The medium is the problem 

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State of BI and analytics

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The role of inquiry in AI-powered data analysis

Hanna Woodburn

Allison Horst

Julio Avalos

Observable

AI makes it faster and easier than ever to get answers from data — but the quality of the questions being asked matters more than ever. We share a practical framework for building better inquiry habits across your team.

The role of inquiry in AI-powered data analysis 

AI code generation produces more code faster, but widens the comprehension gap — the solution isn't better generation, it's restoring the "write, run, inspect" loop at machine speed.

The medium is the problem

A practical guide to alternatives to standard chart types using Observable Plot to build advanced visualizations that can help uncover trends, changes, flow, distribution, and relationships. 

Beyond bar charts: A guide to advanced visualizations

By thinking more like data journalists, data teams can make their visualizations and dashboards clearer, more targeted, and more useful. In this guide, we unpack practical strategies from data journalism that analysts can adapt for their own day-to-day workflows. 

What data teams can learn from journalists about data visualization

Stakeholder expectations around interacting with data are shifting from polished products to on-the-fly insights using AI-powered analytics. Learn how data teams can meet users where they are, while encouraging sound data practices.

Shifting stakeholder expectations in the age of AI-powered analytics

AI is reshaping workflows across the data analysis and BI landscape. We took a look at where AI is delivering real value for data teams today, where it misses the mark, and how to unlock its full potential — without losing the human ingenuity that drives meaningful insights.

How AI is accelerating data analysis work — and where it’s falling short

A recap of our webinar on the evolving data analysis and BI landscape, covering AI’s impact on analyst workflows, changing, stakeholder expectations, and what’s next for data teams.

Webinar recap: The evolving data analysis landscape

Don’t stumble into common data analysis pitfalls. Learn how to avoid issues related to missing values, data wrangling mistakes, and AI-generated outputs.

4 ways to avoid common gotchas in data analysis

The analytics and BI industry is changing rapidly. In this post, we recap the major shifts in the BI landscape, forecast where it’s going, and outline how you can keep up. 

The state of BI and analytics in 2026

Learn how we’ve upgraded AI integration  for fluid, inspectable  text-to-SQL that accelerates data wrangling and analysis in Observable Canvases. 

Observable blog

The role of inquiry in AI-powered data analysis

The role of inquiry in AI-powered data analysis

The medium is the problem

Beyond bar charts: A guide to advanced visualizations

What data teams can learn from journalists about data visualization

Shifting stakeholder expectations in the age of AI-powered analytics

How AI is accelerating data analysis work — and where it’s falling short

Webinar recap: The evolving data analysis landscape

4 ways to avoid common gotchas in data analysis

The state of BI and analytics in 2026

Draft and customize queries even faster with streamlined text-to-SQL