Exploratory analysis is a technique used BH data analytics such as detectives that involves figuring out what to make out of the data. It means exploring and looking for clues. It seeks to establish the underlying structure of the data as well as the critical variables of the data. Other aspects of the data that are of importance with the exploratory analysis are the identification of mistakes and missing data, identification of anomalies, and margins of error.  Others include hypothesis testing and examining assumptions as they relate to a particular model. With exploratory analysis, data analytics find out trends, and patterns from models using quantitative and visual methods (Bandalos and Finney 2018),. The information obtained from this analysis assists data analytics in determining the next step to take.

On the other hand, confirmatory is a technique used by data analytics to evaluate evidence. According to Bandalos and Finney (2018), confirmatory analysis involves the use of traditional statistical tools to assess evidence. Some of the commonly used such tools are significance, inference, and confidence. It means challenging the already set assumptions and the actual testing of the hypothesis. It is in the confirmatory analysis where data analytics put their findings and arguments to trial.

Reference

Bandalos, D. L., & Finney, S. J. (2018). Factor analysis: Exploratory and confirmatory. The          reviewer’s guide to quantitative methods in the social sciences (pp. 110-134). Routledge.

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