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Data Visualization:- Principles and Best Practices for Communicating Data Insights

By Manas Kochar Category Data Science Reading time 4.55 mins Published on Nov 27, 2023

4 Most Helpful Practices to Master Your Data Visualization Skills

Consider a situation where you made a striking discovery that could alter industries; however, you cannot communicate its importance to the audience. As others might have a different level of domain knowledge than you, it becomes essential to find a way to present your findings so that it is easy-to-understand.

Similarly, data science requires you to visualize your insights to create effective communication between teams. A data scientist fulfills many roles within an organization, one of which is Data Visualization. Being able to communicate data insights to stakeholders and other departments is an important skill.

Data visualization is a way that helps present insights gained from data in a visual form. Based on their attributes, we can represent the data insights through graphs, lines, charts, and other formats. By understanding the best practices for data visualization, you can develop a better sense of the data.

Data visualizations must be helpful, visibly attractive, and not deceptive. When working with huge data sets, creating an organized format for data helps build useful and engaging visualizations.

But why do we need data visualization? And how does an online master's degree in data science help? Let’s find out.

Need for visualizing data

An image suggest the use of visualizing data with graphs, bar, pie chart etc.

We produce massive amounts of data online every day. There are many zettabytes of raw data available already. Data scientists can understand and analyze this data using their specialized skills. However, to simplify their data insights for people not skilled in data science, they require data visualization skills.

Visualizing data is essential in fields ranging from science to economics to healthcare. Hence, we turn complex data into graphs and charts that help easily picture them in a simpler form.

Here are some of the best practices for data visualization that make your visualizations more coherent and clear.

1. Establish your goals

Clearly identify the intentions behind your visualizations beforehand. Take some time to set goals and priorities before you start visualizing for better outcomes. Your goals can decide the design choices and create visualizations that target the right audience.

You can start by considering the following:

  • What data and facts to present before the audience?
  • The audience must be able to take specific action or build a particular viewpoint.
  • The visualization should allow them to act on the data and discover new insights.

Do not make the visualization very much detailed, and too little must also not be provided. Try to decide how much information is required to properly convey the insights to others.

2. Audience comes first

Visualizations are only useful if they clearly communicate the design to the audience. You must keep in mind the target audience’s knowledge and tailor your designs accordingly. You should learn how to communicate effectively your visualizations to an audience.

You may ask questions like:

  • How will the audience understand and use your insights?
  • How will the audience react to the graphs – will they ignore them or ask questions?
  • What consists of the audience – are they a particular group of people, or are they key decision-makers within the company?
  • What level of knowledge does the audience have about the STEM field?

As everybody looks at a picture with a different lens, knowing as much about your audience according to the goal is helpful.

3. Choose correct visuals

After considering the data you need to convey and your target audience, you can choose the right visual features to add to convey your message. The correct visuals should make the data easily accessible and present it accurately.

You may analyze the data to decide on the trends, patterns, or relationships to highlight. Also, keep in mind what type of data you are working with, like time series, categorical, hierarchical, or numerical.

Most of your visualizations will consist of numbers and charts. Here are some types of data visualization you can select from to convey your insights.

  • Area charts
  • Pie charts
  • Line charts
  • Scatter plots
  • TreeMap
  • Bar charts
  • Heat maps

4. Build coherent and organized representations

An image shows an organized representation using data with pie chart, graphs, bargrpash etc.

When working with large data sets, coherence helps a lot. Your design should properly align with the background, so users can easily process the data. Also, creating priority data displays the different data points relevant to key decision-makers. You can show the highest to lowest or create categories to represent the priorities of the users.

Moreover, a chart’s order, colors, or even element size can represent data more easily. Your visualization should clearly define which data should be focused on and which is less important. You must study the proper way to visualize data using the best practices for data visualization.


Data visualization includes telling a story via data. To make a compelling story from your data, you must first understand the audience. An effective visualization shows a narrative based on which the audience can comprehend your message.

You can create a visually appealing visualization with the right data visualization tools, visual representations, clear goals, and audience knowledge. To help you understand and improve your visualization skills, you must have knowledge of the best practices for data visualization.

Many online programs offer top-notch methods and tricks to help learners become proficient in visualization. Not only that, an online master's degree data science will help you learn other essential skills as well, such as communication, data analysis, and more.

The master’s in data science and AI program offers the necessary knowledge to become an expert data scientist. Through their expert mentorship, you can gain basic and advanced level skills. The program helps working professionals progress their careers forward to more advanced positions such as managers.

The data science master’s degree offered in the program is globally recognized. The degree can help you apply for jobs abroad in countries like Europe or the US. You also get IBM and Microsoft course completion certificates and IBM project experience certificates.

The curriculum is expertly designed and helps provide flexibility. You can attend the program in hybrid mode (online and offline) based on your availability.

Learners can opt from the many domains from healthcare to finance to apply their learnings. The institute also offers them the chance to gain interviews with top MNCs. The Career Service Pro feature helps you through mock interviews, resume preparation, reviews, interview calls, and more.

Frequently asked questions

1. Explain the five data visualization techniques.

Ans. Different kinds of data visualization techniques are present. Some common ones include:

  • Matrices
  • Maps (cartograms, dot distribution maps, heat maps, etc.)
  • Diagrams
  • Plots (box, bubble, scatter, etc.)
  • Charts (pie, line, bar, etc.)

2. What are the 3 uses of data visualization?

Ans. The usage of data visualization can be broken down into three major categories:

  • Exploring
  • Monitoring
  • Explaining

3. Explain data visualization tools?

Ans. Software that develops data in a visual manner, like charts, graphs, heat maps, and more, is known as a data visualization tool. These help mainly in data analysis. They turn massive amounts of data into an easily readable and workable format.