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Data analysis is the process in which data are examined, cleaned, transformed, and modeled with the aim of obtaining useful information that will aid in making decisions. It can be accomplished using different statistical and analytical techniques such as descriptive analysis (descriptive statistics like frequencies, averages, and proportions) and regression analysis. cluster analysis, and time-series analyses.

In order to conduct an effective analysis of data it is crucial to begin with a well-defined research question or objective. This will ensure the analysis is focused and can provide useful insights.

After a specific research goal or question is established the next step in data analysis is to gather the required data. This can be accomplished using internal tools like CRM software as well as business analysis software internal reports, and external sources like surveys and questionnaires.

The data is then cleaned to eliminate any anomalies, duplicates, or mistakes. This is known as “scrubbing” and can be done either manually or by using software that automates the process.

Data is then summarized to aid in analysis, which can be accomplished by constructing a table or graph based on a set of observations or measurements. These tables can be either one-dimensional or two-dimensional, and they can be either categorical or numerical. Numerical data can be discrete or continuous. Categorical data can be either ordinal or nominal.

The data is then processed using various analytical and statistical techniques to answer the research question or to address the goal. This can be accomplished by visualizing the data and performing regression analysis, testing the hypothesis and the list goes on. The results of data analysis are used to determine what actions are in line with the objectives of an organization.