Any statistical software system can either be owned by an individual or a company. Such a system might be in place at present or it may have been developed over time. Such systems are usually used to create a logical framework for developing applications or to complete an assessment of data.
In order to perform the statistical analysis in an enterprise the following needs to be considered: the standard output, input of data, reference sources and the related assumptions about the overall set of data that are performed by the analysts. Furthermore, the actual outcome of the statistical analysis has to be determined and if applicable, the results that will be generated should be interpreted in a meaningful way.
Understanding the technical details is the first step to a successful performance of the statistical analysis. The sources of data can be varied. It could be the performance of various software, hardware or network in an enterprise, or the internal activities in the same.
There are different criteria on how they should be grouped and analyzed. Some may include the value-added efficiency, maintenance cost reduction, economic efficiency, reliability, availability and security. The underlying methodology used to collect the data is also important.
Data collection should start by collecting all the types of data that are relevant to the statistical analysis of the enterprise. This will ensure that the appropriate types of data are used for the analysis and that the categories are appropriate to each other.
The outcome is then measured and the results are analyzed ina training sessions. These training sessions can also provide a basis for the subsequent analysis. For instance, in case of the training session on statistical analysis, the findings from the training sessions are used to generate and compare statistics.
The statistical analysis of a complex enterprise could require more time than the average, thus time is also another factor that needs to be considered. The solution to this could be to use the existing systems already in place and build off that base.
Therefore, the technical details must be considered when designing the software. The actual quality of the software will be influenced by the level of collaboration between the analyst and the developer. The reliance on outside experts will only help to increase the accuracy of the software and it will decrease the need for constant changes.
Continuous improvement can be used to maintain consistency in a systematic approach. Several other factors that should be considered while designing the software includes ensuring that it is compatible with existing software in the enterprise. It is essential to ensure that the application is error free.
An example would be to use the same tool in a large scale application that is currently being used to handle the input data. The data cannot be derived out of the database without damaging the existing systems. A version of the analytical software that is compatible with the data will avoid unnecessary destruction of data.
It is imperative to take a large scale application into consideration when designing a statistical application. This is because data must be collected quickly. The time that is spent searching for the right statistical technique or analyzing the data becomes more time that is not spent on the actual business.