Data analysis is the process of analyzing data, cleaning, changing, and modeling data with the intention of discovering valuable information and assisting in decision-making. It can be done using various statistical and analytical methods that include descriptive analysis (descriptive statistics like frequencies, averages, and proportions) Regression analysis, cluster analysis, and time-series analyses.
It is essential to start with a clearly defined research question or objective in order to perform a reliable analysis of data. This will ensure that the analysis is focused on what’s important and will provide useful insights.
After a specific research question or goal 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, as well as external sources such as surveys and questionnaires.
The data is then cleaned to eliminate any duplicates, anomalies, or mistakes. This is referred to as “scrubbing” and can be done either manually or with automated software.
Data is then compiled to aid in analysis. This is done by constructing a table or graph from a sequence of observations or measurements. These tables can be two-dimensional or one-dimensional and can be numerical or categorical. Numerical data may be discrete or continuous. Categorical information can be ordinal or nominal.
Finally, the data is processed using various methods of analysis and statistical to solve the research question or address the purpose. This is accomplished by visually inspecting the data and performing regression analyses or testing hypotheses and so on. The results of data analysis are then used to determine which actions are in line with the objectives of an organization.
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