data exploration

Data exploration is the first step in data analysis involving the use of data visualization tools and statistical techniques to uncover data set characteristics and initial patterns.

During exploration, raw data is typically reviewed with a combination of manual workflows and automated data-exploration techniques to visually explore data sets, look for similarities, patterns and outliers and to identify the relationships between different variables.

This is also sometimes referred to as exploratory data analysis, which is a statistical technique employed to analyze raw data sets in search of their broad characteristics.

Why is data exploration important?

Humans are visual learners, able to process visual data much more easily than numerical data. Consequently, it's challenging for data scientists to review thousands of rows of data points and infer meaning without assistance.

Data visualization tools and elements like colors, shapes, lines, graphs and angles aid in effective data exploration of metadata, enabling relationships or anomalies to be detected.

What industries use data exploration?

Any business or industry that collects or utilizes data can benefit from data exploration. A few common industries include software development, healthcare and education.

The advanced visualization techniques employed by data exploration and business intelligence tools enable businesses and stakeholders to better understand performance metrics by making raw data more comprehensible and creating a "story" around it.

By visualizing patterns and finding commonalities in complex data flows, data exploration can help enterprises make data-driven decisions to streamline processes, better target their ideal audience, increase productivity and achieve greater returns.

Data exploration vs. data mining

In data science, there are two primary methods for extracting data from disparate sources: data exploration and data mining.

Data exploration is a broad process that is performed by business users and an increasing numbers of citizen data scientists with no formal training in data science or analytics, but whose jobs depend on understanding data trends and patterns. Visualization tools help this wide-ranging group to better export and examine a variety of metrics and data sets.

Data mining is a specific process, usually undertaken by data professionals. Data analysts create association rules and parameters to sort through extremely large data sets and identify patterns and future trends.

Typically, data exploration is performed first to assess the relationships between variables. Then the data mining begins. Through this process, data models are created to gather additional insight from the data.

How machine learning is applied to data exploration

Machine learning can significantly aid in data exploration when large quantities of data are involved. However, for a machine learning model to be accurate, data analysts must take the following steps before performing the analysis:

  1. Identify and define all variables in the data set.
  2. Conduct univariate analysis for single variables, using a histogram, box plot or scatter plot. For categorical variables (those that can be grouped by category), bar charts can be used.
  3. Conduct bivariate analysis, to determine the relationship between pairs of variables. This can be completed using data visualization tools, such as Tableau.
  4. Account for any missing values and outliers.

What is the best language for data exploration?

The most commonly used statistical methods in data exploration are the R programming language and Python. Both are open source data analytics languages.

While R is best for statistical analysis, Python is better suited for machine learning algorithms.

Data exploration tools

Data exploration tools make data analysis easier to present and understand through interactive, visual elements, making it easier to share and communicate key insights.

Data exploration tools include data visualization software and business intelligence platforms, such as Microsoft Power BI, Qlik and Tableau.

Available open source data exploration tools can also incorporate regression functionality, data profiling and visualization capabilities, which enables businesses to integrate various, disparate data sources for faster data exploration.

Some popular open source tools include Knime, OpenRefine, NodeXL, Pentaho, R programming and RapidMiner.

Learn more in our roundup of data exploration software and vendors.

This was last updated in July 2021

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