Like many areas of technology, cloud computing tends to be littered with initials. For those new to the concept, they are understandably a rather repellent deterrent. Being subject to a stream of acronyms, written or vocalised, can be confusing. Many of them end in “-aaS,” here meaning “as-a-Service,” which reflects how the cloud is delivered. There are SaaS (Software-as-a-Service), PaaS (Platform-as-a-Service), IaaS and, crucially here, DaaS. Usually this one stands for Desktop-as-a-Service. Now it faces competition for that title from the emergent Data-as-a-Service. This is a relatively recent cloud development, predominantly used in the healthcare sector at present, but could have implications for commercial activity in the long run.
The Current State Of Affairs
SaaS is, according to various sources, the most popular of the services cloud offers to business. SaaS uses the web to deliver and run business applications, using an interface on the user’s web browser without any lengthy installations or downloads. A third-party vendor manages the system. PaaS remains a commonly used framework to build applications on, but like SaaS, it hands control of those applications to the developers. Users have the flexibility to develop, test and deploy new functions in a cost-effective, efficient way.
IaaS is a rather more self-service option than the previous two. End-users have more control in this system, which Amazon Web Services and Microsoft Azure favour highly at present. It benefits from the sprouting popularity of hybrid cloud computing in business, and is often seen as an arbiter of agile, speedy business networking. Desktop-as-a-Service sees the user’s virtual desktop outsourced and monitored on a consistent basis by a third party.
The New Entrant
Data-as-a-Service has notably arisen from a collaboration between Amazon Web Services and the American Heart Association. The idea is that AWS will provide not only storage services, but also the data itself. When released, the platform will allow clinicians and medical researchers to collate, and therefore, analyse medical data from thousands of different heart disease cases. In doing so, they can source and consequently create innovative new methods of treatment. Users will not have to locate, load and complete complex data evaluation; the data is primed and ready for academic examination.
For the moment, this is a one-off arrangement between cloud provider and user. AWS are not projected to draw a great profit from it. However, it is not difficult to envisage the template becoming relevant in a wider commercial context.
From a business perspective, Data-as-a-Service particularly refers to acting on commercial insights drawn from big data analysis. This is the information process that informs the most successful decision-making of today, and of the future. Many cloud providers will include application packages to cover big data analysis. The most prudent examples include evaluation of marketing and social media initiatives, as well as customer service and satisfaction.
Big Data-as-a-Service is a broad brush, often used to refer to the data itself, the supply of analytical tools, the process of analysis, and the resultant reports and conclusions. However, business users should theoretically be free to pursue their commercial interests, with the cloud provider overseeing technical support matters.
This measure is not industry-specific; it is applicable to any sector in business, and any size of company. For startups, it is particularly useful as it grants access to analysis applications they might not otherwise have. In addition, the cloud platform allows them to only pay for the data they use, rather than paying a large blanket cost covering a certain function or time period. For bigger businesses, the motivation is slightly different. The importance of getting business decisions right in large enterprises is ever more paramount. The potential of losing ground to a market rival is greater. The financial losses associated with that are correspondingly larger.
These businesses need all the advice and information they can get. To inform decisions with machine-learning analysis, rather than human evaluation or even pure instinct, is therefore hugely valuable. We live in a world where more data is becoming cloud-based, especially as mobiles and wearables grow in seemingly perpetual popularity. That data is a priceless mine of information for businesses to use and promote themselves to consumer accordingly. For that, data-as-a-service offers an innovative platform to facilitate long-term growth.
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