Data analysis focuses on uncovering knowledge for predictive and descriptive purposes, sometimes uncovering new trends and sometimes to confirm or disprove existing ideas. In this article, I will walk you through what is data analysis and the entire data analysis process.
What is Data Analysis?
Data Analysis is the procedure of first of all setting goals as to what data you need and what questions you’re hoping it will answer, then collecting the information, then inspecting and interpreting the data, to sort out the useful bits, to suggest conclusions and help with decision making by various users.
The business intelligence requirements may be different for each business, but the majority of the outlined steps are similar for most of the tasks in the data analysis process:
- Obtaining: data must be obtained before any other activity can be carried out
- Scrubbing: the next step is to clean, format and reorganize the data to facilitate analysis and model training.
- Exploring: An exploration of the dataset using descriptive statistics and visualizations. The task is to find any existing statistical significance and test the hypotheses. Based on the newly acquired information, we perform feature extraction to determine which variables are suitable for predictive analysis.
- Interpretation: What insights or meaningful information can we get from the dataset? Is our choice of a predictive model sufficient? What business questions can we answer with this data?
There are several methods that you can use for data analysis, for example, data mining, business intelligence, data visualization, or exploratory analysis. The latter is a means by which sets of information are analyzed to determine their distinct characteristics. That way, the data can finally be used to test your original hypothesis.
Any method can be used, as long as it helps the researcher to examine the information that has been collected, to derive meaning from it, to look for patterns and relationships, and to help answer to your questions.
The analysis part of the overall process takes a lot of work. Statistics should be compared and contrasted, looking for similarities and differences. Different researchers prefer different methods. Some prefer to use the software as the primary means of analyzing data, while others use software simply as a tool to organize and manage information and use a programming language like Python to analyze the data.
Hopefully, from here you can see how vital collection and analysis is to the well-being of your business and how it can help in all areas of your business, from customer service to employee relations, manufacturing and marketing of products.
Hope you liked this article on what data analysis is. Please feel free to ask your valuable questions in the comments section below.