Data as a service
In 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) and the widespread use of application programming interface (API) 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.
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. Sensing as a Service (S2aaS) also shares a similar vision. S2aaS is presented as a business model that aims to use Internet of Things data to create data trading marketplaces.
More recently, Data as a Service solutions offered by leading vendors (MuleSoft, Oracle, Microsoft) help organizations more rapidly ingest large volumes of data, integrate that data, analyze the data and publish the data to business users in real-time using Web service APIs that adhere to the REST architectural constraints (also known as RESTful API)
Benefits of DaaS
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.
There are hundreds of DaaS vendors on the Web, and the pricing models by which they charge their customers fall mainly into two major categories.
- Volume-based model that has two approaches:
- Quantity-based pricing: This is the simplest model to implement. The vendors charge their customers based on the amount of data they want to use. Subscriptions for an unlimited amount of data is referred to as the fire-hose approach.
- Pay per call: In this approach, vendors charge for each call from the customer to the API.
- Data type-based model: In this model, vendors charge based on the type or attribute of data that customer needs. For example, geographic, financial and historical data necessary for customer business are examples of types of data upon which pricing may be based. Some vendors such as Microsoft Azure store the data in three different types (blobs, queues, and tables).
Some DaaS vendors have restrictions on subscription, such as minimum or maximum space and time (monthly or yearly).
The drawbacks of data as a service are generally similar to those associated with any type of cloud computing, such as the reliance of the customer on the service provider's ability to avoid server downtime. Specific to the DaaS model, a common criticism is that when compared to traditional data delivery, the consumer is really just "renting" the data, using it to produce a graph, chart or map, or possibly perform analysis, but for data as a service, generally the data is not available for download.
"Service automation units" (code that expresses the service interface) may contain methods for all "CRUD" operations (create, read, update, delete), as in traditional data operations, but data as a service is generally limited to read.
- Machan, Dyan (August 19, 2009). "DaaS:The New Information Goldmine". Wall Street Journal. Retrieved 2010-06-09.
Unfortunately, the business world has given this baby a jargony name: data as a service, or its diminutive, DaaS. It rhymes with SaaS, its better-known cousin that stands for software as a service.
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