SaaS stand for Software as a service. It is a data analysis and reports producing software system. SaaS is a collection of software tools that work together to save and retrieve data, edit data, perform simple and advanced statistical analyses, and generate reports.
SaaS is becoming more apparent with the progress of this technology as it gains more applications. First developed by IBM, the first full implementation of SaaS into an enterprise environment was in 1990. Since then, there have been many developments that make SaaS more effective for statistical analysis for not only business intelligence but also for other industries like healthcare, finance, Ecommerce, etc.
Benefits of SaaS
As far as the benefits of SaaS are concerned, they boil down to three:
- it allows you to write code,
- it allows you to do parallel data analysis
- it allows you to save and restore your work on a server.
It is also worth noting that with the development of various programs for machine learning and artificial intelligence, the ability to write code is getting better. This makes the decision of what to do next much easier for researchers. Another thing that comes from SaaS is the ability to run applications from a server. This means that you do not have to buy and maintain a server just to be able to do statistical analysis on your data files on a server.
Advantage of SaaS
One of the main reasons why SaaS is so successful with data mining is because it allows users to conduct their analyses and create their workflows. Another advantage of SaaS machine learning and AI is that it allows users to conduct a wide range of statistical analyses and visualizations. One of the downsides is that it does not allow users to directly manipulate real physical systems. The main drawback of SaaS data mining is that it does not allow users to directly manipulate real physical systems.
Disadvantages of SaaS
There are also two disadvantages when it comes to using statistical data analysis software like SaaS. The first disadvantage includes the cost factor involved in buying the machine. If you are running a small research operation then there is no need for you to spend thousands of dollars on purchasing a machine.
However, if you are a medium-sized or large organization that is planning to do lots of statistical analysis on large data sets, then purchasing a machine would be a worthy investment. It should be noted that SaaS comes in various forms, which is another reason why it is quite expensive compared to other types of statistical machine learning software.
The second disadvantage associated with SaaS is its inability to directly manipulate actual physical systems. Most machine learning and AI packages in the market today come with pre-defined procedures and commands that you can easily extend. You will be able to write an automated function that performs the same task as what you want to be done.
However, with statistical analysis software like SaaS, the only thing you can do is define how to collect the data files and save them into a particular format. This feature of SaaS makes it unsuitable for tasks such as live trading where you need to manipulate and change the price of the stocks depending on the present stock price.
Another disadvantage associated with this software is that it uses high-speed serial drives as a means of storing data. Since these drives are slow in response time, it could result in a data loss which is a costly affair if it happened on a critical data file. In addition, there are chances of overwriting some files especially when multiple users are using the same application on the same computer or in a system of shared server using UNIX/ Linux platforms.
There are also certain disadvantages inherent in using this technology apart from the fact that it cannot be used to directly manipulate physical devices such as drives. In the case of high-performance computing devices such as servers, it is not possible to use only one type of drive. This is because the number of drives must be proportional to the number of servers in a cluster or a system. Similarly, it is not possible to use only fiber channel based drives because the speed of these drives is so high that it is very impractical.
Several companies are offering various types of storage subsystems for a specific application. These include TQM, Person Systems, Sun Microsystems, IBM and Sycon. Each company has its strengths and weaknesses. To choose the most appropriate company, one must take into consideration the needs of the organization as well as its compatibility with other data processing and storage systems.
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