Data visualization is an increasingly popular way to gain valuable insights from data. It is a great tool for uncovering trends, patterns, and correlations that are not easily spotted in a long list of raw numbers. However, it can be difficult to know where to start when it comes to choosing the right data visualization tool.
To make this easier, we’ve created a list of the best data visualization tools available. The list provided has been broken down into eight key categories, each with its own set of benefits.
Desktop applications
First up is desktop applications, which are the most popular software solutions for data visualization. They allow users to generate interactive, intuitive visuals from large datasets. Popular desktop applications include Tableau, Microsoft Power BI, and Grafana. Each of these programs offers a wide range of features and customization options, allowing users to create a highly tailored experience.
Web-based tools
Next is web-based data visualization tools, which are designed to run in a web browser without the need for any additional software. Web-based tools provide a great deal of flexibility, as they can be accessed from any device with an internet connection. Examples include Google Charts, Plot.ly, and Highcharts.
Customizable applications
Third, there are customizable solutions that are tailored to the user’s individual needs. These solutions allow for a more complex level of data visualization, with features such as customizable colur palettes and interactive user interfaces. Popular examples include D3.js and Chart.js.
Open-source tools
Fourth is open-source data visualization tools. These are open-source solutions that are freely available online. Open-source solutions tend to be more demanding on resources, but can produce extremely detailed visuals. Popular open-source solutions include R, Matplotlib, and Bokeh.
API services
Fifth is visualization API services, which are designed to allow developers to quickly and easily embed data visualizations into applications. These services provide a high level of control over the visuals, as well as APIs that allow users to update their visuals in real time. Popular examples include Google Maps and Tableau Public.
Comprehensive tools
Finally, there are data analytics platforms that offer comprehensive data visualization capabilities. These platforms provide a range of features, such as predictive analytics, machine learning, and natural language processing. Popular examples include Splunk and Qlik.
Whichever type of data visualization tool you choose, it’s important to consider how it will fit into your workflow and how it will help you gain valuable insights from your data.
Now that we’ve talked about the different data visualization tools that are available, let’s talk about how to make good use of them.
How to get started with data visualization tools
1. Setting up the data visualization tool
To get the most out of your data visualization tool, it’s important to set up the environment before you start exploring the data.
First, you’ll need to have the correct software installed and set up on your computer. It’s important to choose one that best suits your data, your skillset, and your budget. Consider researching different tools and weighing up their features to help you make an informed decision.
Once you’ve chosen the software, you’ll need to connect it to your data source. This could be a database, an Excel spreadsheet, or an external data file. Depending on the data format, you may need to convert it into a format that is compatible with the visualization tool.
Once the data is ready, it’s time to start exploring. Different data visualization tools may have different features, such as interactive charts, heat maps, and 3D visualizations. Utilizing these features can help you uncover valuable insights from the data.
Finally, you’ll need to be mindful of the size of the data set. If the data set is too large, the visualization tool will struggle to display it, resulting in a slowed-down user experience. Consider reducing the data size, such as through filtering or sampling, to ensure smooth performance.
By setting up the data visualization tool correctly, you’ll be well-placed to start exploring the data and uncovering valuable insights.
2. Designing your visualization
Designing your visualization is an essential step in uncovering valuable insights from your data. A well-designed visualization can help to summarise, clarify and interpret data, as well as facilitate better decision-making. Here, we will discuss some of the key considerations for designing a successful visualization using data visualization tools.
When designing a visualization, it is important to consider the goal of the project and the audience who will be viewing the visualization. Different visualization tools offer different features and capabilities, so it is important to select a tool that meets your specific needs. Some tools are designed to help create complex visualizations quickly and easily, while others focus on data exploration and analysis.
The structure of the data is essential in the design of an effective visualization. It is important to think about how to best represent the data in a visual format. For example, simple charts and graphs can be used to represent numerical and categorical data, while maps can be used to visualize geographical data. Additionally, it is important to think about how to display multiple variables in a single visualization.
It is also important to consider the data sources when designing a visualization. Different data sources can have different levels of detail, accuracy, and relevance. In some cases, combining multiple data sources may be necessary in order to create a consistent visualization.
You should also carefully consider the color palette for your visualization. Different colors can have different meanings, so it is important to choose a color palette that is suited to the context and purpose of the visualization. You can also use color to draw attention to key points in a visualization, while also making it look aesthetically pleasing.
Finally, the design of a visualization should be user-focused. It is important to design a visualization that is easy to understand and interact with. This means considering the interface elements such as navigation, search boxes, labels, and legends. Additionally, the size of the fonts and the layout of the elements should be kept in mind in order to make the visualization as clear and concise as possible.
3. Interpreting your visualizations
Interpreting the data visualizations you have created is one of the most important steps in uncovering valuable insights from your data. There are a variety of factors to consider in order to get the most out of your visualizations.
The first step is to understand all the features of the visualization. These features can include labels, axes, data points, and interactive elements that can help you gain a better understanding and appreciation for the relationship between the data elements. Each visualized element can be used to uncover and interpret data relationships, trends, and outliers.
When looking at visualizations, the most important piece of information to consider is the context in which the visualization was created. It is important to understand both the context of the data and the context of the visualization. For example, if a visualization is contextualized to a specific goal, such as tracking customer retention, the visualizations should be interpreted in the light of that goal. Likewise, if the visualization is related to a certain market, understanding the market trends and conditions can help inform your interpretation.
It is also important to pay attention to the data points that stand out from the rest. You should examine any data points that are outliers and do not fit the rest of the data more closely. This will help you understand their implications and potential impact on the visualization and the overall goal.
Finally, it is important to bear in mind that data visualization is just one tool of many for reaching insights. When interpreting your visualizations, it is important to take into account any other data sources that may be available. These can include interviews, surveys, or other sources of qualitative data. Combining qualitative and quantitative data sources can provide additional context and understanding to help ensure that the insights gathered are meaningful and valuable.
By considering all of these elements and understanding how they interact, you can make the most of your data visualizations and uncover valuable insights.
4. Fine-tuning your visualizations
Once you have chosen the right type of data visualization tools, you can start to refine your visualizations. You want to make sure that all the information contained in the visualizations is easy to understand and interpret. Here are some tips to help you perfect your visualizations.
Get rid of any unnecessary elements
You should remove any information that does not add value to your data visualization. This will make the visualization more effective and easier to follow.
Make sure the visualizations are legible
The audience should be able to read the text and understand the data without having to zoom in or squint. Make sure the text size, font, and color contrast are appropriate for the audience.
Utilize the data hierarchy
Group similar data together and use colors, size, and position to differentiate between different data points. This will make it easier for the audience to identify related information.
Incorporate some design elements
Design elements such as lines, shapes, and labels can help to create a more aesthetically pleasing visualization. It can also help to draw the audience’s attention to particular data points.
Highlight data patterns
Highlighting patterns in data can draw attention to important insights and identify trends in the data. These can be further emphasized with the use of color or shape.
By following these tips, you will be able to fine-tune your visualizations and make the most of your data visualization tools. This will help you uncover valuable insights in your data that you may otherwise have missed.
5. Incorporating feedback and making edits
One of the benefits of using data visualization tools to extract meaningful insights is the ability to easily make edits and incorporate feedback. After you have established the desired visualization and generated your insights, the next step is to review the visualization and make the necessary edits to ensure accuracy and understanding.
To effectively incorporate feedback in your data visualization project, it’s important to first decide who you are hoping to receive feedback from. Depending on the nature and purpose of the project, it may be useful to reach out to colleagues, customers, or stakeholders for their opinion. Having input from multiple perspectives can help to identify any potential issues or inaccuracies you may have missed.
When making edits to the visualization, look for areas that could be improved, evaluated, or clarified. Data visualizations should always be concise and easy to understand. For this reason, it’s worthwhile to take the time to make sure all labels, annotations, and titles are informative, concise, and accurate. Additionally, colors should be used judiciously to ensure they are meaningful and easily distinguishable.
It is also important to consider the data visualization platforms available and determine which one is most suitable for your project. Different data visualization tools provide different features, so exploring different platforms can help you to find one that best fits your needs.
By taking the time to incorporate feedback and make edits, you can ensure you have an accurate and effective data visualization that can be used to uncover meaningful insights.
6. Final touches and exporting your visuals
By now, you should have your data visualization all set up and ready to share. However, before you go ahead and share it, there are a few final touches you should consider making to ensure it looks its best.
The most important step to ensure that your visualization looks great is to make sure your data is clean. Check for any outliers or spikes in your data, as these can give off a false impression. You may also want to use a color-coding system to make your visualization easier to read and interpret.
Next, consider adding a few annotations or labels to your visualization. Doing this will help you explain the story behind your data, such as what the different bars or lines represent. You may also want to add a key or legend to make your visualization even easier to interpret.
Finally, you’ll want to consider any formatting changes you may want to make to your visualization. For example, you can adjust the axes and label placement, or add gridlines to make your visualization easier to read. You may also want to adjust the font size or color of your labels.
Once you’ve made all your desired formatting changes, you’re ready to export your visualization. The most common file type for data visualizations is a .png or .jpg, as these are easy to share and view on the web. However, if you want to keep the formatting of your visualization, you may want to export it as an .svg.
Now that your visualization is all set up and ready to go, you can share it with the world!
Conclusion
Data visualization is a powerful way to uncover insights in large datasets that would normally remain hidden. By following the steps outlined in this guide, you can use data visualization tools to draw meaningful conclusions from raw data while helping others do the same. Data visualizations can help you make better decisions, tell more relatable stories, and make sense of complex data.