We are living in the age of big data. Organizations today are swimming in massive volumes of data being generated from an ever-expanding variety of sources. However, data alone is not enough – it needs to be translated into insights in order to drive better decision making. This is where data visualization comes in.
Data visualization is the graphical representation of information and data using visual elements like charts, graphs, and maps. Effective data visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable.
With the explosion of big data in recent years, visualization has become a critical tool for making sense of it all. It enables users to explore large datasets interactively, identify trends and outliers, and communicate insights in a compelling way.
The good news is that there is no shortage of powerful data visualization tools available today, ranging from drag-and-drop tools for non-technical users to advanced programming libraries for developers. In this post, we‘ll take a look at 30 of the best data visualization tools to consider in 2021, with a particular focus on those designed to handle big data.
No-Code Data Visualization Tools
First, let‘s look at some of the top data visualization tools that enable non-technical users to create interactive charts, dashboards and visual stories without needing to write any code.
Charts and Graphs
If you‘re looking to create basic charts and graphs, these tools have you covered:
Tableau: The market leader in self-service BI, Tableau‘s intuitive drag-and-drop interface makes it easy to explore and visualize data. Tableau is particularly well-suited for big data, with native connectors for popular big data platforms.
Microsoft Power BI: Microsoft‘s business intelligence platform offers similar functionality to Tableau. Its deep integration with other Microsoft products makes it a popular choice for many organizations.
Google Data Studio: Recently out of beta, Google Data Studio is a free, easy-to-use dashboarding and reporting tool. It integrates seamlessly with Google‘s marketing and advertising products.
Qlik Sense: Qlik pioneered the in-memory analytics category and Qlik Sense continues that tradition, standing out for its associative data indexing engine.
Sisense: Sisense is a robust platform for creating interactive dashboards, known for its ability to handle complex data models.
Infographics
Going beyond basic charts, these tools are optimized for creating professional-looking infographics:
Infogram: With an extensive library of templates and design assets, Infogram is one of the most popular tools for creating engaging infographics, reports, and social media visuals.
Piktochart: Piktochart offers a simple, block-based editor for designing infographics. It‘s also useful for making posters, flyers, and presentations.
Venngage: Venngage provides a wide selection of themed infographic templates for different industries and use cases, such as timelines, comparisons, and data visualizations.
Easel.ly: Easel.ly has thousands of free infographic templates and design elements, making visualization accessible for all skill levels.
Dashboards
For pulling multiple data sources and visualizations together into interactive dashboards, check out these tools:
Databox: Databox enables you to build KPI dashboards that pull data from various source to provide a unified, real-time view of business performance.
Domo: Domo is an all-in-one business management platform known for its powerful yet user-friendly visualization and dashboarding capabilities.
Geckoboard: Geckoboard focuses on building live TV dashboards for displaying key metrics and performance data on large screens.
Klipfolio: Klipfolio is another popular tool for building interactive real-time dashboards, with a special focus on integrating multiple data sources.
Mapping and Geospatial Visualization
For visualizing location-based data and creating custom interactive maps, consider these leading tools:
Mapbox: Mapbox is developer platform used by many leading brands to integrate custom maps and location services into their websites and apps.
CARTO: CARTO provides geospatial analysis, visualization, and data management tools, with a user-friendly interface for non-developers.
ArcGIS: A leader in GIS software, ArcGIS enables users to create interactive maps and apps and perform advanced geospatial analytics.
Leaflet: Leaflet is one of the most popular open-source JavaScript libraries for building interactive maps. It‘s lightweight, mobile-friendly, and easy to customize.
Developer Tools and Programming Libraries
For developers and data scientists looking to build custom data visualizations from scratch, there are a variety of open-source libraries and frameworks to leverage.
JavaScript Libraries
JavaScript is the go-to language for creating custom web-based data visualizations. Here are some of the top libraries:
D3.js: Short for Data-Driven Documents, D3.js is the most powerful and flexible library for data visualization on the web. It‘s not the easiest to learn, but its capabilities are unmatched.
Chart.js: On the other end of the spectrum, Chart.js is a beginner-friendly library for creating basic HTML5 charts using the canvas element.
Highcharts: Striking a nice balance between ease-of-use and customization, Highcharts is a popular JavaScript charting library used by many major companies.
amCharts: amCharts is another established name in the JavaScript charting space, offering a wide variety of chart types and themes.
Python Libraries
For data scientists using Python, some of the essential data visualization libraries include:
Matplotlib: The oldest and most widely used Python visualization library, Matplotlib is highly customizable and works well for basic charts and plots.
Seaborn: Built on top of Matplotlib, Seaborn simplifies many common visualization needs and provides additional chart types and themes.
Plotly: Plotly‘s Python library provides a high-level interface for creating interactive web-based visualizations that can be easily shared and embedded.
Bokeh: Designed for real-time streaming and large datasets, Bokeh is a powerful library for building interactive dashboards and data applications.
Big Data Visualization
When dealing with truly massive, petabyte-scale datasets, you‘ll need specialized tools designed for big data visualization:
deck.gl: Created by Uber, deck.gl is a WebGL framework for visualizing huge datasets (millions of data points) in the browser.
Superset: Developed by Airbnb, Superset is a cloud-native BI tool providing fast, interactive visualizations on large datasets.
Kibana: Part of the popular Elastic Stack, Kibana is an analytics and visualization platform for exploring log and metrics data in real-time.
Grafana: Grafana has become the leading open-source tool for visualizing time series data for infrastructure and application monitoring.
Specialized Visualization
There are also a variety of tools designed for specific data visualization use cases:
RAW: Built on D3.js, RAW makes it easy for designers to create custom vector-based data visualizations.
TimelineJS: TimelineJS is an open-source tool for building interactive, visually-rich timelines and chronologies.
Gephi: Gephi is a powerful open-source platform for visualizing and analyzing large network graphs and complex systems.
Leaflet: Already mentioned above, Leaflet deserves another mention as the go-to library for visualizing geospatial data and mapping.
Emerging Trends in Data Visualization
As data visualization evolves to keep up with the growth of big data, there are a few key trends shaping the future:
AI-Powered Visualization: Artificial intelligence is increasingly being used to enable advanced capabilities like automatic chart/graph recommendations, detection of relevant patterns and insights, and conversational interaction via natural language.
Immersive Experiences: There is growing interest in immersive data visualization and storytelling using augmented reality (AR) and virtual reality (VR) to provide multi-sensory and highly engaging experiences.
Collaboration and Democratization: With data visualization becoming a team sport, new tools are focusing on enabling seamless collaboration, sharing, and democratization of data across the organization.
Choosing the Right Data Visualization Tool
With so many tools to choose from, how do you pick the best one for your needs? Here are a few key factors to consider:
- Your technical skills (coding vs drag-and-drop)
- Types of visualizations needed (basic charts vs advanced/custom)
- Data management needs (size, speed, location of data)
- Collaboration and sharing requirements
- Budget and pricing model
It‘s often useful to start with a free or low-cost option that meets your basic needs and then graduate to a more advanced tool as your needs evolve.
Data Storytelling Best Practices
Regardless of which tool you choose, it‘s important to follow data visualization and storytelling best practices:
- Understand your audience
- Focus on key insights and takeaways
- Keep it simple and clutter-free
- Use the right chart for the data and message
- Make it interactive when possible
- Provide context through labeling, annotation, and narration
- Iterate and get feedback
Effective data visualization is really about effective communication. The tools are continually evolving, but the principles of great data storytelling remain constant. Master those, and you‘ll be able to bring your data to life and drive real impact for your business.