Data analysis is a process of examining, cleaning, transforming and modeling data with the aim of finding useful information, informing conclusions and supporting decision making.
Data analysis itself has many facets and approaches that cover a variety of techniques under different names, and are used in different business, science and social science domains. In today’s business world, data analysis plays a role in making more scientific decisions and helping businesses operate more effectively because data is more accurate and real-time.
Data mining is a specific data analysis technique that focuses on statistical modeling and knowledge discovery for predictive purposes rather than purely descriptive purposes. While business intelligence includes data analysis that relies heavily on aggregation, with a focus primarily on business information.
In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses more on finding new features in the data while CDA focuses more on confirming or falsifying existing hypotheses.
Predictive analysis focuses on applying statistical models to forecasting or predictive classification, whereas text analysis applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, unstructured data species.
All of the above are the types of data analysis that exist today. Data integration is a precursor to data analysis, and data analysis is closely related to data visualization and data dissemination.
Get to know what data analysis is
Data analysis is defined as the process of cleaning, transforming, and modeling data to find useful information for business decision making. The purpose of data analysis is to extract useful information from the data and make decisions based on data analysis.
Whenever we make a decision in our daily lives it is to think about what happened last time or what will happen by choosing that decision. It is nothing but analyzing our past or our future and making decisions based on it. For that, we collect past memories or dreams of our future. So it is nothing but data analysis. Now the same thing analysts do for business purposes, it’s called Data Analytics.
Types of Data Analysis
In a study, there are several types of data analysis. The two types of analysis are qualitative analysis and quantitative analysis. Below is an explanation of the two types of analysis:
Is an analysis of data obtained by a systematic process. Namely, by searching for and processing various data sourced from field observations, document studies, field notes, interviews, documentation, and others so as to produce a report on research findings.
This data analysis itself can be done by organizing the data into a category, synthesizing it, breaking it down into units, arranging it into patterns, choosing which ones are important and which ones will be studied, then making conclusions that are easily understood by everyone.
Is a type of analysis that uses tools with a quantitative nature. This means that an analysis is carried out using certain models. Like mathematical models, econometric models, statistical models, and so on. Then, the results of this type of analysis will later be presented in the form of figures that are interpreted or explained in a description.
In this type of research itself, data analysis can also be obtained. That is, an activity that is carried out after data from all other sources / respondents have been collected. These activities include:
a) Grouping data according to the type and variable of the respondent
b) Tabulating data according to the variables of all respondents
c) Presenting data on each variable that has been studied
d) Calculating data to answer the formulation of the problem made
e) Calculating also a data so that the proposed hypothesis is tested
In the technical analysis of quantitative data types that use the statistical model itself, there are two types of divisions that you need to know, namely:
a) Descriptive statistical data analysis
This technique is used to analyze the data by describing or describing the data that has been collected without generalizing the results of the research. Examples include presenting data in the form of diagrams, tables, modes, mean, frequency, percentage, and others.
b) Analysis of inferential statistical data
Namely one of the techniques used in analyzing data by making a general conclusion. What is characteristic of this data analysis technique is inferential. This means using certain statistical formulas, then the results of the calculations carried out are used as the basis for making generalizations or general conclusions.
In this case, inferential statistics can be useful in generalizing the results of a study. Not surprisingly, this inferential statistical technique is very useful for sample research.
Example of Data Analysis
In order for you to get a clearer picture of data analysis, it is very important to know what examples of data analysis are. What are some examples of data analysis? You can find an example of what data analysis itself is in the external audit services of financial companies. These workers will generally provide audit services for financial records as well as regulatory compliance operational procedures.
These workers do their job by analyzing the data. Namely, starting with conducting direct interviews, taking samples, documenting, and so on. Of course, in order to obtain high-quality data analysis results one needs to carry out an analysis with high qualifications and a sharp level of analytical skills. In this case, the analysis activity is carried out by researching in depth the sources. Whether it’s through interviews, photo and video documentation, and data collection.
Businesses today need every advantage and advantage they can get. Thanks to barriers such as rapidly changing markets, economic uncertainty, a changing political landscape, finicky consumer attitudes, and even the global pandemic, businesses today are working with a smaller margin of error. Companies looking to not only stay in business but also thrive can increase their chances of success by making smart choices.
So how does a person or organization make this choice? They do this by gathering as much useful and actionable information as possible and then using it to make more informed decisions. This strategy makes sense, and it applies to both personal and business life. No one makes important decisions without first figuring out what is at stake, the pros and cons, and the possible outcomes.
Likewise, no company that wants to be successful should make decisions based on ignorance. Organizations need information; they need data. The need for data is what causes the discipline of data analysis to enter the picture. This article is your primer on data analysis, what the phrases mean, available types and processes, popular data analysis methods, and how to perform data analysis.