First impression of Looker Studio

Last week, I wrote a blog post about my initial impressions of GoodData (First impression of GoodData). This week, I explored Looker Studio.

Both Looker and Looker Studio, developed by Google, are business intelligence and data analytics platforms. Looker is designed to help businesses structure data using its proprietary ‘semantic layer’ and LookML, a language developed by Looker for defining dimensions and metrics in reports. This platform supports analysis, data exploration, dashboarding, and offers numerous collaboration features. However, Looker has a steep learning curve and requires a higher investment compared to most visualization tools.

This is where Looker Studio—formerly known as Google Data Studio—comes in. It’s a free, user-friendly tool for both analysts and end-users. It might be the easiest tool I have used so far for data visualization. My familiarity with other Google Workspace tools (like Docs and Slides) and data visualization platforms allowed me to quickly start building a dashboard without the need for lengthy tutorials or complicated documentation. It took me less than an hour to create my first Looker Studio dashboard.

While Looker Studio is an excellent data visualization tool for beginners like me, it does have some limitations. It offers fewer collaboration features than its counterpart, Looker; it lacks version control; and it struggles with large, complex datasets due to limited data processing capabilities. These shortcomings are understandable for a free tool, and some may be mitigated with a pro account. Despite these limitations, I find Looker Studio to be a useful tool for quickly creating dashboards, and I plan to continue using it in the future.

For more information on the differences between Looker and Looker Studio, visit: Looker vs Looker Studio.

– Tessa (data viz enthousiast)

Content

Is your data ready for what’s next?
Flexible data solutions that grow with you.

Let’s debrief: data & AI | Issue 3, March 2026

What happened in data and AI? If February was about AI growing up, March is about AI actually doing things. Actually running pipelines, detecting threats,...

AI-assisted code development: What you should know

This article is part of a series on how i-spark uses AI in our work. For an overview of the three main categories of AI...

How AI is changing the data industry 

Take a single office building on any given morning. On the third floor, a data engineer is building a transformation model in dbt. She has...