Data analysis is the process of inspecting and cleaning, transforming, and modeling data with the aim of uncovering useful information and supporting decision-making. It can be carried out using various statistical and analytical methods including descriptive analysis (descriptive statistics like frequencies, averages, and proportions) and regression analysis. cluster analysis, and time-series analysis.
It is essential 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 yield actionable insights.
The next step in data collection is to define a clear research objective or question. This can be done using internal tools, such as CRM software and business analysis software internal reports, as well as external sources such as surveys and questionnaires.
This data is then cleaned by removing any duplicates, anomalies or other errors from the dataset. This is referred to as “scrubbing” the data and can be done manually, or using software that is automated.
Data is then summarized to be used in the analysis, which can be done by constructing a tables or graph using a series of observations or measurements. These tables can analyze a conglomerate merger be one-dimensional or two-dimensional, and are either numerical or categorical. Numerical data can be discrete or continuous. Categorical information can be ordinal or nominal.
The data is then analyzed using a variety of statistical and analytical techniques to determine the answer or to achieve the desired result. This can be achieved by examining the data visually as well as performing regression analyses or testing hypotheses and so on. The results of data analysis are then used to determine what actions are in line with the objectives of an organization.