What is Data Exploration?

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.

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

This process makes deeper analysis easier because it can help target future searches and begin the process of excluding irrelevant data points and search paths that may turn up no results. More importantly, it helps build a familiarity with the existing information that makes finding better answers much simpler.

Many times, data exploration uses visualization because it creates a more straightforward view of data sets than simply examining thousands of individual numbers or names.

In any data exploration, the manual and automated aspects also look at different sides of the same coin. Manual analysis helps users familiarize themselves with information and can point to broad trends.

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These methods are also by definition unstructured so that users can examine a whole set without any preconceptions. Automated tools, on the other hand, are excellent at pruning out less applicable data points, reorganizing data into sets that are easier to analyze, and scrubbing data sets to make their findings relevant.

What Can I Use Data Exploration For?

In any situation where you have a massive set of information, data exploration can help cut it down to a manageable size and focus efforts to optimize your analysis.

Most data analytics software includes visualization tools and charting features that make exploration at the outset significantly easier, helping reduce data by rooting out information that isn’t required, or which can distort results in the long run.

By taking the time to perform a real exploration of your data along with visualization tools, you can also start finding correlations, patterns, and determine if a certain path is worth researching, or if the information is less usable.

Data exploration can also assist by reducing work time and finding more useful and actionable insights from the start alongside presenting clear paths to perform better analysis.

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