DATA STUDIO

APRIL 2026 PRODUCT UPDATES

Covering: Platform Rebrand · Conversational Analytics · Policy Users · Ads Field Deprecations

Published: May 2026

Source: Google Cloud Documentation — docs.cloud.google.com/data-studio

Executive Summary

April 2026 marks a pivotal month for Google's data analytics ecosystem. In a series of coordinated releases spanning the first and third weeks of the month, Google announced a landmark platform rebrand, expanded AI-powered analytics capabilities, introduced stronger enterprise governance controls, and enforced a long-signalled deprStudecation of legacy Google Ads location fields. Together, these changes reshape how individuals, teams, and enterprises engage with Google's self-service analytics and visualisation platform.

The centrepiece is the reversal of the 2022 'Looker Studio' rebrand. The product is now once again called Data Studio — a naming decision driven by persistent market confusion between the lightweight, free analytics tool and Google's enterprise-grade Looker BI platform. Alongside the rename, Google shipped a redesigned home page, expanded Conversational Analytics access, a BigQuery data-agent integration, and new Policy User controls for Pro team workspaces. On the deprecation front, advertisers using location extension fields in Google Ads connectors must complete migration to the newer Asset location fields before 4 May 2026 to avoid report errors.

1. Platform Rebrand: Looker Studio Returns to Data Studio

1.1 Background and Context

In October 2022, at Google Cloud Next, Google renamed its popular free reporting product — Data Studio — to Looker Studio, aligning it with the enterprise BI platform acquired for $2.6 billion in 2020. The intent was to present a unified analytics portfolio under the Looker umbrella. However, the shared branding created immediate and lasting confusion in the market. Enterprise procurement teams regularly conflated the two products. Sales cycles slowed as consultants and Google's own teams spent considerable time explaining that 'Looker' (the enterprise semantic-layer BI platform with LookML at its core) and 'Looker Studio' (the free, browser-based dashboard builder) were fundamentally different tools serving different audiences at very different price points.

On April 10, 2026, Google publicly announced the reversal. The rebranding went live on April 16, 2026. Looker Studio is now Data Studio. Looker Studio Pro is now Data Studio Pro. The enterprise Looker platform retains its name exclusively, creating a clear two-product delineation: Data Studio for personal data exploration and ad-hoc reporting; Looker for governed enterprise analytics with a centralised semantic model.

1.2 What Changed

The rebrand encompasses the following concrete changes:

• The product URL, interface labels, and all in-product references now read 'Data Studio'

• Looker Studio Pro is renamed to Data Studio Pro, with no changes to pricing or feature set

• The home page has been redesigned to serve as a unified portal for Data Studio reports, BigQuery conversational agents, and Colab notebook-based data apps

• Gemini in Looker Studio is renamed to Gemini in Data Studio with no functional changes

• All existing reports, data sources, shared links, and user permissions carry over automatically — no user action is required

1.3 Strategic Rationale

Google's stated goal is to position Data Studio as the front door to AI-driven personal analytics in the Google Data Cloud era. The platform is being broadened beyond visualisation to serve as a central navigation layer for diverse data assets: traditional dashboards, BigQuery conversational AI agents, and interactive data apps built in Colab notebooks. In contrast, Looker is being reinforced as the enterprise-grade platform for organisations where every metric must be governed, consistently defined, and trusted by AI agents at scale.

Industry analysts and practitioners have broadly welcomed the move. The shared 'Looker' name had eroded Looker's premium positioning and created real commercial friction. Restoring the Data Studio brand also reconnects the product with years of accumulated brand equity built since its 2016 launch as part of the Google Analytics 360 suite.

1.4 Impact on Users and Organisations

For existing users, the impact is minimal and positive: a familiar brand returns, with no data migration, no report rebuilding, and no access changes required. For organisations evaluating their analytics stack, the two-product naming now accurately signals architectural fit: Data Studio for speed and Google-native integration; Looker for enterprise governance, LookML semantic modelling, and agentic AI readiness.

• Marketers and agencies: Day-to-day Google Ads, GA4, and Sheets reporting is unaffected

• Enterprise teams: Clearer product differentiation aids procurement and platform decisions

• Looker practitioners: Removing the shared name strengthens Looker's premium positioning

• New adopters: 'Data Studio' is more self-explanatory than 'Looker Studio' for non-Looker contexts

2. A New Conversational Analytics Experience

2.1 What Conversational Analytics Is

Conversational Analytics allows users to query data using natural language — typing questions in plain English (or other supported languages) and receiving answers as charts, tables, and textual summaries, without writing a single line of SQL. Behind the scenes, the system leverages Gemini models to translate natural language prompts into SQL queries, execute those queries against connected data sources (primarily BigQuery), and return results with a transparent reasoning trail that shows users exactly how their question was interpreted and which queries were run.

2.2 What Is New in April 2026

The April 2026 release ships a redesigned 'new experience' for Conversational Analytics with three headline changes:

A legacy view remains accessible for users who need to retrieve past conversations or agents that were built in the old experience. However, past conversations and legacy agents cannot be migrated to the new experience — they can only be accessed by switching back to the legacy view.

2.3 BigQuery Data Agent Integration

The most significant architectural change is the decoupling of agent creation from Data Studio. Agents — customised AI query assistants that include business-specific context, instructions, verified queries, and knowledge sources — must now be built in BigQuery's Agent Catalog using the Google Cloud console. Once published in BigQuery, agents appear automatically in Data Studio's 'Chat with your data' interface, available to users with the appropriate IAM permissions.

This design places agent authoring in the hands of data engineers and analysts who understand the underlying data, while making consumption frictionless for business users in Data Studio. The result is a governed, accessible, no-code analytics chat experience that bridges the gap between technical data teams and self-service users.

• Agents are built once in BigQuery and shared to multiple surfaces: Data Studio, Gemini Enterprise, or custom applications via the Conversational Analytics API

• The 'Show reasoning' toggle surfaces a plain-text audit trail of query interpretation, providing transparency for client-facing and compliance use cases

• A Code Interpreter feature (available to Data Studio Pro users with Gemini enabled) translates natural language to Python for more complex analysis and visualisations beyond standard SQL

• A 5,000-row result cap applies; single-table conversations only (multi-source joins require a pre-built view)

2.4 Why This Matters

The democratisation of Conversational Analytics — removing the Gemini subscription gate for basic access — significantly expands the potential user base. Any Data Studio user can now ask questions of their BigQuery data in plain language. For organisations with large numbers of data consumers who lack SQL skills, this reduces the dependency on analyst or engineering queues for routine data questions, improving business agility.

For organisations requiring advanced analysis, forecasting, and Python-powered visualisations, the Code Interpreter capability remains behind the Data Studio Pro paywall, creating a clear tier distinction between exploratory self-service and power analytics use cases.

3. Share BigQuery Data Agents to Data Studio

3.1 Overview

This feature enables data teams to create context-aware, business-specific AI query agents in BigQuery and publish them directly to Data Studio, where a wider audience of non-technical users can interact with them through a guided chat interface. This closes a gap that previously required custom application development (e.g., a Streamlit app or API-based chatbot) to expose BigQuery data agents to business users.

3.2 How It Works

The workflow from agent creation to consumption involves the following steps:

• A data engineer or analyst creates a data agent in BigQuery's Agent Catalog via the Google Cloud console, connecting it to one or more BigQuery tables, views, or UDFs as knowledge sources.

• The agent is enriched with business context: field metadata, custom glossary terms, verified queries (golden queries), and natural language instructions that guide how the agent interprets domain-specific questions.

• The agent is published from BigQuery, at which point it automatically becomes available in Data Studio's 'Chat with your data' interface for users with the appropriate permissions.

• Business users in Data Studio select the agent and begin asking natural language questions. Responses are presented as text, tables, and visualisations, accompanied by the agent's reasoning and the underlying SQL.

3.3 Key Capabilities and Limitations

• Multi-surface availability: published agents can be accessed in Data Studio, Gemini Enterprise, and via the Conversational Analytics API for custom apps

• Transparent reasoning: agents surface generated SQL and a 'thinking process' summary, making answers verifiable and auditable

• Security by design: IAM-governed access ensures users only query data they are authorised to view, with all queries logged in BigQuery's compliance framework

• Predictive analytics: agents can leverage BigQuery AI functions (AI.FORECAST, AI.DETECT_ANOMALIES) to answer forward-looking questions in natural language

• Current limitation: only one BigQuery table per conversation; multi-table queries require a pre-built view

• Agent management (edit, revoke access, delete) is performed in BigQuery, not in Data Studio

4. Pro Feature: Policy Users for Team Workspace Assets

4.1 The Problem This Solves

In Data Studio Pro team workspaces, reports and data sources often lack an individual owner — they are organisational assets rather than personal ones. This creates a governance gap: when the person who originally created or moved an asset leaves the organisation, there is no clear owner whose account is used to determine which Google Workspace or Cloud Identity sharing policies apply. This can silently change the sharing behaviour of reports, expose assets to policy violations, or prevent appropriate external sharing when needed.

4.2 How Policy Users Work

A Policy User is a designated account that Data Studio uses to evaluate organisational sharing policies for a specific asset residing in a team workspace or folder. The Policy User is not necessarily the owner of the asset — they are the reference identity for policy enforcement.

• When a user moves an asset into a team workspace or folder, that user automatically becomes the Policy User for that asset

• If the asset is moved out of all team workspaces and folders, the Policy User designation is removed

• If you are an asset owner but not the Policy User, you can designate yourself as Policy User via the Advanced Sharing Settings modal (Share > Settings gear > Make me policy user)

• Administrators can audit and reassign Policy Users, ensuring continuity when team members change roles or leave the organisation

4.3 Why This Matters for Enterprise Governance

For organisations with strict data-sharing policies — common in regulated industries such as financial services, healthcare, and public sector — the Policy User feature provides a reliable mechanism to maintain policy compliance even as team membership evolves. Without it, a shared report's effective sharing policy could silently change if its creator leaves, potentially opening or restricting access in unintended ways.

This feature is particularly valuable for teams using Google Workspace Domain Restricted Sharing or similar organisational sharing policies, where only users within the domain should have access to sensitive reports. Assigning a stable Policy User — such as a service account or a long-tenured admin — ensures policy continuity regardless of individual personnel changes.

5. Deprecation: Ads Location Extension Fields

5.1 Overview

Google has deprecated a set of location extension fields across both the Google Ads connector and the New Search Ads 360 (SA360) connector in Data Studio. These fields — which previously surfaced affiliate location and store location data — stopped serving data on 4 May 2026. Any report or data source that references these fields will return an error from that date onwards.

5.2 Deprecated Fields

The following fields are deprecated in both the Google Ads and New SA360 connectors:

5.3 Required Migration: Asset Location Fields

Google has replaced the deprecated fields with a unified set of 'Asset location' fields. These must be substituted on a one-to-one basis in all affected reports and data sources:

5.4 Why This Deprecation Occurred

This deprecation aligns with Google Ads' broader migration from the legacy location extension model to the unified 'location asset' framework. Location extensions were a legacy mechanism that attached physical store or affiliate location data directly to ad campaigns. Google Ads deprecated this format in 2023 in favour of location assets, which offer more flexible, granular, and reusable location data that can be shared across campaigns and linked to Google Business Profile data.

The field-level deprecation in Data Studio is the downstream consequence: as the Google Ads API no longer returns data for the old field types, the connector must be updated accordingly. Reports that reference the deprecated fields will return empty results or errors, making migration urgent for any advertiser who reports on physical location performance within Data Studio.

5.5 Recommended Action Steps

• Audit all Data Studio reports and data sources that connect to the Google Ads or New SA360 connectors.

• Identify any charts, tables, scorecards, or filters referencing the deprecated 'Affiliate location' or 'Store location' fields.

• Replace each deprecated field with its corresponding 'Asset location' field using the Data Studio field editor in the data source settings.

• Verify that Google Ads campaigns are properly configured with location assets (not legacy extensions) in the Google Ads console before relying on the new fields.

• Test updated reports to confirm data populates correctly. Reference the Google Ads help article on Ads location assets (support.google.com/google-ads/answer/2404182) for further guidance.

6. Summary of April 2026 Releases

7. Key Takeaways and Recommendations

For All Data Studio Users

• No migration is needed for the platform rebrand — all reports, links, and permissions continue to function as before

• Explore the Conversational Analytics feature on the updated home page; basic access now requires no additional subscription

• Bookmark the new Data Studio documentation hub at docs.cloud.google.com/data-studio for the latest guidance

For Data Analysts and Engineers

• Begin evaluating BigQuery's Agent Catalog to create governed data agents that can be shared to Data Studio users — this is the new recommended path for AI-powered analytics

• Consider the 'Show reasoning' audit trail for any client-facing or compliance-sensitive analytics use cases

• Note that agent creation is now exclusively in BigQuery; plan data modelling and access control accordingly

For Data Studio Pro Administrators

• Audit team workspaces to identify assets without an appropriate Policy User, especially those created by former team members

• Establish a convention for assigning Policy Users — e.g., a team account or current workspace manager — to prevent policy gaps

• Leverage the updated home page to centralise access to reports, BigQuery agents, and Colab apps for your organisation

For Digital Marketers and Advertising Teams

• URGENT: If you use Google Ads or Search Ads 360 connectors, audit all Data Studio reports for deprecated location extension fields immediately

• Data serving on deprecated fields stopped on 4 May 2026; reports referencing these fields will return errors

• Complete migration to Asset location fields and verify campaign-level location asset configuration in Google Ads before relying on new fields in reports

Sources: Google Cloud Blog (cloud.google.com/blog) · Data Studio Release Notes (docs.cloud.google.com/data-studio/release-notes) · Data Studio Documentation (docs.cloud.google.com/data-studio) · Google Ads Help (support.google.com/google-ads)