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n computing, data as a service (or DaaS) is a cousin of software as a service (SaaS). Like all members of the "as a service" (aaS) family, DaaS builds on the concept that the product (data in this case) can be provided on demand to the user regardless of geographic or organizational separation of provider and consumer. Additionally, the emergence[when?] of service-oriented architecture (SOA) has also rendered the actual platform on which the data resides irrelevant. This development has enabled the emergence of the relatively new concept of DaaS.[citation needed]

Data provided as a service began primarily in Web mashups, but as of 2015 is being increasingly employed both commercially and - less commonly - within organisations such as the UN.

Traditionally, most organisations have used data stored in a self-contained repository, for which software was specifically developed to access and present the data in a human-readable form. One result of this paradigm is the bundling of both the data and the software needed to interpret it into a single package, sold as a consumer product. As the number of bundled software/data packages proliferated and required interaction among one another, another layer of interface was required. These interfaces, collectively known as enterprise application integration (EAI), often tended to encourage vendor lock-in, as it is generally easy to integrate applications that are built upon the same foundation technology.

The result of the combined software/data consumer package and required EAI middleware has been an increased amount of software for organizations to manage and maintain, simply for the use of particular data. In addition to routine maintenance costs, a cascading amount of software updates are required as the format of the data changes. The existence of this situation contributes to the attractiveness of DaaS to data consumers, because it allows for the separation of data cost and of data usage from the cost of a specific software environment or platform.
Data as a service brings the notion that data quality can happen in a centralized place, cleansing and enriching data and offering it to different systems, applications or users, irrespective of where they were in the organization or on the network. As such, data-as-a-service solutions provide the following advantages:

Agility – Customers can move quickly due to the simplicity of the data access and the fact that they don’t need extensive knowledge of the underlying data. If customers require a slightly different data structure or have location specific requirements, the implementation is easy because the changes are minimal.
Cost-effectiveness – Providers can build the base with the data experts and outsource the presentation layer, which makes for very cost-effective user interfaces and makes change requests at the presentation layer much more feasible.
Data quality – Access to the data is controlled through the data services, which tends to improve data quality, as there is a single point for updates. Once those services are tested thoroughly, they only need to be regression tested, if they remain unchanged for the next deployment.