Data analysis is the process of evaluating data using analytical and logical reasoning to examine each piece of the data provided. This analysis method is merely one of several steps that need to be completed when performing a research experiment.
Data from multiple sources is compiled, examined and then analyzed to construct some form of discovery or conclusion. There are multiple data analysis methods, including data mining, text analytics, business intelligence and data visualizations. Two common data analysis techniques are qualitative and quantitative methods.
Data analysis in qualitative research may involve statistical procedures and can be a continuing process where data is simultaneously collected and analyzed. Qualitative data offers a rich, comprehensive image to be built up about why individuals act in certain ways and their feelings about these actions. It is necessary to be conscious of the benefits and drawbacks of qualitative data analysis as it may impact an organization’s choice of data collection.
A quantitative approach is generally concerned with discovering proof to either support or contradict an idea or hypothesis. A hypothesis is when an anticipated answer to a research question is proposed. For instance, one might propose that if you provide an employee training on how to best use social media marketing, it will improve their success in placing effective social media advertisements.
Data analysis helps organizations react to change and provides the ability to make faster, more informed business decisions, backed up by facts. Additionally, it offers a deeper understanding of customer requirements which, in turn, builds better business relationships.