Exploratory data analysis is a tool that can be used by businesses to discover trends and insights that can strengthen key strategic decisions. Let’s explore this process in more detail to understand whether it’s right for you and your business.
What Is Exploratory Data Analysis?
This data analytics process is designed to help you understand the characteristics of the data in front of you to give greater depth to the insights you’re able to extract. Exploratory data analysis often allows for the discovery of useful patterns and trends, which can be crucial to decision-making processes that will impact your business moving forwards.
What Does Exploratory Data Analysis Look Like in Practice?
There are a number of steps involved in the exploratory data analysis process. Firstly, it is essential to identify the goals you want to achieve through the analysis you’re about to complete.
Data collection is the next step, which may involve pulling in data from a variety of different sources. When the data has been collected, the data-cleaning process will enable you to remove duplicates, make corrections to data types, address inconsistencies and appropriately deal with any missing values.
During the data-transformation stage of the process, you can standardise the data if required and aggregate data if your analysis needs depend on this. From here, you can begin the data-exploration process, which involves conducting relevant analysis that will inform the insights you’re able to obtain.
Working with a data analysis company such as https://shepper.com/ can simplify the process for your business, enabling you to ensure you’re getting the most value to strengthen your decision-making processes.
Do I Need to Leverage Exploratory Data Analysis?
Exploratory data analysis can provide strong foundations when it comes to understanding and accurately interpreting even the most complex sets of data. This type of data analysis can help you to identify patterns, develop and refine hypotheses and locate anomalies.
It can also help to understand the relationships that exist between several variables and highlight errors that may otherwise unnecessarily skew the insights you gain from your data.