning becomes more accessible to a greater number of firms. It also offers an option for those organizations with data that previously were unsure of how to monetize their data securely.

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Tom Pringle
Author

Tom Pringle

Tom is vice president of technology research at G2 leading three key topic areas: AI & analytics, cloud & IT, and security & privacy. Tom's entire professional experience has been in information technology where he has worked in both consulting and research roles. His personal research has focused on data and analytics technologies; more recently, this has led to a practical and philosophical interest in artificial intelligence and automation. Prior to G2, Tom held research, consulting, and management roles at Datamonitor, Deloitte, BCG, and Ovum. Tom received a BSc. from the London School of Economics.