What is Data Visualisation? Why is it Important?

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Data is complex to interpret due to its sheer volume, variety, and complexity. It often contains hidden patterns, relationships, and insights that can be difficult to discern. Data visualization simplifies data interpretation by presenting information in visual formats such as charts, graphs, and maps. These visual representations allow us to perceive trends, patterns, and outliers quickly. By condensing complex data into visual elements, data visualization enhances the understanding, aids in identifying correlations, and supports effective decision-making across various domains, from business analytics to scientific research.

The biggest advantage of having information presented in a visual format is that it enhances cognition and comprehension. It allows the user to identify outliers, compare data points, and make informed decisions more easily. Visualizations also facilitate the exploration of data from different perspectives, enabling users to uncover insights and draw meaningful conclusions. 

In this blog, we will explore data visualization, its definition, significance, and why it has become an integral part of various industries. 

According to  Springer Link, people who follow illustrations are doing 323% better than people who are following texts.

Data Visualization Simplifies Data Interpretation

Data Visualization Simplifies Data Interpretation

Importance of Data Visualization

What are the 4 pillars of data visualization?

The four pillars of data visualization are clarity (clear communication of information), simplicity (avoiding unnecessary complexity), efficiency (conveying insights quickly), and accuracy (maintaining data integrity).

Data visualization is of paramount importance in various domains and fields, ranging from business and academia to journalism and government. Here are some key reasons why data visualization is essential:

  • Facilitate Data Exploration: Interact with data, uncover insights, and gain a deeper understanding through interactive visualizations.
  • Enable Storytelling: Craft compelling narratives by presenting data visually, captivating audiences, and conveying messages effectively.
  • Promote Transparency and Accountability: Make complex data accessible and transparent, fostering trust, accountability, and better decision-making.
  • Facilitate Collaborative Analysis: Foster collaboration and collective analysis by sharing visualizations among team members.
  • Improve Data Quality Assessment: Enhance data cleaning and validation processes through visual representations of data anomalies.

Benefits of Data Visualization

1. Improved Understanding of Complex Data

Data visualization enhances the comprehension of complex data by presenting it in visually intuitive formats. It enables users to identify patterns, trends, and correlations more effectively, leading to improved insights and decision-making. By simplifying information, data visualization facilitates a deeper understanding of intricate datasets.

To understand better, Imagine a bustling metropolis with a complex transportation system, where thousands of commuters navigate the city daily. To better understand the flow of people and optimize transportation routes, city officials turn to data visualization.

They create an immersive 3D map of the city, with colored lines representing different modes of transportation, such as buses, trains, and subways. As the visualization unfolds, they notice clusters of vibrant colors converging around certain areas, indicating high traffic congestion. They zoom in to explore these hotspots further.

By layering real-time data on population density, they witness a surge in activity around a particular neighborhood during the morning rush hour. Next, they superimpose weather data and discover a correlation between rainy days and increased congestion in that area. This insight prompts them to implement targeted infrastructure improvements, like additional bus routes or covered walkways, to alleviate the strain during inclement weather.

2. Identification of Patterns and Trends

Data visualization aids in identifying patterns and trends within data by presenting information visually and interactively. It allows users to spot recurring patterns, detect anomalies, and uncover trends that may not be apparent in raw data alone. This enables better forecasting, strategic planning, and data-driven decision-making.

Let's consider an example to understand the concept of identifying patterns and trends using data visualization. Imagine you own a small ice cream shop in a popular tourist destination. You have been tracking your daily sales data for the past year. Simply looking at rows and columns of numbers makes it hard to extract meaningful insights.

However, you can easily spot recurring patterns when you create a line graph using data visualization. Ice cream sales consistently spike on weekends and during the summer months. With this information, you can make data-driven decisions, such as increasing inventory and staffing during peak times, leading to better forecasting and improved profitability.

3. Communication of Insights Effectively

Transforming complex data into visual representations enables concise and compelling storytelling. This helps to convey information and findings in a more accessible and engaging manner, fostering better understanding and engagement among stakeholders and facilitating data-driven discussions.

Let's consider the example of a manufacturing company that wants to improve its production efficiency. They have gathered extensive data on various factors such as machine downtime, production volumes, and maintenance schedules. To communicate insights effectively, the company decides to utilize data visualization.

Using visually appealing charts and graphs, they create a dashboard that presents real-time information on machine performance, downtime patterns, and maintenance effectiveness. This allows stakeholders, including production managers and maintenance technicians, to grasp the key gaps and identify areas for improvement quickly. By visually representing the data, the company fosters meaningful discussions and collaboration among teams, leading to more informed decisions and targeted actions to optimize production processes.

4. Data-driven Decision-making

By transforming complex data into visual representations, data visualization empowers data-driven decision-making. Visualizations provide a clear and concise overview of information, enabling faster comprehension and identification of trends, patterns, and relationships. This aids in making informed decisions, driving insights, and maximizing the value of data for strategic choices.

Have you ever wondered how sports teams make strategic decisions during matches?

Let's take the example of a professional soccer team.

They collect vast amounts of data on player performance, such as distances covered, successful passes, and shots on goal. To make data-driven decisions, the team utilizes data visualization.

Through visually compelling charts and graphs, the coaching staff creates a dashboard that presents key performance metrics during matches. They can quickly analyze trends, identify players who are excelling or struggling, and pinpoint areas for improvement. Visualizations help the team understand their opponents' strategies, such as their preferred attacking areas or defensive weaknesses.

By leveraging data visualization, the soccer team makes informed decisions on substitutions, game plans, and strategies, ultimately increasing their chances of success on the field. Visualization empowers the coaching staff to transform complex data into actionable insights, leading to better performance and results.

Common Use Cases of Data Visualization

1. Business Analytics and Reporting

Data visualization plays a crucial role in business analytics and reporting. It transforms complex data into intuitive visualizations that business users can easily interpret and understand. Here are some key aspects of this use case:

  • Data Exploration: Visualize and uncover insights by representing and exploring large volumes of data.
  • Performance Monitoring: Monitor KPIs, identify bottlenecks, and improve efficiency through real-time visual dashboards.
  • Trend Analysis: Identify patterns, and predict future performance using historical data visualizations.
  • Comparative Analysis: Compare data across dimensions to identify strengths, weaknesses, and opportunities for improvement.
  • Sales and Revenue Analysis: Understand sales performance, analyze customer behavior, and track revenue growth through visualizations.
  • Decision-Making Support: Simplify complex data through visually appealing dashboards and reports for better decision-making.
According to The 2021 Gartner Analytics & Business Intelligence Platforms Magic Quadrant Insights Webinars, 25% of leaders view data storytelling as one of the most important emerging capabilities.

2. Scientific Research and Exploration

Scientific research and exploration is a significant use case for data visualization. Data visualization is crucial in helping scientists and researchers analyze and understand complex datasets, discover patterns, and communicate their findings effectively. Here's how data visualization is used in scientific research and exploration:

  • Data Exploration: Visualize and interact with complex datasets to identify patterns and relationships.
  • Pattern Recognition: Spot correlations and meaningful insights by visualizing data in various formats.
  • Hypothesis Testing: Use data visualization to test hypotheses and validate research findings.
  • Presenting Research Findings: Communicate complex data effectively through visually appealing representations.
  • Data Sharing and Collaboration: Facilitate collaboration and feedback by sharing visualized research findings.
  • Simulation and Modeling: Visualize complex models to understand system behavior and simulate scenarios.
  • Geographic Visualization: Map data onto geographical regions for spatial analysis and environmental research.

3. Social Media and Web Analytics

Social Media and Web Analytics is another important use case for data visualization. It helps businesses and marketers gain insights from social media platforms and web data. Here are the common use cases of data visualization in this context:

  • Social Media Monitoring: Visualize data to track brand mentions, sentiment, and engagement levels.
  • Audience Analysis: Analyze demographic and behavioral data to understand audience characteristics and preferences.
  • Trend Analysis: Identify emerging trends, popular topics, and viral content through visual representations.
  • Campaign Performance: Visualize key metrics like reach, impressions, conversions, and engagement to evaluate campaign effectiveness.
  • Content Optimization: Analyze visualizations to optimize content strategy, including post types, timing, and platforms.
  • Website Analytics: Visualize website data, such as traffic sources, user behavior, and conversion funnels.


In the future, data visualization is expected to evolve in several ways. Firstly, with advancements in technology such as augmented reality (AR) and virtual reality (VR), data visualizations will become more immersive and interactive, allowing users to explore data in three-dimensional spaces. Secondly, there will be an increased emphasis on real-time visualizations, enabling users to monitor and analyze data in real time, facilitating faster insights and responses. Additionally, as data becomes more complex, advanced visualization techniques like network visualizations and advanced machine learning algorithms will emerge to reveal deeper insights. Lastly, the integration of storytelling and narrative techniques within visualizations will play a significant role in conveying data-driven insights and engaging audiences effectively.

As an expert in analytics, Phygital Insights has the expertise and experience to handle datasets of any size and complexity. Businesses bank on us to create dashboards that are highly communicative and insightful and, at the same time, advanced and well-integrated with modern-day technologies. Contact us to utilize most of your data for success and leave your competitors in the dust!

Article by

John is a seasoned data analytics professional with a profound passion for data science. He has a wealth of knowledge in the data science domain and rich practical experience in dealing with complex datasets. He is interested in writing thought-provoking articles, participating in insightful talks, and collaborating within the data science community. John commonly writes on emerging data analytics trends, methodologies, technologies, and strategies.



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