Machine Learning

What is Data Exploration? – Important, Cases, and More

What is Data Exploration?

Data exploration is the first step of information examination used to travel and visualize data to discover knowledge from the beginning or identify areas or patterns to deepen more. Using interactive dashboards and “point-and-click” data exploration, users will better understand the big picture and obtain information more quickly.

Why is Data Exploration Important?

Starting with data exploration helps users make better decisions about where to drill down into the data and gain a broad understanding of the business when asking more detailed questions later.

With an easy-to-use interface, anyone in an organization can become familiar with the data, discover patterns, and generate thoughtful questions that will stimulate more profound and more valuable analysis.

Visual Analytics and Data Exploration tools promote understanding, allowing users to explore data on any display. This approach speeds response time and deepens user understanding by covering more ground in less time.

Data exploration is essential for this reason because it democratizes access to data and provides self-service managed analytics. In addition, companies will accelerate data exploration by sourcing and delivering data through visual data marts that are easy to explore and use.

What are the Top Use Cases for Data Exploration?

Data exploration helps companies explore large amounts of data quickly better understand the next steps in further analysis. This gives the business a more manageable starting point and a way to target areas of interest. In most cases, data exploration involves using data visualizations to examine the data at a high level.

By taking this high-level approach, companies will determine which data is most important and which data will distort the analysis and therefore should eliminate. Data exploration can also help reduce time spent on less valuable research by selecting the right path from the very beginning.

Concepts of Data Exploration

As you learn to explore and visualize your data, it helps to review critical concepts like visualization, measurement, and dimension.

Visualization is commonly a graph, such as a bar chart, donut chart, timeline, or heat map. It can also be data in tabular forms, such as a comparison table or a pivot table. Each visualization has an underlying query, which Tableau CRM uses to retrieve information from the source data.

A measure is a quantitative value that contains numerical data, such as income and the exchange rate. You can perform mathematical operations on the measurements, such as calculating the total revenue and the minimum exchange rate.

The measures have names (income) and values (€ 1,000,000). When you are viewing a chart visualization in Tableau it CRM software, it is essential to remember:

  • The chart displays a portion of your data based on the number or quantity of something or displays tabular data.
  • A measurement usually aggregates somehow, which means that it displays with some arithmetic operation applied. For example, when you first opinion a dataset, you often see a simple aggregation such as counting rows.
  • Typically you will add a different method (sum, mean, maximum, and so on) as you explore and change or add measurements. Still, you must always specify the aggregation method when selecting the size.
  • You can identify measurements by their position (the leftmost items in the upper-left corner of a lens) and the text indicating the aggregation method (such as Sum of Revenue).

Techniques of Data Exploration

Some various approaches/techniques can take in data mining. Some of them are:

  • Using the number of unique values of categorical columns.
  • To detect how often individual values appear in a column. This will give you an overview of the content of the categorical variables.
  • When analyzing numerical values, the minimum, maximum, and variance of the data values provide a good indication of the distribution of the values.
  • Pareto analysis is also effective in data mining.
  • The histogram can use to obtain information for a range of values that fall within the majority sector.
  • It notes any biases in the data and also indicates the maximum and minimum values of the data.
  • A heatmap of correlation between all numeric columns is a great way to understand the relationship between different data types.
  • Pearson’s correlation method use to understand the trend between two numeric columns.
  • Another powerful data mining is Cramer V, which maps on all categorical columns.
  • Cluster size analysis is often adopted to deal with large amounts of data where the data is divided into different groups/clusters and then analyzed.
  • Outlier detection uses when there is something unusual in the data. Here, standard deviation analysis methods or algorithms, such as isolation forest, are used to obtain outliers in numeric columns. Outliers can use for multiple columns.
  • Specialty visualization ranges from bar charts and scatters charts to radar charts, neural network visualization, and Sankey charts.


Data exploration is the initial stage of data analysis, where users explore a large set of data in an unstructured way to discover initial patterns, characteristics, and points of interest. This process does not intend to reveal all the information in a data set but rather to help create a holistic picture of significant trends and key points for further study.

Data mining can use a combination of manual methods and automated tools, such as data visualizations, charts, and initial reports.

Also Read: What is Home Automation? – Work, Advantages, Disadvantages, and More

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