Observable

When D3 was first released in February 2011, it transformed how we build data visualizations for the web and ushered in a new age of unbridled creativity in information design, data journalism, science communication, and beyond. Today, it remains the backbone of modern, interactive data visualization.

Happy 15th birthday, D3. 🎂
https://d3js.org

Your typical choropleth map uses color to represents values for one variable by region. But what if you want to see spatial composition across multiple categories or variables at once?

👀 Check out Joe Davies' ternary choropleth map, which visualizes population age structure based on three discrete age bins using a color triangle: @eurostat-ws@observablehq.com

🗺️ From clarifying patterns to exposing outliers, different map types reveal insights in unique ways. Explore 10 ways to visualize spatial data — from choropleths to bubble maps to grid cartograms. Find a map type that suits your data, then fork an example from the Observable Plot gallery to create your own: @observablehq@observablehq.com

We sat down with Observable Data Visualization Engineer Fil Rivière to talk all about maps. Read our interview for Fil's perspective on what makes maps meaningful, where spatial data viz is headed, and why he's hoping for a "slow cartography movement." Read more: https://buff.ly/QiJ0Yaf

Too often, vague requests and messy datasets lead analysts down the wrong path. So, we put together a practical framework for moving from raw data to clearer, useful insights. Read the guide on our blog: https://observablehq.com/blog/from-raw-data-to-data-storytelling-framework-for-structuring-data-analysis

🧇 Happy International Waffle Day! Did you know that Observable Plot has a built-in waffle mark? Visit the Plot documentation to see examples and learn about helpful options to customize chart units, adjust spacing, round values, and more: https://observablehq.com/plot/marks/waffle

Interactive data visualization can bridge disciplines, democratize exploration, and transform how teams think with data. Learn how Dr. Philip Bogden helped oncology researchers move beyond spreadsheets into collaborative, visual analysis that streamlines discovery: https://observablehq.com/blog/making-science-visible-dr-philip-bogden-bringing-data-visualization-to-cancer-research

Observable's Julio Avalos and Marisa Morby recently chatted about AI's impact and opportunity in data analysis, shifting stakeholder expectations, and the role of advanced data visualization in this new reality. Check it out👇

🎥 Recording: https://www.youtube.com/watch?v=nzpF_j_GAmQ
📝 Recap post: https://observablehq.com/blog/webinar-recap-evolving-data-analysis-landscape

It's great to move fast in data analysis, but not if that means skipping important steps to ensure your work is accurate and useful. In our new post, we share 4 practical habits to avoid common gotchas in data analysis 👉 https://observablehq.com/blog/4-ways-to-avoid-common-gotchas-in-data-analysis

We ❤️ this Observable Notebook from @rreusser@mathstodon.xyz exploring yoga class attendance, with calendars and charts made in Observable Plot! Read Ricky's reflections, and see how they use a notebook import to repurpose an existing Plot component: @rreusser@observablehq.com

Happy 2026 from the Observable team! 🎉

With the start of the new year, we're reminded that time flies and there's always a need to understand how things change as it does. Time series data is a sequence of observations collected over time, usually at regular intervals. This makes it useful for tracking everything from personal habits to company metrics.

Learn about 8 charts to visualize time series data👇

When you're working closely with a dataset, insights may be obvious to you but less clear to stakeholders unfamiliar with the data. Check out 8 tips for developing clearer and more interpretable data visualizations, including highlighting key elements, decluttering, adding takeaways in titles, and including useful context.

Ready to start building more impactful data visualizations? Explore examples in the Observable Plot gallery: @observablehq@observablehq.com

Bar charts, line charts, and pie charts are useful and ubiquitous chart types in BI tools, but sometimes they can fall short. Learn about effective, engaging alternatives that are easy to build with Observable Plot: https://buff.ly/Bz2RXC2

🧠 Know your audience. 💾 Understand your data. 📊 Design for interpretability. These lessons from data journalism can help data teams build better data visualizations. Read more: https://buff.ly/nV2qUan

Pi day is tomorrow, which naturally has us thinking about pie charts! Check out 8 options to visualize parts of a whole, from pies to waffles to treemaps. Then, head over to the Observable Plot and D3 galleries to explore hundreds of chart examples that you can quickly fork and customize.

👉 Plot gallery: @observablehq@observablehq.com
👉 D3 gallery: @d3@observablehq.com

Dashboards aren’t disappearing, but they’re no longer the center of gravity for most daily data work. In our latest post, we discuss:

🤖 How AI tools are democratizing data analysis
💡 What this means for how and where stakeholders expect to interact with data
🤝 Practical ways data teams can meet users where they are, without sacrificing rigor

Check it out 👇
https://observablehq.com/blog/shifting-stakeholder-expecations-in-the-age-of-ai-powered-analytics

AI is fundamentally changing the data analysis and BI landscape. Learn how AI is meaningfully improving data analysis workflows today, where it's falling short, and what it means to operationalize AI responsibly in real-world data teams.

👉 https://observablehq.com/blog/how-ai-is-accelerating-data-analysis-and-bi-work

The Observable Plot API helps you build expressive, interactive data visualizations in JavaScript using concise and approachable code. Even better, options for advanced customizations like spatial interpolation, color blend modes, and interactive crosshairs come built-in.

👉 Learn about 8 underused Plot options to uplevel your charts in just a few characters:
https://observablehq.com/blog/underused-options-observable-plot

If you’re on a data team, you’re probably being asked for more insights — faster. AI is reshaping BI & analytics, but it’s not without challenges. Join Observable Co-CEO Julio Avalos and Director of Research Marisa Morby for a live conversation on what data teams are learning — and how to turn new tech into real impact. Save your spot: https://streamyard.com/watch/cpSWShqkpzS3

🔮 The BI and analytics landscape is rapidly changing. While we may not be able to precisely predict the future, we have a pretty good sense of where the industry is going. Read our POV and see how you can take advantage of these shifts. https://observablehq.com/blog/state-of-bi-and-data-analytics-2026

The features that define modern, high-impact data visualizations — like responsiveness, visual filtering, and extreme value handling — are often the hardest to build from scratch. In Observable Canvases, we’re making advanced, interactive charts accessible to everyone. Learn how 👇 https://observablehq.com/blog/democratizing-advanced-interactive-data-visualization-in-observable-canvases

The nature of data analysis and visualization is changing rapidly, and data teams are being asked to deliver more insights, faster than ever. Register now for a webinar on Feb 4 at 1PM ET / 10AM PT unpacking these changes, and how your data team can adapt:
https://streamyard.com/watch/cpSWShqkpzS3

People have strong opinions about pie charts — but are they based on rigorous research, or just personal preference? Learn what studies tell us about the chart type that everyone loves to hate in our new blog post: https://observablehq.com/blog/truth-about-pie-charts

Whether you're building a chart from scratch or forking a notebook to reuse an example, it's helpful to understand common data formats expected by data visualization tools.

In this post, we describe methods to reshape data for use with our open-source visualization libraries, Observable Plot and D3. For each, we share reusable code snippets in a companion notebook, so you can get your data into shape even faster: https://observablehq.com/blog/reshaping-data-plot-d3

Between AI advances and rising stakeholder expectations, the BI and data analysis landscape is changing fast. What can data teams do to adapt? In a recent fireside chat, our CEO and Director of Research unpacked what these changes mean for data teams: https://buff.ly/0XaR6e2

Missed our webinar on the evolving data analysis & BI landscape? Observable’s Director of Research Marisa Morby and co-CEO Julio Avalos discuss AI’s impact and opportunity in data analysis, shifting stakeholder expectations, and why advanced data viz matters. 🎥
https://www.youtube.com/watch?v=nzpF_j_GAmQ

Our webinar on the evolving landscape of data analysis and visualization is tomorrow at 1PM ET / 10AM PT! Register now and bring questions for the live Q&A: https://streamyard.com/watch/cpSWShqkpzS3

🗺️ Comparing values by region can be tricky with spatial data due to vastly different geographies. Enter the grid cartogram: an engaging map type that represents subregions using uniform tiles, while avoiding overlap. Learn more 👇
https://observablehq.com/blog/what-is-a-grid-cartogram

At Observable, we think rich, interactive charts shouldn’t be limited to expert developers. That’s why the Observable Canvases chart library includes advanced features like visual filtering, responsiveness, and extreme value handling by default.

📈 Learn more 👉 https://observablehq.com/blog/democratizing-advanced-interactive-data-visualization-in-observable-canvases

Visual data exploration can uncover patterns and questions in business data. But big data can bring charts to a crawl, which disincentivizes exploration and hinders insight discovery. Learn how we deliver rich, performant charts in Observable Canvases 👉 https://observablehq.com/blog/big-data-fast-data-visualizations

AI agents are making it easier than ever for people across organizations to explore data and find answers on their own. But broader access makes a strong data culture grounded in learning, transparency, and responsible practices more important than ever.

Data teams have a critical role to play in shaping that foundation. Learn practical ways analysts can help build and sustain a healthy data culture within their organization 👇
https://observablehq.com/blog/how-to-build-data-culture