Data analysis is the process of analyzing data, cleaning, changing, and modeling data with the intention of discovering useful information that can aid in decision-making. It can be accomplished using various statistical and analytical methods including descriptive analysis (descriptive statistics such as frequency, averages, and proportions) Regression analysis, cluster analysis, and time-series analysis.
It is crucial to begin with an explicit research question or goal in order to conduct an effective data analysis. This will ensure that the analysis is focused on what’s important and will provide useful insights.
The next step in data collection is to determine an objective of research that is clear or a question. This can be accomplished using internal tools like CRM software, business analysis software, internal reports, as well as external sources like surveys and questionnaires.
The data is later cleaned by removing duplicates, anomalies, or other mistakes in the data. This is referred to as “scrubbing” the data. This can be done manually, or using automated software.
The data is then compiled to be used in the analysis. This can be accomplished using a table or graph built from a series of observations or measurements. These tables can be one-dimensional or two-dimensional and can be numerical or categorical. Numerical data may be continuous or discrete. Categorical data may be ordinal or nominal.
The data is then evaluated using various statistical and analytical techniques to answer the question or achieve the desired result. This can be done by examining the data visually, performing regression analyses, testing the hypotheses, and so on. The results of data analysis are used to determine which actions are in line with the objectives of an organization.
