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data exploration

By Scott Robinson

What is 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 identify the relationships between different variables.

Data exploration is also sometimes referred to as exploratory data analysis, which is a statistical technique used 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 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 such as colors, shapes, lines, graphs and angles aid in effective data exploration of metadata, enabling relationships or anomalies to be detected.

How does data exploration work?

There are three general steps included in data explanation:

1. Understand your variables. To explore a data set meaningfully, it's essential to know the nature of all the data in it. This is usually clear from the names and the descriptions of the data columns, the data types and other associated metadata. These can usually be found in an organization's data catalog.
2. Search for outliers. A data set can include outliers -- data points that stray far from the average of similar data points. They can distort the exploration of the data by skewing averages and other statistics that emerge during exploration. Such outliers can be found by visually plotting the data in scatterplots, histograms or other graphics. Once identified, data scientists can remove, ignore or investigate outliers as needed.
3. Look for patterns and relationships between data items. Visual data plots as well as a broad range of analytics tools can reveal patterns within data sets that provide additional layers of meaning and insights. This, in fact, is the primary product of data exploration: to discover value in data that wasn't apparent before. These insights can lead to using the data to explain past events, facilitate predictive modeling and select the best courses of action.

What industries use data exploration?

Any business or industry that collects or uses data can benefit from data exploration. In fact, it's difficult to conceive of an industry that wouldn't. Some of the more prominent industries where data exploration is prevalent include the following:

• Software development.
• Engineering.
• Healthcare and medicine.
• Education.

Businesses and stakeholders use advanced visualization techniques, data exploration and business intelligence tools 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.

What is exploratory data analysis?

Exploratory data analysis is an explicit subset of data exploration that's comprised of many statistical analysis techniques and visualization strategies used to surface patterns more accurately and examine them more deeply. These can include correlation, regression testing, standard deviation, dimensionality reduction, significance testing and principal component analysis.

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's 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.

Data exploration in machine learning

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 an 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 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.
4. Account for any missing values and outliers.

What is the best language for data exploration?

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

R is generally best suited for statistical analysis, and many business analysts and data scientists find it easier and often faster to use than Python. But Python is better suited for machine learning algorithms. It can be more flexibly applied in complex processing environments and there are numerous open source libraries available for Python that are focused on data exploration and analysis.

Data exploration tools

It's possible to do data exploration with the simplest of desktop tools -- even Structured Query Language and Excel spreadsheets. But there are also many dedicated tools suites that are suited to the purpose.

Data exploration tools from software vendors include data visualization software and business intelligence platforms, such as the following examples:

• Microsoft Power BI.
• QlikView.
• SAS.
• Splunk.
• Tableau.

Several open source tools are also available. They offer regression functionality, data profiling and visualization capabilities that let businesses integrate various, disparate data sources for faster data exploration. These tools include the following:

• Knime Analytics Platform.
• OpenRefine.
• NodeXL.
• Plotly.

Learn how data teams can use generative AI to improve their predictive analytics insights.