"Data exploration is the initial step in data analysis, where users explore a large data set in an unstructured way to uncover initial patterns, characteristics, and points of interest. This process isn’t meant to reveal every bit of information a dataset holds, but rather to help create a broad picture of important trends and major points to study in greater detail" (Sisense).

"Often, data is gathered in a non-rigid or controlled manner in large bulks. For ... analysis, this unorganized bulk of data needs to be narrowed down. This is where data exploration is used to analyze the data and information from the data to form further analysis" (Techopedia). "A data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, [which] can include size or amount of data, completeness of the data, correctness of the data, possible relationships amongst data elements or files/tables in the data" (Wikipedia). "Analysts commonly use data visualization software for data exploration because it allows users to quickly and simply view most of the relevant features of their dataset" (TechTarget).

"A best practices approach is to break the analysis process into two distinct steps:

  • Exploration – After data has been prepared, you "explore" the data to see what parts of it will help reveal the answers you seek. You can also explore various hypotheses. One could also think of it as a data refinement or narrowing process.
  • Discovery – Once you know what data helps you find the answer, you dig deep into the data to identify the specific items that reveal the answers and find ways to show those answers to the business teams." — Datameer

More from the data glossary

A definitive guide to data definitions and trends, from the team at Stitch.

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