Data analysis is a procedure of collecting and analyzing raw data by interpreting the inference out of raw data. Data analysis is one of the important aspects of the analyst’s work. Data analysis plays a crucial role in deciding whether or not the retrieved data is reliable.
Data analysis is basically a two-step procedure that involves collecting and analyzing data. Data analysis can be explained with the help of the following example:
Suppose a researcher has conducted a survey in order to know if the manufacturing of auto parts in an auto industry is more in Pune or in Chennai. The first step of data analysis is to collect the data through primary or secondary research. The next step of data analysis is to make an inference about the collected data. The second step of data analysis in this case will involve SWOT Analysis. SWOT Analysis stands for Strength, Weakness, Opportunity and Threat of the data under study.
Primary research in data analysis is the one that involves collection of data through questionnaires or telephone interviews. Secondary research in data analysis is the one that involves collection of data using the internet.
There are basically two types of data analysis. These two types are as follows:
Qualitative data analysis: This kind of data analysis is the one that consists of an unstructured, exploratory research methodology based on small samples intended to provide an insight into the problem being solved.
Quantitative data analysis: On the other hand, this kind of data analysis seeks to quantify the data and typically involves some form of statistical data analysis.
Quantitative data analysis can be performed in those cases when one needs to get statistical inferences about the data. In such cases, data analysis is done by using some statistical techniques. These statistical techniques include Factor Analysis, Discriminate Analysis, etc.
A technical analyst performs data analysis by interpreting the charts using a time series technique, and he/she forecasts the price trends of a particular commodity or share. Thus, data analysis can be used to forecast about the data as well.
Data analysis is an integral part of every research work. The validity of data can be known only through data analysis.
In statistics, data analysis is done on quantitative data. Data analysis in relation to quantitative data analysis can be divided into descriptive statistics, exploratory data analysis and confirmatory data analysis.
Descriptive Statistics in data analysis involves techniques like mean, median, mode, variance, standard deviation, etc.
Exploratory data analysis involves the following steps:
· Formulation of a problem in data analysis.
· Identifying alternative courses of action in data analysis.
· Developing hypotheses in data analysis.
· Isolating key variables and relationships for further examination in data analysis.
· Gaining insights for developing an approach to the formulated problem in data analysis.
Sometimes, qualitative data analysis is undertaken to explain the findings obtained from quantitative data analysis. Thus, one can say that both qualitative data analysis and quantitative data analysis are interrelated with each other.
Data analysis is also synonymous to data modeling. Data modeling is a process in which a perfect model (which represents the data as a whole) is being fitted during the data analysis.
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