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In the age of data-driven decision making, the swiftness of the human-data interaction is as important as the reliability of the data. As the data-sets are getting larger and larger, running between sources to make an analysis becomes harder. The fact that data sets are got larger may sound simple at the first sight; but, without going deeper, one should understand that big data does not mean solely quantitative data (i.e. increased number of sample point). Concern on the big data comes from its bidirectional expansion. That is, data definition is expanded onto things we didn’t call data until recently; thus, it got bigger. Therefore, as the paradigm of data altered, the solution to related problems are changed, too. At least for now, the paradigm suggests its solution to every problem in 21st century: internet.

What is SaaS (Software as a Service)

As many things, SaaS enters the world of business with the new paradigm of Industry and computing. In its simplest form Software as a Service (SaaS) is a software model, in which the provider’s service is hosted over internet and made available to users. The concept of cloud computing is crucial here to understand the way SaaS work and how to make better use of it. Specifically, a business-oriented application, provided through internet web browser, can be used from monitoring data to process information. Thus, although subtitle looks as if it promises to give an exact definition, the description of the SaaS is not clear-cut since the boundaries are limitless. Thanks to the cloud computing concept, it is easy to implement almost every situation. Since the same paradigm accommodates both machine learning and SaaS, the human-machine interaction got richer and quicker with the availability of provided service.

What is SaaS (Software as a Service)

The most difficult part of new data paradigm is to describe every concept separately; because of their interconnected nature, one’s benefit is another one’s implication prerequisite. As in the SaaS example, the machine learning, augmented learning, and cloud computing have almost same benefits with each other, although they are implemented differently. Long story short, having any of abovementioned tools in hand may help you substantially thanks to below listed benefits:

  • Cost: since the payment schemes are turned to subscriptions or monthly fees, the overall cost on the user is reduced heavily. In fact, instead of paying large amounts of money to initial systems and then to maintaining the same systems, SaaS providers sell you license which consist of both software and the maintenance service; thus, small businesses eliminated the financial risk of expensive software.
  • Small Learning Curve: as the name suggests, the time and effort you would put into a conventional software in order to properly use it, will be high. However, the SaaS saves you a lot of time since intrinsically they are designed to be easy-to-use.
  • Easy Access: one of the biggest innovations that comes with the SaaS is that it can be used from anywhere. That is, if you have a device that connects to internet, you can reach any information you want. Therefore, different than any other technology solution, SaaS can be mobilized according to needs of the user.

The SaaS has a lot more to offer; hence, in the not so long term we will see more web-based adaption of complex software as the machine power enhances and human needs are shifting the paradigm of data science. The crucial point here is to point out the drawbacks of the technology as it is not free from them. Nevertheless, despite of time of its life, it solves more than it seems to do.

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