Once network data has been collected, the next crucial step is to analyse the data. Data analysis is a process used to inspect, clean, transform and remodel data with a view to reach to a certain conclusion for a given situation. Data analysis is typically of two kinds: qualitative or quantitative. The type of data dictates the method of analysis. In qualitative research, any non-numerical data like text or individual words are analysed. Quantitative analysis, on the other hand, focuses on measurement of the data and can use statistics to help reveal results and conclusions. The results are numerical. In some cases, both forms of analysis are used hand in hand. For example, quantitative analysis can help prove qualitative conclusions.
Among the many benefits of data analysis, the more important ones are:
- Data analysis helps in structuring the findings from different sources of data.
- Data analysis is very helpful in breaking a macro problem into micro parts.
- Data analysis acts like a filter when it comes to acquiring meaningful insights out of huge data set.
- Data analysis helps in keeping human bias away from the conclusion with the help of proper network problem resolution.
When discussing data analysis it is important to mention that a methodology to analyse data needs to be picked. If a specific methodology is not selected data can neither be collected nor analyzed.
The methodology should be present in the dissertation as it enables the reader to understand which methods have been used during the research and what type of data has been collected and analyzed throughout the process.
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