Data analysis is the process of examining the, cleaning, transforming and modeling data with the goal of gaining useful information that can aid in the process of making decisions. It can be done using various statistical and analytical methods, such as descriptive analysis (descriptive stats like proportions and averages), cluster analysis, time-series analyses, and regression analysis.

It is crucial to start with an explicit research question or goal in order to conduct a successful analysis of data. This will ensure that the analysis is focused on what’s important and will provide useful insights.

Once a clear research question or goal is determined the next step in data analysis is to gather the required information. This can be read this post here done using internal tools like CRM software, business analysis software, internal reports, as well as external sources like surveys and questionnaires.

The data is then cleaned to remove any duplicates, anomalies, or errors. This is known as “scrubbing” and can be done either manually or through automated software.

Data is then compiled for use in the analysis. This is done by creating a table or graph based on a set of observations or measurements. These tables may be one-dimensional or two-dimensional, and they can be categorical or numerical. Numerical data is described as continuous or discrete, and categorical data is classified as nominal or ordinal.

The data is then analyzed by using a variety of statistical and analytic techniques to answer the question or achieve the objective. This is done by visualizing the data as well as performing regression analysis, testing the hypothesis and then on. The results of the analysis are then used to interpret what actions are in line with the objectives of the organization.