GA4 in Google Data Studio (formerly Looker Studio): The Complete Guide

GA4 is where you analyse your data; Google Data Studio is where you turn it into reports people actually read. Connected well, it gives you custom, shareable, always-live dashboards that combine GA4 with Google Ads, Search Console and more, all for free.

This guide is a practical, end to end walkthrough of building GA4 reports in Google Data Studio: connecting your data, designing a dashboard, choosing the right charts, working with calculated fields and filters, blending sources, using templates, and avoiding the pitfalls that quietly break reports. Work through it in order, or jump to the part you need.

What is Google Data Studio (formerly Looker Studio)?

Google Data Studio is Google's free reporting and dashboarding tool. It connects to GA4 and dozens of other sources (Google Ads, Search Console, BigQuery, Sheets) and turns that data into interactive, shareable reports. It launched as Google Data Studio, was renamed Looker Studio in 2022, and in April 2026 Google renamed it back to Data Studio; older tutorials and your own bookmarks may still say Looker Studio, but it is the same product.

The obvious question is why use it at all when GA4 has its own reports. Three reasons:

• Custom layouts: you design exactly the view stakeholders need, instead of navigating GA4's menus.
• Sharing: send a live link or schedule a PDF, with no GA4 access required for the viewer.
• Blending: combine GA4 with Ads, Search Console or CRM data in one chart, which GA4 cannot do.

In short, GA4 is where you analyse; Google Data Studio is where you report. This guide covers building GA4 reports in Google Data Studio from connection to sharing.

Connect GA4 to Google Data Studio

Start by adding GA4 as a data source. In Google Data Studio, create a report, choose the Google Analytics connector, authorise it, and pick your account, property and (for GA4) the property itself. Google Data Studio creates a data source that maps GA4's dimensions and metrics into fields you can drag onto charts.

Two things to know before you build:

• The connector can sample and threshold. Large date ranges or high-cardinality queries may return sampled or thresholded data, just as in GA4. If numbers look rounded or an "(other)" row appears, that is the cause.
• Field naming differs slightly from GA4's UI, and some GA4 features (certain explorations, custom funnels) have no direct connector equivalent. Plan reports around what the connector exposes.

Build your first GA4 dashboard

A good GA4 dashboard answers a specific question for a specific audience. Before dragging fields around, decide who it is for (an executive wants outcomes; a marketer wants channels and campaigns) and design backwards from that.

The mechanics are quick once you have a data source:

1. Add scorecards for headline numbers (key events, sessions, engaged sessions, conversion rate).
2. Add a time series to show the trend, and a table for the detail (top pages, top channels).
3. Lay the page out top to bottom in priority order, and keep one page focused on one theme.

Resist the urge to put everything on one page. Two clean pages (an overview and a deep dive) beat one cluttered wall of charts every time.

Choose the right chart for the job

Google Data Studio gives you many chart types, and picking the wrong one is the fastest way to mislead a reader. A quick guide:

• Scorecards for single headline numbers.
• Time series and line and combo charts for trends over time, which is most GA4 reporting.
• Bar charts and tables for comparing categories (channels, pages, campaigns).
• Scatter charts to reveal the relationship between two metrics, such as sessions versus conversion rate.
• Area charts for cumulative or part-to-whole trends.
• Waterfall charts to show how sequential positive and negative changes build a total.
• Sankey charts to visualise flow between stages, such as channel to landing page to outcome.

Default to clarity: a plain time series and a sorted table answer most questions. Reach for the advanced charts only when the shape of the data genuinely calls for them.

Dimensions, metrics and calculated fields

Every chart is built from dimensions (the things you group by, such as channel or page) and metrics (the numbers, such as sessions or key events). Google Data Studio inherits these from GA4, but you can also create your own.

Calculated fields are where Google Data Studio becomes powerful. With formulas you can:

• Create new metrics (for example, a custom conversion rate as key events divided by sessions).
• Reshape dimensions with CASE statements (group messy page paths into clean content groups).
• Clean and concatenate text, or extract parts of a URL.

Set the correct aggregation (sum, average) and data type on each field, because a metric averaged when it should be summed is a silent reporting bug. Build calculated fields at the data-source level when you want to reuse them across charts and reports.

Filters, controls and date ranges

Static reports get ignored; interactive ones get used. Google Data Studio's controls let viewers explore without editing the report.

• Date range control: add one to every report so viewers can change the period themselves. Set a sensible default date range (last 28 days is a good baseline) so the report opens to something useful.
• Filter controls: let viewers slice by channel, device or country with a dropdown.
• Comparison: turn on a comparison date range (previous period or previous year) so every number carries context, not just a value in isolation.

Understand the difference between a filter control (interactive, for viewers) and a chart-level filter (fixed, applied by you). Use chart-level filters to scope a chart permanently, and controls to hand exploration to the reader.

Blend data from multiple sources

One of Google Data Studio's biggest advantages over GA4's built-in reports is data blending: combining multiple sources into a single chart. Common blends for marketing teams include GA4 with Google Ads (to see cost next to on-site behaviour) and GA4 with Search Console (to join queries to landing-page performance).

A blend works by joining sources on a shared key (a date, a landing page, a campaign). Keep three rules in mind:

• Pick a join key that exists and matches in both sources, or the blend returns nothing.
• Choose the right join type (left outer is the usual default) so you do not silently drop rows.
• Keep blends lean. Large, multi-source blends are the most common cause of slow, timing-out reports.

Blending is powerful but easy to get subtly wrong, so always sanity-check a blended total against each source on its own.

Recreate GA4 channel groupings in Google Data Studio

GA4's default channel grouping does not always survive into Google Data Studio the way you expect, and you often want groupings that match your business rather than Google's defaults. The fix is to build the grouping yourself with a calculated field.

Using a CASE statement on source and medium, you can define exactly which traffic counts as Paid Social, Branded Search, Email and so on, then use that field as a dimension across your reports. Our walkthrough on GA4 custom channel groupings in Google Data Studio gives you the formula and the logic to adapt.

This is the single most useful calculated field most teams build, because clean channels make every traffic and conversion report instantly more trustworthy.

Templates and sharing

You do not have to start from a blank canvas. Google Data Studio's template gallery and community templates give you a tested layout you can connect to your own GA4 data in minutes, which is the fastest way to a presentable dashboard.

When the report is ready, sharing is where the value lands:

• Share a link with view access so stakeholders always see live data, no GA4 login needed.
• Schedule email delivery of a PDF for people who will not open a link.
• Embed the report in an intranet or Notion page where your team already works.

Manage access deliberately: give viewers Viewer access, not Edit, so a stray click cannot rearrange the report everyone relies on.

Common Google Data Studio mistakes to avoid

Most broken Google Data Studio reports fail for the same handful of reasons:

• Trusting sampled data: a rounded number or an "(other)" row means sampling. Narrow the date range, reduce cardinality, or move to the BigQuery export.
• Wrong aggregation: a rate or ratio that is summed instead of recalculated will be nonsense. Check calculated-field aggregation.
• Heavy blends: too many sources or a bad join key makes reports slow or empty.
• Stale cache: Google Data Studio caches data; if numbers look frozen, refresh the data or check the data-freshness setting.
• Reporting on bad data: the most common of all. A beautiful dashboard built on a broken GA4 setup just makes wrong numbers look credible.

That last point matters most: fix the tracking first, then report on it.

Get a GA4 dashboard built for you

A great Google Data Studio report depends entirely on the data underneath it. If your GA4 numbers are not yet trustworthy, a dashboard will simply present the problems more convincingly, so it is worth confirming the foundation first with a GA4 audit.

Once the data is clean, we design and build Google Data Studio dashboards that your team actually uses: the right metrics, custom channel groupings, blended sources and a layout matched to your audience. If you would rather have that done for you than build it from scratch, that is part of our GA4 services.