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What Is Data Visualization? (All you need to know)

In the increasingly data-driven business world, it has become a dominant factor to have an accessible way to gather and perceive data. After all, the constant demand for data skill sets in modern employees has been increasing steadily since the 2000s. So, it’s crucial for an organisation and the employees working in it to understand the impact of data. 

And that’s where data visualisation can come in handy. 

To make business information more understandable and gatherable, data visualisation, in the form of dashboards, has become go-to tool for many people.  

So, let’s find out more about it. 

Data Visualization – The Definition 

Data visualisation is the process or practice of translating data into a specific visual context. It can be formed as a graph or map to make it easier for the human brain to perceive and get more insights accordingly. The primary goal of data visualisation is to help a business identify trends or consumer-related behaviour patterns and outliers.  

However, that’s not the only flexible technology employed in a trading scenario. 

The management department of a company can also leverage it to understand the structure of the organisation and convey its hierarchy to everyone. On the other hand, a data analyst might use it to discover or explain the upcoming trends in the market. 

According to HBR, data visualisation can be used for three different purposes, including – 

1: Idea Illustration

In this case, data visualisation assists in conveying a specific idea, such as a process or tactic. It can also be used to learn a subject, go through a certification course, or understand a tutorial about a topic, consumer behaviour, or something else. 

Many project managers use waterfall and Gantt charts to illustrate the workflow of an ongoing project. Data modelling might also use abstraction to represent the data flow within an organisation and understand the relationship between a warehouse and a database. 

2: Idea Generation 

Data visualisation is commonly employed to spur new ideas and strategies across teams. They are primarily leveraged during a design thinking or brainstorming session at the beginning of the project by supporting a diverse range of perspectives. 

Data visualisation in this phase will be pretty unrefined and unpolished. But, it can establish the cornerstone within the project to keep your team well-aligned about an issue you might encounter later on. Maybe you’ll find a way to address it as well. 

3: Visual Discovery 

Data visualisation, if done correctly, can also lead to visual discovery. It, in turn, might ensure better idea generation, adoption of insights, and much more. Visual discovery can also aid you in identifying trends and patterns within a dataset.  

Visual discovery can also be a critical step in a data science process, as it can help you convey business-related data to decision-makers and colleagues effectively. 

Note: Besides, data visualisation can also be beneficial during text mining. The analyst will use a word cloud to capture trends, key concepts, and hidden relationships in this aspect. 

Alternatively, they might use a specific graph structure to illustrate different relationships between entities to learn more about your organisational needs and targets. 

Types of Data Visualization 

The earliest usage of data visualisation dates back to the 17th century when it was employed to assist with navigation. However, as time progressed, we’ve essentially found a new way or two to use it more broadly. For example, currently, it’s being implemented in – 

  • The economic industry, 
  • Social environments, and 
  • Health disciplines. 

Depending on the business segment you are working on, the application of data visualisation will change prominently. Here’s what you may choose from – 

1: The Table Form 

This type of data visualisation technique may feature columns and rows to create and compare different variables. It can offer the data in a more structured manner. However, if too much information is available, making a comparison might get a bit overwhelming. 

2: Area and Line Charts 

These visuals show more than one quantity change by plotting a series of information over time. Due to this reason, it’s primarily used in the segment of predictive analysis. You can use a colour or two to differentiate between the available data and make it easier to perceive here. 

3: Stacked Bar and Pie Charts 

This type of graph will be divided into sections representing parts of a project. This, in turn, can make it easier to organise the available information and compare one component to another. The better you do it, and the more accurate the results will be. 

4: Scatter Plots 

A scatter plot will be beneficial in revealing the core relationship between two options or variables. And they can also be used within the regression of data analysis.  

Nonetheless, it might sometimes be confused with a bubble chart, which is used to visualise three different variables: the bubble’s size, the y-axis, and the x-axis. 

5: Heat Maps 

This can be used on a place available on a map, on Google’s page, or maybe even the webpage of an organisation. It’s ideal for visualising behavioural data, especially of a consumer group, by their geographical location. A heat map usually promotes the usage of extensive colouring. 

The Two Sides of the Data Visualization Coin 

Data visualisation can be beneficial and disadvantageous for an organisation, even though it may not seem so. Please keep reading to know more about it. 


Most people usually use data visualisation to understand specific data efficiently. However, that is not where its usage ends. There is a lot more to explore in this aspect. 

  • With data visualisation, you can quickly create an interactive and intuitive visual representation of something. Thus, analysing the complex dataset available within it will be much easier and more accurate. It can also help you find key insights instantly. 
  • In business intelligence, it’ll be essential for you to find a specific correlation and comparison prospect between market performance and business functions. Owing to the application of data visualisation, you’ll be able to easily track the connections above while ensuring the growth of your organisation. 
  • Through data visualisation, you can dig deeper into the sentiments of the consumer base you’re targeting and present the analysis on an interactive report or a chart. It will help a person understand their audience’s pain points and preferences perfectly and ensure that everyone is treated the same way. 

Data visualisation, if used correctly, can also identify the presence of a trend and ensure that an organisation is addressing it properly. The accuracy of the insights offered by this technology is also ideal in the context of coming up with a new marketing vision. 


Even though it’s quite accurate, data visualisation usually works speculatively. Thus, the data provided by the same can sometimes be biased and inaccurate. Apart from this – 

  • It also cannot assist in any business-related data eminently. Hence, a different group might decipher a piece of data differently from you. 
  • The basic information offered by data visualisation can sometimes be a little vague. So, if you misinterpret it, the outcome won’t be as beneficial as you may please. 
  • Due to recent technological development, data visualisation’s core design tends to be improper too. This, in turn, can lead to communication confusion. 

Honestly, the disadvantages of data visualisation can be pretty minor and repetitive. Therefore, if your business has an efficient analyst working for you, it won’t be much of an issue. 

Best Practices for Data Visualization 

Data visualisation, in all honesty, is a form of art. However, no matter what or how you want to apply it, your goal should always be about offering valuable insights and information. 

Here’s how you can do it. 

1: Define Your Purpose 

Like any other project associated with data analytics, it’ll be essential for you to define a clear goal or purpose for your data visualisation. So, while you’re at it, try to find answers to – 

  • What is the priority of the subject you are trying to communicate and convey? 
  • What should your consumer base take away from the core visualisation? 

When visualising data, it’s always essential to have a defined outset for your target audience and the information you want to gather. That way, you’ll be able to get valuable data through market research and offer your audience something that can solve their problem. 

2: Keep It Easy and Simple 

When creating a visualisation, you should also try to cut down the data as much as possible. Ultimately, you’ll want everyone else to understand your message. 

Therefore, it’s better to trim down the unnecessary information as much as you can and present the key insights clearly. The goal here is to reduce your cognitive load as much as possible. 

3: Learn about Your Audience 

The purpose of using data visualisation is to communicate plans and insights about your target audience. Hence, it might be better to take some time and do research on them. For example – 

  • Which context might fit better to understand your audience? 
  • What form of visualisation will be more accessible to the targeted people? 

Keep your audience in mind always, and ensure that you focus on their pain points or problems. Nothing else. 

4: Inclusive Visualisation 

When visualising something, always think about how colour can affect or uplift the message you want to convey. In this case, the use of font sizes, contrasts, as well as the use of white space will matter a lot as well. To perfect your assessment, ask yourself – 

  • Do you think the data will be distinguishable for visually-impaired people and people with twenty-twenty vision? 
  • Does the font size seem ideal enough to convey a piece of data perfectly? 

Accessibility and inclusivity will be the central notion of any data visualisation. Hence, there’s no need to overlook this step. 

5: Don’t Distort the Data 

You must always strive to offer your findings accurately when working in a business scenario. Hence, avoiding any trick that may distort the information you want to convey is important. 

Think about the labels you’re trying to use – and how well your data might be interpreted and perceived. For instance, aspects like ‘blowing up’ some specific data segments may make them appear more significant than anything else. 

Starting the graph axis on a number apart from zero is a bad practice and might mislead most of your audience. So, make sure to prioritise accuracy and integrity as much as possible. 

The Bottom Line 

At this point, you probably already know how important data visualisation can be for your business. Thus, it might be best if you started strategising about it and worked in accordance with it. Good luck!