Data Analysis Process

What is Data Analysis?

Data analysis is the art of collecting, inspecting, cleaning, transforming, and modeling raw data to draw conclusions and make insightful decisions. It involves various techniques and programming languages, such as Python and SQL. Modernization and access to raw data have challenged organizations and companies to utilize data analysis to make informed decisions, cut operation costs, predict sales, and increase revenue.

In data analysis, various crucial stages are involved to guarantee accuracy and useful results. This article outlines the different stages of data analysis, starting from defining the business question to the final stage, which is presenting your findings.

1. Defining the Business Question

In data analysis, the first step is to define the objective and outline specific questions you aim to answer in your analysis. At this stage, you must comprehend the problem, identify the data needed, and define the appropriate metrics to measure the outcome.
For example, when analyzing data from Amazon, you can set the objective as: What is the seasonal pattern on sales? or What are the top-selling products on Amazon?

2. Data Collection

After defining the objective, it is time to collect the relevant data. This can be done by observing, conducting interviews or surveys, or extracting from existing databases such as Kaggle. Depending on the nature of your objectives, the data collected can be qualitative (non-numerical) or quantitative (numerical).

3. Data Cleaning

Data cleaning, also known as data cleansing, involves checking, removing, and correcting inconsistencies and errors in the data. By doing this, you are increasing the quality and reliability of the data, which is vital in obtaining meaningful results from the data.

4. Exploring Data

Once all the errors and inconsistencies have been eliminated from the data, it is time to explore or analyze the data. It involves applying various statistical and mathematical techniques to unearth patterns, trends, and relationships. Additionally, various software such as Python, R, Excel, and SPSS are used at this stage.

5. Data Interpretation and Visualization

After exploring and analyzing the data, it is time to interpret and visualize your findings in a way that can be easily understood by other stakeholders. This can be done by generating charts, graphs, or any other visualizing technique. For data analysis with Python, libraries such as Matplotlib and Seaborn can be used for data visualization.

6. Data Storytelling

This is the final stage of data analysis, and it involves presenting and narrating the findings of your data analysis in a manner that is easily understood by other stakeholders. Moreover, as a data analyst, you must be equipped with good communication skills to outlay your findings to the non-technical team and to deliver data-driven decisions.

Conclusion

The data analysis process requires a lot of keenness to generate insightful findings and make the correct decisions. With the help of data analysts, most companies rely on this process to plan, forecast, and strategize for the future.