Data exploration

Why is data exploration important?

Datasets often comprise a large number of data points gathered from a range of sources, making it difficult to gain a comprehensive view of what the data contains. Data exploration provides this insight, prior to more detailed analysis.

As it uses data visualization techniques, data exploration outputs (such as charts or graphs) are easier for humans to process, understand and act on.

Data exploration helps identify:

• Patterns and relationships
• Anomalies
• Trends
• Errors or outliers
• Latent insights

What are the benefits and uses of data exploration?

What are the benefits of data exploration?

Data exploration provides the foundation for data analysis, enabling:

• Better informed decision-making
• Risk mitigation and compliance
• Optimized operations
• Improved operational efficiency

Where is data exploration used?

• Finance: Detecting fraud by analyzing transactional data
• Retail: Analyzing sales data to optimize inventory/supply chains and better forecast demand
• Manufacturing: Identifying production inefficiencies or predicting equipment failures
• Marketing: Analyzing customer behavior and using it to deliver targeted, personalized campaigns
• Regulatory Compliance: Spotting fraudulent/non-compliant activities and immediately flagging them

What are the tools for data exploration?

Data exploration can be carried out through both manual analysis and automated data exploration software solutions. Manual methods include writing queries scripted in languages such as Python, SQL or R, and spreadsheets such as Microsoft Excel, while automated data exploration tools, such as data visualization software and business intelligence software, help speed up and scale the process.

What is Exploratory Data Analysis?

Exploratory Data Analysis (EDA), is a subset of data exploration made up of statistical techniques such as correlation, regression testing,standard deviation, dimensionality reduction, significance testing and principal component analysis, used to analyze data sets for their broad characteristics.

What are the steps in data exploration?

There are three general steps included in data exploration:

• Understand the data, such as through metadata and the names/descriptions of data columns
• Search for outliers or errors that can then be removed, corrected or investigated through data cleaning
• Visualize the data to create charts and graphs that enable users to look for patterns and relationships to discover value in data that wasn’t apparent before.
• Once data exploration is complete, fuller analysis can be carried out in specific areas of interest, either by humans or algorithms.

How does data exploration differ from other techniques?

While it has similarities with other data techniques, data exploration is a standalone discipline, as the comparisons below show:

Data exploration and data mining

Data exploration manually analyzes data, whereas data mining is an automated process that aims to extract useful information and patterns from large datasets. Data exploration typically occurs before data mining in order to understand relationships and thus focus algorithms most effectively.

Data exploration and data visualization

Data exploration often involves data visualization, helping to understand datasets and find patterns by representing them visually, such as through charts and graphs. However, data visualization has many more uses than solely data exploration – for example, it can be used to visualize datasets on a data portal or data marketplace, as graphs, maps and dashboards, helping make them more understandable and usable to non-specialists.

Data exploration and data discovery

Data discovery and data exploration are related but different concepts. Data discovery involves the process of helping users to search for and find specific data, such as through a data catalog or data marketplace. It is key to making data assets available and consumable at scale across organizations and ecosystems. Data exploration happens before data discovery, and gives deeper insight into the meaning of a dataset by identifying areas or patterns to dig deeper into.