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Data analysis is the process in which data are inspected as they are cleaned, transformed and modeled with the goal of discovering useful information that can aid in making decisions. It can be done using various statistical and analytical techniques including descriptive analysis (descriptive stats like averages and proportions), cluster analysis, time-series analyses, as well as regression analysis.
It is essential to start with a clearly defined research question or objective in order to conduct an effective data analysis. This will ensure that the analysis is centered and can provide useful insights.
The next step in data collection is to determine a clear research objective or question. This can be done using internal tools, such as CRM software or business analytics software and internal reports or external sources like surveys and questionnaires.
This data is then cleaned by removing duplicates, anomalies, or other errors from the dataset. This is called “scrubbing” and can be done manually or using automated software.
The data is then summarized to be used in the analysis. This can be done by using a graph or table built from a series of observations or measurements. The tables can be one-dimensional or two-dimensional, and they are either numerical or categorical. Numerical data can be discrete or continuous. Categorical data can be either ordinal or nominal.
The data is then analyzed using various statistical and analytical techniques to solve the problem or reach the desired result. This is done by inspecting the data visually, conducting regression analysis, evaluating the hypothesis and then on. The results of the analysis are then used to interpret what actions will help to achieve the goals of an organization.