Why Is Data Visualization Important and How Can It Improve Business Efficiency?

As businesses increasingly rely on the data for decision-making, presenting complex information in a clear and understandable manner is crucial. Interpreting datasets into actionable insights is not an easy task and requires the help of professionals. This is why data visualization consulting services have become essential for companies that want to use the data they gather effectively. 

However, why is data visualization important? In our article, we will discuss its benefits and applications. 

The growing importance of data

The amount of information generated every day is humongous. According to Statista, the total volume of data in the digital world is predicted to exceed 180 zettabytes by 2025. These numbers represent everything from personal information to scientific research and global trends. The information comes from various sources, like IoT devices, online interactions, social media activities, sensor data, and more. For businesses, this data is a treasure trove of insights that can drive decision-making, enhance customer experiences, and foster innovation. 

However, this wealth of information also brings challenges. As much as we celebrate data generation, we must also acknowledge the complexities that accompany it.

Challenges of raw data

Raw data is the one that has not been processed after collection and may contain information of various formats. That means, a set of raw data may include text, numerical values, images, or audio. So if an organization wants to make use of this data, it either needs to process it first or bring it to an understandable format, with both options requiring a significant amount of time and effort.

Another big issue is data overload since many organizations do not have tools or resources to properly analyze and process the generated data.

Other challenges of raw data include:

• Data quality issues. Raw data can contain missing or inconsistent values, corrupted data, duplications and errors, which makes it difficult to draw reliable conclusions from it. That, in turn, can lead to misguided decisions.
• Complexity. Data can come in various formats: structured, semi-structured, and unstructured. Therefore, you need to use various analytical tools and techniques to make use of it. 
• Skill gaps. Many organizations do not have in-house data science experts or are unwilling to assemble a data science team to assist with their data processing tasks. So, they miss opportunities to make use of all the information they hoard and gain market advantages with data-driven decisions. 

Considering these challenges, it becomes clear that organizations must find ways to transform raw data into something that is understandable and actionable. This is where data visualization steps in as one of the best options. 

What is data visualization?

Data visualization is the process that uses visual elements to display the data in a clear manner. These elements include:

• Charts (bar charts, line charts, pie charts);
• Plots (scatter plots, bubble plots, box plots);
• Maps (heatmaps, dot distribution maps, cartograms);
• Diagrams and matrices (hierarchical diagrams, correlation matrices, etc);
• Dashboards.

So how does data visualization relate to raw data? The thing is, raw data can sometimes be visualized immediately without preprocessing. For instance, sensor data from IoT devices or transactional data can be directly plotted to show trends or patterns. 

The importance of data visualization

Data visualization has a broad use across different fields, including education, business, finances, healthcare, science, marketing, and social studies. It helps to see trends and connections that may not be obvious in other formats, so it’s easier to make confident data-driven decisions and predictions. 

Here are some of the biggest data visualization benefits: 

• A well-designed infographic quickly conveys key insights, statistics, and trends and makes it easy to understand;
• It makes it easier for potential investors to grasp the value of a project at a glance;
• Visuals such as charts and graphs highlight growth projections, market size, and competitive advantages;
• Investors can easily see the potential for return on investment without navigating through dense text;
• It helps to illustrate factors that can influence customer behavior;
• Visualization simplifies the information and makes it more memorable.

And that’s not all of the ways of how data visualization helps with information challenges. Therefore, let’s explore other reasons for the importance of data visualization.

Enhances decision-making

Data overload can impact decision-making since it’s challenging to analyze massive amounts of data. This is where charts, graphs, and infographics shine. Data visualization allows decision-makers to see patterns, trends, and outliers at a glance. Moreover, it’s not necessary to analyze all the data you have; you can visualize the most relevant parts to get insights quickly. 

For instance, a line graph depicting sales trends over the last 12 months can immediately highlight seasonal fluctuations or unexpected spikes, which might be buried in raw data. 

A good real life example is Netflix. It uses data visualization to analyze viewing habits, preferences and trends among its subscribers. By representing this data visually, Netflix can identify what genres are gaining popularity, leading them to make informed decisions about content creation.

Faster decision-making

You need to think fast and make quick decisions to stay ahead of the competition in the modern fast-paced business world. It’s especially important to industries such as finance, marketing, and logistics, where timely decisions can significantly impact profitability and efficiency. Data visualization helps to reduce the cognitive load of information and speed-up decision making. Research shows that the human brain processes visual information much faster than text or numbers. So, turning data into graphs or charts will help stakeholders to see the big picture at a glance, not wasting time on analyzing tables and spreadsheets.

For instance, in the retail industry, visualizations can help store managers quickly identify which products are selling well and which ones are lagging behind. With this information, managers can make immediate decisions on reordering stock or adjusting promotional strategies without having to go through complex data reports. 

Simplifies complex data

As we already mentioned, data visualization is all about clarity and simplicity. Businesses often work with complex datasets that contain multiple variables such as time, geography, demographics, and etc. Data visualization helps to combine these different variables and show them in a single visual. It helps to better understand and analyze the information to make more informed decisions. 

For example, a heatmap can represent sales performance across different regions and time periods, with color intensity indicating the volume of sales. This allows viewers to quickly grasp which regions are performing well and which are lagging, all in one glance.

Visual storytelling

Going through long reports filled with text and tables of numbers can be tiring. There’s also a chance you can miss important details if you decide to skim through the content. Visual elements make presentations both engaging and clear, helping convey the message more effectively. Just like stories are easier to remember with pictures, data visualization brings complex information to life through colorful visuals.

People are more likely to recall a story than a dry report. By using graphs, charts, and maps, you can create presentations that are both impactful and memorable. 

Identifies trends and patterns

One of the undoubted data visualization benefits is that it makes it easier to recognize trends in data that might not be evident at first. It’s easier to see the patterns when you have the information in a visual form than when you read through it. By presenting data in graphs, lines, pie charts or other visual formats you minimize the risk of misinterpreting it.

To be able to timely recognize patterns and trends is especially important in healthcare and finance spheres. For instance, visualizing patient data can reveal trends in disease outbreaks, patient recovery times, or treatment efficiency. This enables doctors to take quicker, more informed actions, improving patient outcomes and preventing issues from escalating.

Predictive analytics

Data visualization is often combined with machine learning models to help present complex analytical outcomes in a more straightforward visual format. Predictive analytics employs machine learning and statistical algorithms to analyze large datasets of historical data and make accurate future predictions. Organizations across various sectors use this method to assess risks, optimize operations, and develop effective strategies based on the insights. Visual elements such as charts, graphs, and maps highlight the connections and patterns in data, which is essential for making accurate future forecasts. 

Interactive dashboards

The use of interactive dashboards has become increasingly popular in both corporate and academic settings. These boards allow users to interact with data in real-time, adjust variables, and view the information from different angles. 

For instance, Walmart uses real-time visual dashboards to enhance its inventory management system. Through the dashboards, users can track product sales and inventory levels across its vast network of stores. 

Besides, interactive dashboards became essential tools for visualizing and communicating data during the COVID-19 pandemic. They were widely used across various sectors to track the spread of the virus and monitor public health responses. 

A notable example is Johns Hopkins University COVID-19 dashboard. It pulls data from trusted sources like the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), using maps, charts, and graphs to make complex information easy to understand. The dashboard provided real-time updates on COVID-19 cases, deaths, and recoveries worldwide. It greatly helped to raise public awareness and understanding of the pandemic, empowering people to make informed decisions about their health and safety. 

Improves communication across teams

A significant challenge in modern organizations is the communication gap between technical and non-technical teams. Data analysts, engineers, and IT professionals often work with complex data models and algorithms that can be hard for others to understand. Data visualization bridges this gap by translating technical data into accessible visual formats.

Data democratization

When employees can independently access and analyze data, they can make quicker, more informed decisions. Data visualization facilitates this process; you don’t need to be a data scientist to understand a graph or chart. For example, marketing teams can use visualization tools like Tableau or Power BI to explore customer data without relying on data analysts. This easy access allows teams to experiment with data, test ideas, and discover insights independently.

Collaborative decision-making

In cross-functional meetings, data visuals provide a common ground for discussion, enabling team members from different departments to interpret and contribute to the decision-making process. Visuals enhance engagement with the data, allowing everyone to share insights and align on key priorities.

Increases engagement and retention

Data visualization is not just a tool for internal use — it’s also a powerful method for engaging external audiences, such as customers or stakeholders. Visual content is inherently more engaging than text-heavy formats. Bright colors, dynamic images, and interactive elements capture attention and maintain it longer. 

For instance, social media platforms like Instagram and TikTok thrive on visual storytelling, where users are drawn in by eye-catching images and videos. Brands that leverage these platforms often find that their visual campaigns resonate more deeply with their audiences, leading to increased shares, likes, and conversions.

Things you need to consider

While data visualization can significantly improve efficiency and communication, to make a comprehensive visualization, you need to consider the following factors:

• Choose the right chart. The type of chart or graph that you choose can impact the clarity of your data. For example, a pie chart may work for representing proportions, but a line graph might be better for tracking changes over time.
• Avoid clutter. Simplicity is the key in data visualization. Too much information or overly complex visuals can be confusing and not helpful. So, it’s important to find a balance between clarity and detail.
• Know your audience. Always tailor your visuals to the audience according to their knowledge level. Ensure that they are familiar with the data interpretation. The goal is to make the data both accessible and relevant for any audience.
• Use reliable sources. Verify the data thoroughly to prevent sharing misleading information. Cross-check facts with multiple reliable sources and ensure accuracy before presenting or using the data. It will help maintain credibility and avoid potential errors or misinterpretations.

Conclusion

Now that we’ve answered the “what is the purpose of data visualization” question, it should be clear that it is a highly effective way to present information. It transforms raw data into simple visuals like charts and graphs, helping audiences quickly grasp valuable insights.

Data visualization not only makes information easier to understand but also highlights patterns and trends that might be missed. This shows why data visualization is important: it helps people and organizations find useful insights, improves communication, and supports better decision-making. To maximize these benefits, you can always turn to a trusted IT provider to ensure you’re using the right tools and strategies. SoftTeco offers comprehensive data visualization consulting services to help organizations select the most suitable data visualization tools and set up the necessary processes.