Data analysis is the process of analyzing data, cleaning, changing, and modeling data in the hopes of discovering valuable information and assisting in decision-making. It can be accomplished with different statistical and analytical techniques, such as descriptive analysis (descriptive statistics such as averages and proportions) as well as cluster analysis, time-series analyses, and regression analysis.

In order to conduct an effective analysis of data, it’s important to start with a clear research question or objective. This will ensure the analysis is focused and can provide useful insights.

When a clear research goal or question is established, the next step in data analysis is to gather the necessary data. This can be done with internal tools, such as CRM software, business analytics software, and internal reports, as well as external sources like surveys and questionnaires.

The data is later cleaned by removing duplicates, anomalies or other errors that may exist in the dataset. This is referred to as «scrubbing» the data. This can be done manually, or using automated software.

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

The data is then evaluated using a variety of statistical and analytical techniques to answer the question or meet the objective. This can be accomplished by examining the data visually, doing regression analysis, testing the hypothesis and the list goes on. The results of the data analysis are then interpreted to determine what actions should be taken to help achieve the goals of the organization.

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