Data analysis is an exercise in assessment of data. Since the range of what we refer to as “data” is so vast, the possibilities of data analysis are also limitless. Data analysis is a crucial step of most research projects which involve the assimilation of large quantities of data. It can include any kind of data, and can be subdivided into two categories: qualitative and quantitative data analysis. They are differentiated as follows:
- Qualitative data analysis: Involves the non-numerical data that has to be analyzed in order to reach certain conclusions or unearth certain trends. The data is often in textual form and it is harder to disseminate.
- Quantitative data analysis: Involves the use of numerical data and reaching conclusions that the data reflects.
Date analysis is a formal exercise and hence it must be done using standard analytical tools. It must be comprehensive and to the point. The results of the data analysis should be highlighted in the report that is tabulated. This should be done carefully so that the conclusions reached can be used in a beneficial manner. The advantages of data analysis are manifold. Some important ways in which they are useful are as follows:
- Data analysis can be an effective way of unearthing trends and tendencies. For example, the survey of shopping habits of people across a broad spectrum of class and background can throw up interesting conclusions.
- Data analysis is a tool which will also come useful later on. It is extremely beneficial in all cases of research and must be acquired at the earliest.
Data analysis is an inevitable tool of research of any kind, whether it is market surveys or migration surveys. All kinds of research involve data that must be processed and selectively retained to make sense and a logical, coherent picture.