What are the Top 4 Benefits of Data Exploration for Businesses?

What are the Top 4 Benefits of Data Exploration for Businesses? | Data Science and Analytics | Emeritus

Data lies at the core of business decision-making. From implementing Artificial Intelligence (AI) and Machine Learning (ML) resources effectively to planning marketing campaigns, almost every business decision needs data backing. However, data collected by organizations is often unstructured and contains scattered information, which AI or ML tools cannot understand. This is where exploratory data analysis helps. Let’s discuss what exploratory data analysis is and how it benefits organizations.

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What is Exploratory Data Analysis and Why is it Important?

Exploratory Data Analysis (EDA) is a data mining technique for initial exploration or data manipulation to generate new ideas. It involves visualizing the data to check for errors, broad data patterns, and valuable insights. You can understand data trends, behaviors, and data relationships

As the name suggests, the purpose of exploratory data analysis is just that—to explore data to familiarize oneself with it and find potential insights or probable efforts. Therefore, exploratory data analysis refers to a basic data hygiene check before the data analysis is initiated. Essentially, data analysts or big data professionals visualize the data in a simple format to get an overview. An analogy for understanding exploratory data analysis is a magnifying glass. You can use it to find patterns or trends that are not obvious to the human mind.

How Does Exploratory Data Analysis Differ From Other Types of Data Analysis?

Exploratory data analysis is the first step to initiate advanced data analysis techniques such as predictive analysis, sentiment analysis, or cluster analysis. The key purpose of data exploration is to get a quick overview of its patterns and errors to produce accurate results in further data analysis stages. 

Contrary to this are other data analysis techniques, which are used to get specific insights. For example, sentiment analysis is a technique to assess the tone or sentiment behind a text or audio. This is beneficial in the customer support industry, where businesses use it to analyze customers’ sentiments and helps organizations provide better customer support.

Similarly, predictive analysis is another excellent technique for making predictions or forecasts. This analysis involves analyzing historical data to forecast future trends. For example, organizations launching new products in the market can use predictive analysis to predict consumer demand and forecast their revenue.

Even though exploratory data analysis and other techniques help organizations to make data-driven decisions, the key difference is their use case. The primary purpose of exploratory data analysis is to manipulate data for more effective results. However, other data analysis techniques, as elaborated above, offer specific benefits.

What are the Benefits of Conducting Exploratory Data Analysis for Organizations?

With zettabytes of data being generated every day, organizations need better resources and skills to effectively manage data and remove any silos. However, exploratory data analysis simplifies this. Let’s look at some ways in which exploratory data analysis benefits organizations.

1. Prepare Data Sets for Analysis 

With EDA, you get a basic understanding of data which sets the path for effective data insights, their usage, and implementation. Moreover, as it helps explore new patterns and trends beyond the obvious, exploratory data analysis facilitates innovation.

2. Ensure Data Quality 

Ever wondered why some organizations within the industry have excellent innovations and updates while their competitors offer only basic products? Even though companies in a particular industry may have access to similar data, insights and data quality change the game altogether. It comes down to what information businesses are able to collect and how. 

For example, consider a company whose target audience is C-suite executives. The data collected from market surveys undergoes data exploration. The data analysis team also finds a significant number of responses from early-age professionals, which are removed to avoid data consistency. Therefore, exploratory data analysis ensures data sets comprise all relevant values and figures for making data-driven decisions.

3. Generate Hypotheses 

Organizations can also use EDA to find new patterns and test theories. For example, a retail business, while going through last year’s sales records to calculate its revenue, finds that the sales of grocery items have decreased significantly in the last few months.

They can use this information to create hypotheses, such as that sales have gone down due to poor-quality products or that customers are purchasing items from other shops due to low pricing. Hence, EDA is an effective tool to make data-driven decisions.

4. Communicate Data Better 

EDA also involves data visualization in the form of charts and graphs. This helps professionals communicate data better by simplifying complex information, ultimately leading to better decision-making.

ALSO READ: Data Science vs Data Analytics: Why Data Makes the World Go Round

What are Some Common Techniques Used in Exploratory Data Analysis?


EDA is a simple process that sets the foundation for effective advanced data analysis. It involves data collection, data cleaning by removing unwanted variables, data formatting, and data visualization in the form of charts or graphs.

Let’s see some common techniques for exploratory data analysis:

Summarizing information is one of the most effective EDA techniques, where you analyze data to calculate the mean, median, and mode of variables

  • Data visualization to identify data patterns or outliers using scatter plots, histograms, or heat maps
  • The correlation analysis technique, which involves calculating linear relationships between variables to find how two variables are correlated, such as calculating how the cost of the product impacts its sales
  • The cluster analysis technique, which involves segregating data into different categories to find patterns

ALSO READ: 5 Essential Prerequisites for a Successful Career in Data Analysis

How Can Organizations Effectively Implement the Insights Gained from Exploratory Data Analysis?

The key purpose of exploratory data analysis is to get accurate data insights for effective decision-making. Here is how businesses can use insights from EDA to enhance decision-making:

  • Mitigate risks by anticipating future trends and defining clear risk management strategies
  • Analyze resource consumption and allocate resources better to facilitate growth
  • Generative hypotheses and leverage innovation opportunities 

ALSO READ: How to Build a Successful Career in Data Science and Analytics?

According to the US Bureau of Labor Statistics, data science jobs will increase by 35% from 2022 to 2032. This makes data science one of the most promising careers for the future. However, it is also highly competitive. To stay ahead, therefore, you need to learn relevant data techniques and tools to secure well-paying jobs. Continuous upskilling is the key, and Emeritus can help you with that. Explore Emeritus’ online data science courses to advance your career in data!

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About the Author

Senior Content Contributor, Emeritus Blog
Iha is the grammar guru turned content wizard who's mastered the delicate dance of correcting bad grammar and teaching people how to correctly pronounce her name. With a filmmaker's flair for marketing and digital media, she's the project ninja, flawlessly coordinating remote and in-person teams for 6+ years. When not conjuring captivating copy, she's delightfully torn between diving into 5 books or diving into endless series—decisions, decisions. Beware of her mischievous dog, who is always ready for a great escape!
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