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Exploratory data analysis explained

18 June 2024
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The earliest origins of data analytics we are aware of date back to 18,000 BCE when Paleolithic tribes marked the number of supplies and trades on sticks and bones. Today, with such advanced methods as exploratory data analysis, we can simultaneously keep track of numerous records and draw conclusions from information. Humankind’s interest in collecting and processing data since its beginning shows how quickly we realized it’s useful and valuable. The fact that for centuries we’ve put large efforts into perfecting these methodologies proves that data-related operations can lead to impressive results.

In this article, we will take a closer look at exploratory data analysis (EDA), because it stands out as a fundamental approach that unlocks precious insights within vast datasets. Businesses can use this framework to their advantage, and we will comprehensively explain how. This guide will also discuss what EDA is, what its benefits are for organizations from different sectors, how it’s performed, and what its applications are.

What is exploratory data analysis?

The definition of exploratory data analysis (EDA) says that it’s an iterative process of examining and visualizing data to discover hidden patterns, find anomalies, characterize the nature of specific datasets, and create hypotheses based on these discoveries. It stands in opposition to confirmatory data analysis, which has one goal – to confirm or deny a particular argument. EDA is a method used to explore collected data without preconceptions, often by letting certain ideas come to the surface in real time.

Exploratory data analysis

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Exploratory data analysis is usually the first step in the general process of data analytics. It’s focused on finding unusual conclusions that were not expected, but are evident after the data becomes preprocessed and initially looked through. Moreover, it allows analysts to detect dependencies and connections between certain factors. EDA results can be a great foundation for future, more advanced activities like statistical analysis. It’s also used for decreasing potential failures since one of its objectives is to check if the initial expectations towards particular datasets were true or false.

Why is exploratory data analysis important?

Businesses analyze tons of data from multiple sources: social media, CRMs, ERPs, online news, digital platforms, internal systems, data warehouses, Google Analytics, and even hardware sensors. Drawing quality conclusions requires meticulous preparations and attention to detail. Even with the help of AI and data analytics automation, it’s important to divide the data analysis process into parts to make sure information groups are organized, categorized, clean, and concise.

EDA becomes the ally for companies that want to kickstart their data analysis without presumptions. It can be the first stage of a bigger project that brings an opportunity to learn about potential errors within datasets and trace missing but required information. The results of exploratory data analysis can be surprising – it can showcase irregularities or relationships that were not at all anticipated.

EDA definition

Exploratory data analysis steps

Let’s take a closer look at the EDA process and its respective elements. How to perform such analysis the right way?

  • Data collection: At first, the data analytics team has to gather relevant datasets from all the required sources, including internal and external databases, cloud services, external APIs, social media statistics, news, etc. The choice of sources depends on the goals of the analysis.
  • Data cleaning: This phase is necessary to handle missing values, eliminate duplicates, and address inconsistencies. High integrity and quality of data ensure better results and more relevant actionable insights.
  • Descriptive statistics: Calculating important numbers such as mean, median, mode, standard deviation, quantities, and more allows analysts to understand emerging tendencies and data dispersions.
  • Data visualization: Data representations can come in many shapes and forms, like diagrams, graphs, charts, heat maps, and plots. They make relations, trends, and distributions visible for the stakeholders who are not proficient in data reading.
  • Feature engineering: This process is dedicated to preparing the data from EDA for being processed by predictive models and further analysis. It can also lead to additional conclusions and is often done multiple times to test if datasets provide expected outcomes.
  • Dimension reduction: Next it’s time for simplification. Various techniques are used to reduce the number of variables in the prepared data without discarding valuable information. This stage allows the data scientists to remove noises, eliminate useless parts, and underline the most important ones. It’s crucial to achieve efficiency and accuracy of EDA AI models, and can significantly reduce required storage space and computing power.
  • Hypothesis testing: Validating the major assumptions of the project is important to establish if the previous steps were performed according to the state-of-the-art. If some issues come to the top, the next iteration can be dedicated to addressing them.
  • Iterative exploration: EDA is often used to refine data in a series of repeatable steps from this list. Different techniques are used to gain even deeper insights while validating data and cleaning it from irrelevant resources.

Exploratory data analysis techniques

Specialists can use a variety of exploratory data analysis methods to dig through datasets and extract the best insights. Here are some essential EDA techniques:

Univariate analysis

The act of examining single variables in isolation to determine their properties is called univariate analysis. It doesn’t involve relationship analysis since there’s only one variable to consider. This type of EDA can also be categorized as non-graphical or graphical – the latter involves the usage of histograms, bar charts, plots, and other visual methodologies for more complete results. Summary statistics can be involved, too.

Data analysis

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Examples:

  • Identifying the most frequently occurring value in a dataset can be useful in understanding central tendencies, such as the most common type of car color sold by a dealership.
  • A pie chart can represent the proportion of different categories within a single variable, like the percentage of different types of pets owned by households in a neighborhood.
  • Descriptive statistics can help understand such conditions as the distribution of housing prices in a city.

Bivariate analysis

Bivariate analysis observes the relationships between pairs of variables to determine correlations and dependencies between them. By finding how a change in one variable influences another, analysts can identify more associations within data architecture and share opinions on how significant are their relations. This type of analysis can focus on categorical and numerical types of data.

Examples:

  • By using data from marketing tools, the analysis can showcase the relationship between advertising costs and sales revenue.
  • Scientists can examine how two weather parameters influence each other and what results they produce.
  • Investigating the correlation between stock prices and interest rates leads to better financial decisions.

Multivariate analysis

This technique simultaneously explores interactions among multiple variables. It’s recommended to analyze complex data structures and detect advanced dimensions. Multivariate analysis can oscillate around dependent and independent variables, and often uses advanced algorithms (like machine learning) to speed up the process.

Examples:

  • Applying Principal Component Analysis (PCA) to customer demographic data can reveal appropriate segments for targeted marketing strategies.
  • Analyzing survey responses can uncover potential factors that can be improved to increase customer satisfaction.
  • Checking out the relationship between employee engagement questionnaire results and performance metrics shows what drives employee productivity.

Time series analysis

To understand temporary patterns, trends that happen over time, and potential periodical changes within datasets, a time series analysis is performed. To make it viable, data should be collected at regular intervals and organized in a chronological order called sequence. This type of analysis can be applied to many industries, including sales, stock markets, meteorology, and economics. Besides tendencies, it can also be useful in discovering the seasonal appearance of certain phenomena.

Examples:

  • Monthly sales data can be an indicator of long-term trends and periodical fluctuations.
  • Assigning temperature data from various days, weeks, and months into different categories can help identify climatic patterns.
  • Analyzing energy usage over time can lead to better resource allocation and optimization.

Cluster analysis

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Cluster analysis

Cluster analysis aims to identify groups of similar observations within the processed data. It’s helpful when data analysts want to segment information into clusters based on their characteristics to uncover even more hidden patterns. Moreover, it can showcase the natural tendencies of certain data parts to groups.

Examples:

  • Segmenting customers based on their purchasing behaviors can help e-commerce stores to curate tailored marketing strategies.
  • Grouping countries based on socioeconomic indicators allows politicians and policy-makers to see similar development profiles within a few clusters.
  • Clusters can also be handy in judging anomalies in network traffic to enhance cybersecurity measures.

Advanced exploratory data analysis techniques

To achieve even deeper, more comprehensive results, EDA teams can harness the power of artificial intelligence algorithms. They can power up the fundamental methodologies mentioned above or collaborate with them to extract new insights. They are especially useful in the case of vast, complicated datasets. For example, Natural Language Processing (NLP) allows organizations to extract insights from unstructured text like customer reviews, social media posts and comments, emails, documents, and articles. NLP-based models prove themselves in processing textual data to uncover sentiments and themes that enable organizations to recognize the information required to grow. Without such algorithms, this process would be extremely time-consuming and prone to errors (when done by humans).

Machine learning is another tool appreciated for its quick skills in clustering, classification, and detection of interesting patterns within large datasets. The exploratory process requires a detailed approach, and ML algorithms can provide it thanks to appropriate training. AI models can be fed with historical data or connected with other sources of intelligence to identify current trends, detect anomalies, and predict future outcomes. Thus, machine learning can augment traditional exploratory data analysis to produce more precise and relevant results.

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Automated exploratory data analysis

When discussing AI in EDA, it’s important to underline the amount of data produced every day. It is predicted that in 2024, humanity will produce 120 zettabytes of information. In 2025, it will reach 181 zettabytes. It seems that we will create more data every year, and handling it without the help of technology became impossible a long time ago. Thanks to AI advancement, companies can automate certain data analysis activities, leading to even more streamlined and successful processes.

Why automation is the future of EDA?

Here’s a list of improvements AI-driven automation can offer:

  • Handling large-scale datasets: Manual EDA, even done with dedicated toolkits, becomes highly impractical. Automated exploration means efficient processing of vast data volumes without overwhelming involved teams.
  • Accelerated analysis: Companies have to make decisions and react to trends as swiftly as possible. Thanks to the automation of repetitive processes, they can quickly get access to concise, visualized insights. Also, analysts can focus on interpreting insights early, instead of spending time on the initial phases of a specific project.
  • Improved consistency: Manual EDA is prone to human errors and low accuracy, especially when it comes to most mundane tasks. Automation algorithms can perform repetitive activities 24/7 and deliver the same level of consistency thanks to predefined rules.
  • Boosted scalability: Automation enables scalability that is beyond traditional exploratory data analysis techniques. Companies can increase their efforts without compromising performance whenever required, even if they suddenly provide big, complex datasets.

What EDA automation can do?

There are many things artificial intelligence-driven automation can level up in exploratory data analysis:

  • Data cleaning and preprocessing: Algorithms can be responsible for ensuring and keeping the high quality of datasets by searching for missing values and eliminating inconsistencies.
  • Exploratory visualization: Automation tools can generate a range of diverse visualizations without human interference. They can create scatter plots, heatmaps, graphs, box plots, histograms, and complete reports that explore the distribution of data, dependencies, patterns, and conclusions.
  • Statistical analysis: Thanks to advanced capabilities, automation AI and EDA can perform a wide range of statistical tasks, including descriptive analysis, hypothesis testing, simulations, and correlation analysis. This leads to a deeper understanding of specific datasets and their properties.
  • Feature engineering: Automated EDA platforms can assist in tasks such as feature selection, transformation, and creation. By extracting relevant features from the data, machine learning models receive enhanced predictive power and improve their performance.

Business advantages of exploratory data analysis

To understand why EDA can be helpful for organizations, no matter their area of expertise, it’s important to know what benefits it offers:

Data-driven decision-making

To make decisions based on evidence, not hunches, CEOs, managers, and other high-level employees need data-based knowledge to learn from. Exploratory data analysis delivers empirical proofs and actionable recommendations that can be used to take the next steps in business, despite the subject.

Enhanced decision making

Source: Unsplash

Personalization

Understanding customer behaviors and crafting strategies according to their preferences is imperative for businesses to thrive. Automated AI for EDA enables organizations to group their customers based on various factors and deliver the most personalized, satisfying customer experience to each one of them. Thanks to the power of data, companies don’t have to guess consumers’ expectations.

Risk mitigation

Eliminating potential risks is a key goal for many organizations that want to proactively care for their stakeholders and customers. Exploratory Data Analysis can play a crucial role in safety management by identifying threats, irregularities, and other suspicious indicators within datasets. This way, companies get an advantage in addressing them early on, minimizing losses in revenue and reputation.

Innovation and optimization

EDA can be an ally for brands that want to drive innovative ideas and optimize their internal process. Data analysis is the best way to uncover opportunities and take action on them – it can be used to quickly discover current trends regarding new technologies, tools, and platforms for workflow improvement. Moreover, it can reveal inspiring insights into customer demands and help build ambitious strategies to enhance market competitiveness.

Enhanced communication

EDA visualizations are communication mediums between analysts and the rest of the company. Thanks to graphs, charts, dashboards, and other reporting measures, every involved party can understand and interpret the findings, drawing conclusions and insights to meet mutual objectives. By effectively using them, companies can foster collaborations, align their goals with departments, and help teams make data-driven decisions.

Strategic planning and forecasting

Combining past performance analysis with emerging trends examination, Exploratory Data Analytics is viable for businesses that want to make accurate predictions and plan their budgets, sales, or other endeavors accordingly. Planning sessions backed up with EDA results support promising initiatives and can also help with appropriate budgeting, resource organization, process optimization, and revenue streamlining. Additionally, it increases the company’s ability to match rapidly evolving market dynamics when it’s necessary.

Planning

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Compliance with regulatory requirements

Almost every industry, especially the ones that operate in the digital realm, but also the ones that handle sensitive data (healthcare, finance, etc.), has to comply with numerous regulations and laws. EDA can help organizations determine if their data practices adhere to regulatory standards. By identifying issues within datasets, companies can swiftly introduce corrective measures to avoid fees and other penalties. Maintaining compliance is also essential to building and keeping customer trust.

Applications of exploratory data analysis

In what areas EDA can prove its usefulness? Here are the most common use cases:

Marketing

Exploratory Data Analysis is a great way to process tons of various marketing data, including texts from ads, social media posts, and websites, statistics from campaign performance, reviews, and traffic heatmaps. Every piece of information is valuable to maximize the impact of marketing efforts and increase ROI. Marketing analytics is the foundation of all promotional strategies.

Customer care

Understanding customers is crucial to achieving business goals. That’s why EDA can be harnessed to investigate feedback, interactions, and service metrics to introduce fresh ideas that can improve customer satisfaction, personalize experiences, and increase sales in the long run. Thanks to customer care analytics, companies don’t have to ask their clients directly about their expectations and needs.

Finances

EDA can be an ally for brands that want to handle their finances with caution, and it also plays an instrumental role in the financial sector. Data analysis is crucial in fraud detection, portfolio optimization, stock market predictions, and risk management. Budgeting, investing, and other money-related decisions wouldn’t be so efficient if it weren’t for historical data and data-driven forecasting.

Financial analysis

Source: Unsplash 

Supply chain

Optimization of inventory levels, demand predictions, and proper resource allocation are several areas where exploratory data analysis proves to be useful. It can be used to examine past sales data, measure supplier performance, and investigate logistics planning to optimize costs, streamline operations, and increase overall customer satisfaction. EDA is a great tool for managers and employees who want to eliminate bottlenecks and enhance efficiency in that area.

Human resources

HR departments can apply EDA results to talent management and recruitment strategies – from everyday tasks like determining employee performance, retention, and skill gaps to analyzing candidates’ data and workforce trends that can help find the best new hires. Human resources professionals can foster a culture of excellence and innovation since they are the first instance for the entire staff. Exploratory data analytics is their support in building expert teams and increasing lasting employee engagement.

HR

Source: Unsplash 

Conclusion 

Exploratory data analysis provides countless benefits to companies of all industries and can be applied to various areas of operations. Using it to increase business profits and reach other goals is a strategic decision that can also provide a competitive edge and introduce innovation. In addition, it’s a great tool to foster satisfaction among employees, customers, and partners. We recommend introducing EDA solutions with a seasoned big data partner to achieve spectacular outcomes.

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