Data analysis is the process of analyzing the, cleaning, transforming and modeling data with the intention of obtaining valuable information and assisting in the process of making decisions. It can be accomplished using various statistical and analytical techniques, including descriptive analysis (descriptive statistics such as averages, frequencies, and proportions), regression analysis, cluster analysis, as well as time-series analysis.
To conduct a successful data analysis it is important to begin with a clearly defined research question or objective. This will ensure that the analysis is centered and can provide valuable insights.
The next step in data collection is to define an objective of research that is clear or a question. This can be done with internal tools like CRM software and business analytics software and internal reports, or external sources such as surveys and questionnaires.
The data is then cleaned to eliminate any anomalies, duplicates or errors. This is referred to as “scrubbing” the data and can be done manually or by using software that is automated.
The data is 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 one-dimensional or two-dimensional and may be categorical or numerical. Numerical data is characterized as discrete or continuous, and categorical data is classified as nominal or ordinal.
Finally, the data are processed using various methods of analysis and statistics to answer the research question or answer the aim. This can be done by examining the data visually or performing regression analysis or testing hypotheses and so on. The results of the data analysis are then interpreted to understand what actions can be taken to achieve the goals of the company.