Open In App

Six Steps of Data Analysis Process

Last Updated : 10 Jan, 2024
Like Article

Data analysis is the methodical exploration and interpretation of data, underpins decision-making in today’s dynamic landscape. As the demand for skilled Data Analysts grows, understanding the six key steps in this process becomes imperative. From defining problems to presenting insights, each step plays a vital role in transforming raw data into actionable knowledge.

In this article let’s delve into the six essential steps of data analysis, emphasizing the significance of each phase in extracting meaningful conclusions.

What is Data Analysis?

The collection, transformation, and organization of data to draw conclusions make predictions for the future and make informed data-driven decisions is called Data Analysis. The profession that handles data analysis is called a Data Analyst.

There is a huge demand for Data Analysts as the data is expanding rapidly nowadays. Data Analysis is used to find possible solutions for a business problem. The advantage of being a Data Analyst is that they can work in any field they love healthcare, agriculture, IT, finance, business. Data-driven decision-making is an important part of Data Analysis. It makes the analysis process much easier. There are six steps for Data Analysis.

Steps for Data Analysis Process


  1. Define the Problem or Research Question
  2. Collect Data
  3. Data Cleaning
  4. Analyzing the Data
  5. Data Visualization
  6. Presenting Data

Each step has its own process and tools to make overall conclusions based on the data. 

1. Define the Problem or Research Question

In the first step of process the data analyst is given a problem/business task. The analyst has to understand the task and the stakeholder’s expectations for the solution. A stakeholder is a person that has invested their money and resources to a project. The analyst must be able to ask different questions in order to find the right solution to their problem. The analyst has to find the root cause of the problem in order to fully understand the problem. The analyst must make sure that he/she doesn’t have any distractions while analyzing the problem. Communicate effectively with the stakeholders and other colleagues to completely understand what the underlying problem is. Questions to ask yourself for the Ask phase are: 

  • What are the problems that are being mentioned by my stakeholders?
  • What are their expectations for the solutions?

2. Collect Data

The second step is to Prepare or Collect the Data. This step includes collecting data and storing it for further analysis. The analyst has to collect the data based on the task given from multiple sources. The data has to be collected from various sources, internal or external sources. Internal data is the data available in the organization that you work for while external data is the data available in sources other than your organization. The data that is collected by an individual from their own resources is called first-party data. The data that is collected and sold is called second-party data. Data that is collected from outside sources is called third-party data. The common sources from where the data is collected are Interviews, Surveys, Feedback, Questionnaires. The collected data can be stored in a spreadsheet or SQL database. 

A spreadsheet is a digital worksheet that contains rows and columns while a database contains tables that have functions to manipulate the data. Spreadsheets are used to store some thousands or ten thousand of data while databases are used when there are too many rows to store. The best tools to store the data are MS Excel or Google Sheets in the case of Spreadsheets and there are so many databases like Oracle, Microsoft to store the data.

3. Data Cleaning 

The third step is Clean and Process Data. After the data is collected from multiple sources, it is time to clean the data. Clean data means data that is free from misspellings, redundancies, and irrelevance. Clean data largely depends on data integrity. There might be duplicate data or the data might not be in a format, therefore the unnecessary data is removed and cleaned. There are different functions provided by SQL and Excel to clean the data. This is one of the most important steps in Data Analysis as clean and formatted data helps in finding trends and solutions. The most important part of the Process phase is to check whether your data is biased or not. Bias is an act of favoring a particular group/community while ignoring the rest. Biasing is a big no-no as it might affect the overall data analysis. The data analyst must make sure to include every group while the data is being collected. 

4. Analyzing the Data

The fourth step is to Analyze. The cleaned data is used for analyzing and identifying trends. It also performs calculations and combines data for better results. The tools used for performing calculations are Excel or SQL. These tools provide in-built functions to perform calculations or sample code is written in SQL to perform calculations. Using Excel, we can create pivot tables and perform calculations while SQL creates temporary tables to perform calculations. Programming languages are another way of solving problems. They make it much easier to solve problems by providing packages. The most widely used programming languages for data analysis are R and Python.

5. Data Visualization

The fifth step is visualizing the data. Nothing is more compelling than a visualization. The data now transformed has to be made into a visual (chart, graph). The reason for making data visualizations is that there might be people, mostly stakeholders that are non-technical. Visualizations are made for a simple understanding of complex data. Tableau and Looker are the two popular tools used for compelling data visualizations. Tableau is a simple drag and drop tool that helps in creating compelling visualizations. Looker is a data viz tool that directly connects to the database and creates visualizations. Tableau and Looker are both equally used by data analysts for creating a visualization. R and Python have some packages that provide beautiful data visualizations. R has a package named ggplot which has a variety of data visualizations. A presentation is given based on the data findings. Sharing the insights with the team members and stakeholders will help in making better decisions. It helps in making more informed decisions and it leads to better outcomes. 

6. Presenting the Data

Presenting the data involves transforming raw information into a format that is easily comprehensible and meaningful for various stakeholders. This process encompasses the creation of visual representations, such as charts, graphs, and tables, to effectively communicate patterns, trends, and insights gleaned from the data analysis. The goal is to facilitate a clear understanding of complex information, making it accessible to both technical and non-technical audiences. Effective data presentation involves thoughtful selection of visualization techniques based on the nature of the data and the specific message intended. It goes beyond mere display to storytelling, where the presenter interprets the findings, emphasizes key points, and guides the audience through the narrative that the data unfolds. Whether through reports, presentations, or interactive dashboards, the art of presenting data involves balancing simplicity with depth, ensuring that the audience can easily grasp the significance of the information presented and use it for informed decision-making.


In conclusion, the data analysis processes the ability to distill complex information into clear, visual narratives empowers organizations to make informed decisions. Data-driven insights, effectively communicated, play a pivotal role in addressing business challenges and fostering continual improvement across various domains.

Frequently Asked Questions(FAQs)

1.What are the 5 methods of data analysis?

Descriptive, Inferential, Diagnostic, Predictive, and Prescriptive are five common methods used in data analysis to derive meaningful insights.

2.What are the 5 levels of data analysis?

Data collection, Data cleaning, Exploratory Data Analysis (EDA), Modeling, and Interpretation are the five levels involved in the data analysis process.

3.What are the 4 stages of data analysis?

Collection, Processing, Analysis, and Interpretation are the four key stages in the data analysis process, leading to informed decision-making and insights.

4.What are the 5 processes of data analysis?

Data collection, Data cleaning, Data analysis, Data interpretation, and Data presentation constitute the five fundamental processes in effective data analysis workflows.

5.What is the process of data analysis?

The process involves defining the problem, collecting and cleaning data, analyzing patterns, visualizing insights, and presenting findings, facilitating informed decision-making and problem resolution.

Like Article
Suggest improvement
Share your thoughts in the comments

Similar Reads