Exploration is the initial step of data preparation and a means of getting to know raw data sets before working with them. Large data sets are prepared for deeper analysis through surveys and investigation. Data exploration allows for a deeper understanding of raw data and makes it easier to navigate and use the data later. The more thoroughly data analysts understand data, the better their data analysis will be. Successful data exploration shows new opportunities and helps identify future analytics questions and problems. Take a look at how data exploration can benefit business users.
How Data Exploration Works
Data is a business intelligence tool that serves two purposes: to provide information and to answer a question. The right questions and exploration applied to data can provide deeper insights into how things work and enable predictive abilities. The most common programming languages used for exploration are R, which is best for statistical learning, and Python, which is best for machine learning.
There are three main steps in the data exploration process. The first step is to understand the variables for data analysis. Review the column names of data sets and take a closer look at data catalogs, field descriptions, and metadata to gain insights into what each field represents. This will also show missing values or incomplete data. The next step is to detect any outliers or anomalies that can distort the reality of a data set. Common ways of detecting outliers include data visualization, numerical methods, and hypothesis testing. The use of visualization such as box plots, histograms, or scatter plots makes it easier to identify points outside of the standard range.
The last step is to examine patterns and relationships by plotting a data set in a variety of formats. This makes it easier to spot patterns and relationships among different variables. Deciding which variables to include in a predictive model helps business users find the answers to specific business questions.
Why Data Exploration?
It’s important to understand why data exploration for businesses is important, and it’s necessary to summarize the characteristics of a data set before a deeper analysis can be done. These characteristics include the number of cases, the variables included, missing values, and any potential hypotheses that might be supported by the data. Further analysis can be made using these characteristics.
Ideally, when data analysts review data sets, the goal is to immediately identify variables that lead to valuable business insights. Several correlating data points are candidates for deeper analysis. By skipping the initial step of data exploration, data analysts don’t immediately notice key issues, which affect the direction of the deeper analysis. It may be difficult to find key insights later in the analysis process if the information in the data set wasn’t properly explored in the first place.
Use Cases for Data Exploration
There are several benefits of using data exploration. This first step of data analysis helps businesses quickly explore large amounts of data to better understand the next steps of further analysis. Having a more manageable starting point provides clarity on target variables of interest, and data exploration and data visualizations go hand in hand. Taking a high-level approach to data examination helps businesses decide which data is most important and which can negatively impact the analysis. Data exploration helps data teams spend less time on analysis by showing the right path forward from the beginning.
Data analysts once used statistical models for data exploration, but now, data visualization software and tools are the main methods.
Visualizations such as dashboards, graphs, and charts help analysts quickly find the most relevant data in their data sets. Visualization tools help accelerate and streamline the data analysis process. The best data exploration tools interact with data teams and enable collaboration in the annotation and searching of data sets, making recommendations for visualizations, and automating exploration through machine learning.
Using the right visual tools saves businesses a lot of time and money while gaining the best exploration results.