Data analysis is the process of analyzing data, cleaning, changing, and modeling data in the hopes of discovering useful information that can aid in the process of making decisions. It can be accomplished with different statistical and analytical techniques including descriptive analysis (descriptive stats such as proportions and averages) and cluster analysis. time-series analyses, as well as regression analysis.
It is essential to start with an explicit research question or objective in order to carry out a thorough analysis of data. This will ensure that the analysis is focused on the relevant aspects and can provide actionable insights.
The next step in collecting data is to define 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 with external sources like surveys and questionnaires.
The data is then cleaned to remove any duplicates, anomalies, or errors. This is referred to as „scrubbing“ and can be done manually or through automated software.
The data is then summarized to be used in the analysis. This can be accomplished with a graph or table made from a series of observations or measurements. These tables can be two-dimensional or one-dimensional and may be numerical or categorical. Numerical data is characterized as discrete or continuous, and categorical data is classified as nominal or ordinal.
Finally, the data are analyzed by using various methods of analysis and statistics to solve the research question or answer the purpose. This can be accomplished by examining the data visually and performing regression analyses, testing the hypotheses, etc. The results of the analysis are then utilized to determine what actions can support the goals of an organization.
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