Data analysis is the process of analyzing, cleaning, transforming, and modeling data with the aim of gaining useful information that can aid in decision-making. It can be done with various statistical and analytical methods, such as descriptive analysis (descriptive statistics such as averages and proportions) cluster analysis, time-series analyses, and regression analysis.
For a successful data analysis, it’s important to start with a clearly defined research question or objective. This will ensure the analysis is focused and will provide valuable insights.
After a specific research question or goal is established, the next step in data analysis is to gather the necessary data. This page can be done using internal tools like CRM software or business analysis software, internal reports, and external sources such as questionnaires and surveys.
The data is then cleaned to remove any anomalies, duplicates, or errors. This is called “scrubbing” and can be done manually or with automated software.
Data is then compiled for use in the analysis, which can be done by constructing a tables or graph from a series of measurements or observations. 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.
The data is then examined using a variety of statistical and analytical techniques to solve the research question or to address the purpose. This can be done by visualizing the data or by conducting regression analysis, evaluating the hypothesis, and then on. The results of the analysis are used to determine which actions are able to support the goals of an organization.
