What is Data Analytics?

Data analytics is a broad term for the process of evaluating raw data in order to draw conclusions that can be used to provide useful insights that will drive decision-making at a company or business. This field is gaining in importance as more organizations become data-driven.

Those with a background in data analytics have many professional opportunities. They help organizations to better understand customers, improve advertising campaigns, create more personalized content, and ensure that their products meet expectations. Companies and organizations that implement data analytics into their business models are able to cut down on cost by finding more direct and profitable methods for conducting business.

This article will provide a deeper look into one kind of data analytics: exploratory data analytics.

What is Exploratory Data Analytics?

Exploratory data analytics (EDA) is one form of data analytics. This field is involved with analyzing and exploring datasets in order to summarize their dominant characteristics. The American mathematician John Tukey developed EDA in 1977, and in the time since, it has continued to play an integral role in the data discovery process.

Once data is collected, it is stored in a data repository. In some instances, the repository is as basic as a spreadsheet; in other cases, it may span many spreadsheets or datasets. The rows within a database pertain to individual records and the columns are the characteristics of the records. However, it can be tedious and taxing to scan data in this format for learning purposes. With the help of EDA, Data Analysts and other stakeholders can incorporate visual tools like statistical graphs and data visualizations to learn as much as possible from the data.

EDA helps Data Analysts and Data Scientists decide the best method for data-source manipulation to yield the most desired answers, which in turn helps locate anomalies and patterns, as well as to test assumptions and hypotheses. One of the main reasons EDA is used is to learn what insights can be revealed in data that extend beyond traditional modeling and hypothesis testing. These insights can offer a company or organization a more detailed understanding of not only the variables in a dataset, but how they are related to one another.

There are typically four main EDA areas:

  1. Univariate non-graphical (pertains to studying one variable, such as income level, age, or height).
  2. Univariate graphical
  3. Multivariate non-graphical (pertains to studying multiple variables simultaneously)
  4. Multivariate graphical
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Real-World Examples of Exploratory Data Analytics

Exploratory data analytics has applications in many fields, such as:

  • In marketing, it sheds insights into various circumstances, such as why customers are no longer purchasing a product or why a particular campaign is successful. By understanding the circumstances surrounding those variables, EDA provides Analysts with a strategy for action.
  • In retail, EDA can be used by business managers to spot weak areas in a store or franchise in order to suggest areas that can be targeted for increased revenue.
  • When used in the healthcare industry, EDA is helpful for spotting natural patterns embedded in large stores of medical data.
  • In historical contexts, EDA can be applied to create new data about past events. By using data collected from sources such as archeological digs, digitized photos, and text, it’s possible for Data Analysts to have a more robust understanding about past events that have remained a mystery for millennia.

Pros & Cons of Using Exploratory Data Analytics

Benefits of Using Exploratory Data Analytics

Those working with EDA cite many benefits this form of analytics can provide. Here are a few of the main perks to using EDA on a dataset:

  • Whereas classical data analytic techniques tend to be quantitative and incorporate tools like ANOVA and hypothesis testing, EDA relies more on graphical representations, such as box plots, scatter plots, and histograms. They target the brain's natural ability to recognize patterns visually.
  • EDA is designed to facilitate unexpected yet important discoveries in data.
  • Unlike classical analytic techniques, which tend to be formal, rigid, and objective, EDA techniques do not adhere to this same rigidity. Instead, they are suggestive and subjective. The interpretations they provide may differ from those of classical analytics, yet they lead to the same conclusions.
  • It provides a detailed understanding of the data.
  • Classical estimation techniques aim to discover the most vital data characteristics by incorporating filtering and mapping, a process that can result in information loss. EDA offers a more holistic approach to how the data is treated, in which no information is lost and Analysts are able to discover more insights by reviewing all available variables.

Challenges of Using Exploratory Data Analytics

In addition to the benefits working with EDA provides to Data Analysts, there are some challenges to be aware of as well before incorporating this analytics approach into your workplace:

  • When working with high-dimensional data, EDA is not effective.
  • If it is not performed correctly, the EDA process can misguide a problem, which can lead to unhelpful or even incorrect insights down the line.
  • The results provided by EDA are qualitative in nature. This can result in biased interpretations.
  • Because most exploratory research draws from a relatively small sample, it can be challenging to apply it to a generalized population.
  • Other data limitations, such as incomplete datasets and blind spots, can affect the conclusions drawn from a dataset. It’s important for a team working on an analytics project to be aware of limitations and outside variables.

The Future of Exploratory Data Analytics

Exploratory data analytics has been around for nearly five decades and continues to play an integral role into the data analytics process. It is likely that Analysts will continue to draw from this approach when handling datasets. In order to continue to optimize EDA, organizations will need to have the proper tools in place, as well as those with the necessary training on board to best manage datasets.

In the future, it is possible that automated visual EDA tools will be incorporated into the analytics landscape, and that large-scale EDA will provide new and innovative ways for humans to draw on their natural ability to visually interpret patterns. These capabilities are expected to continue to transform the way businesses, Data Scientists, and Data Analysts interpret and visualize large datasets.

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