Data analysis is the process in which data are examined and cleaned, transformed and modeled with the goal of identifying useful information that can aid in making decisions. It can be accomplished using various analytical and statistical techniques, including descriptive analysis (descriptive statistics like frequency, averages, and proportions), regression analysis, cluster analysis, as well as time-series analysis.
To conduct a successful data analysis it is essential to begin with a clearly defined research issue or objective. This will ensure that the analysis is focused and can provide useful insights.
The next step in collecting data is to identify a clear research objective or question. This can be done using internal tools such as CRM software as well as business analysis software internal reports, as well as external sources like surveys and questionnaires.
The data is then cleaned to remove any anomalies, duplicates, or mistakes. This is called “scrubbing” and can be done manually or with automated software.
The data is then compiled to be used in analysis. This can be done using a table or graph created from a sequence of observations or measurements. These tables can be either one-dimensional or two-dimensional and can be either numerical or categorical. Numerical data is classified as continuous or discrete, and categorical data is classified as ordinal or Clicking Here nominal.
The data is then evaluated using a variety of statistical and analytical techniques to determine the answer or to achieve the goal. This is done by examining the data visually and conducting regression analysis, evaluating the hypothesis and the list goes on. The results of the data analysis are then used to determine the best course of action that can be taken to achieve the objectives of the company.