Like any other scientific profession, data analysis follows a strict step-by-step process. Each step requires a unique skill set and knowledge. In order to gain fruitful outcomes, it is necessary to comprehend the entire process. A solid foundation is essential for developing results that can withstand examination. Therefore, we will go through the most basic steps in the data analysis process. Also, we will understand how to identify your aim, collect data, and conduct an analysis in this article.

Data analysis: Meaning

Although several people, companies, and specialists approach data analysis in their own way, we can reduce most of them into a one-size-fits-all description. Thus, data analysis is the process of cleansing, transforming, and analyzing raw data in order to obtain usable, relevant information that aids organizations in making educated decisions. The technique involved in data analysis reduces the risks associated with decision-making by offering relevant insights and data, which you can frequently display in charts, graphics, tables, and graphs. Also, to know more about data analysis and become proficient in this technological process, we suggest you enroll in the Data Analysis Online Course, which will help you move forward to start your career in this domain.

Necessary Steps of Data Analysis Process

List down below are the crucial steps of the data analysis process:

●     Defining the question

In this data analysis process, the first stage is to establish the goal. You can refer to it as the 'issue statement' in the data analytics parlance. Defining your aim involves developing a hypothesis and determining how to test it. So, begin by asking yourself; What business problem am I attempting to solve? While this may appear to be a simple task, it might be difficult. For example, your company's top management may ask, "Why are we losing customers?" However, it is possible that this may not fix the underlying issue. The responsibility of a data analyst is to understand the business and its goals thoroughly to describe the problem correctly.

●     Collect the Data

Once you know your goal, you must devise a plan for gathering and aggregating the necessary data. However, a critical component of this is deciding the data you require. This might include quantitative (numerical) data, such as sales numbers, or qualitative (descriptive) data, such as customer feedback. Moreover, you may classify all data into first-party, second-party, or third-party data.

●     Data Cleaning

After you've gathered your data, the next step is to prepare it for analysis. This involves cleaning or scrubbing,' and is crucial in ensuring that you are working with high-quality data. Among the most significant data cleansing tasks are:

  1. Reducing severe mistakes, duplication, and outliers, which become unavoidable when combining data from many sources.
  2. It also includes eliminating undesirable data points, i.e., extracting irrelevant observations that do not influence the analysis.
  3. Developing structure to your data, such as correcting typos or layout flaws, will allow you to map and manage your data more readily.
  4. At last, this process comprises filling up the large gaps.

●     Analyzing the data

Now comes the exciting and last part; analyzing it! However, the data analysis you perform determines your purpose. Some examples are univariate or bivariate analysis, time series, and regression analysis. What matters more than the different varieties is how you use them. It depends on the insights you want. In general, all data analysis methods fall into one of the four categories listed below:

  1. Descriptive Analysis
  2. Diagnostic Analysis
  3. Predictive Analysis
  4. Prescriptive Analysis

●     Sharing the Results

Till now, you've completed your investigations and got your ideas. The final phase in the data analysis process is communicating these results with the rest of the world. This is more complex than sharing the raw results of your work. It entails analyzing the data and presenting it in a way that audiences can understand. Also, ensure that the outcomes are unambiguous. As a result, data analysts frequently employ reports, dashboards, and interactive visualizations to back up their conclusions.


Hopefully, you may find this article informative. We have compiled an easy way to perform data analysis. Every individual working in this domain performs these critical steps daily. So, if you are also looking to enter this industry, it is necessary to go through these steps or enroll in the Data Analysis Training in Delhi.