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Best Conversational Analytics for Looker Users

January 15, 2026

Best Conversational Analytics for Looker Users

By Andrey Avtomonov, CTO at Kaelio | 2x founder in AI + Data | ex-CERN, ex-Dataiku · Jan 15th, 2026

Conversational analytics transforms how Looker users access data by enabling natural language queries that translate into governed SQL respecting existing LookML models and permissions. With the conversational AI market reaching $31.9 billion by 2028, Looker customers can choose between native Gemini-powered capabilities or third-party solutions like Kaelio that achieve 95%+ SQL accuracy while maintaining enterprise governance.

At a Glance

Native vs. third-party options: Looker's built-in Conversational Analytics uses Gemini AI but has limitations including a 5,000-row cap per query, while alternatives like Kaelio provide higher accuracy and HIPAA compliance
ROI expectations: Organizations report $3.70 return per dollar invested with analysts saving 20 hours monthly on routine tasks
Phased rollout approach: Start with well-structured Explores, configure agents for pilot users, then expand adoption based on feedback
Semantic layer critical: A strong LookML semantic layer ensures accurate query interpretation and consistent metric definitions across all natural language queries
Accuracy varies by complexity: Simple queries perform well, but multi-table enterprise analytics can drop to 50% accuracy without proper semantic grounding

Conversational analytics is transforming how Looker users interact with their data. Instead of navigating complex dashboards or waiting for data team responses, business users can now ask questions in plain English and receive instant, governed answers. With the conversational AI market projected to reach $31.9 billion by 2028, Looker customers are evaluating their options carefully.

This guide breaks down what conversational analytics means for Looker environments, compares the native Gemini-powered capabilities with third-party alternatives like Kaelio, and provides a practical rollout framework to help your team adopt these tools safely.

Why Does Conversational Analytics Matter Inside Looker?

Conversational analytics lets users ask data-related questions in natural language and receive immediate, visual answers without writing SQL or navigating BI tools. For Looker users specifically, this means translating plain English queries into governed SQL that respects your existing LookML models, permissions, and business definitions.

The value proposition is straightforward: business users get answers faster, data teams spend less time on ad hoc requests, and everyone works from the same metric definitions. As Thomas Seyller, Senior Director of Technology & Insights at YouTube Business, noted: "We've been testing Conversational Analytics in Looker to give our partner managers instant, actionable data that lets them quickly guide creators and optimize creator support." (Google Cloud Blog)

Market momentum & accuracy standards

The technical maturity of natural language to SQL has improved significantly. Google Cloud recently achieved a 76.13 score on the BIRD benchmark, leading the Single Trained Model Track for text-to-SQL evaluation. Modern platforms now achieve 95%+ SQL accuracy with SOC 2 Type II compliance and 99.9% uptime guarantees.

However, accuracy varies significantly by complexity. AI data analyst tools achieve between 50-89% accuracy depending on query complexity. Simple queries perform well, but multi-table enterprise analytics can drop to around 50% accuracy without proper semantic grounding.

Kaelio: The Fastest Path to Trusted Answers

Kaelio takes a different approach than most conversational analytics tools. Rather than replacing your existing data stack, Kaelio acts as a natural language interface that sits on top of your existing infrastructure, including Looker, dbt, and your data warehouse.

This architecture matters for Looker customers because the platform inherits your existing LookML models, metric definitions, and governance rules. When a user asks a question, governed SQL is generated that respects the same permissions and business logic already defined in your Looker environment.

The platform differentiates itself through transparency, showing "the reasoning, lineage, and data sources behind each calculation" (Kaelio), allowing users to verify how answers were computed. It also "finds redundant, deprecated, or inconsistent metrics and surfaces where definitions have drifted," helping data teams maintain clean, consistent analytics over time.

Enterprise governance & compliance

For regulated industries, governance is non-negotiable. Kaelio is both HIPAA and SOC 2 compliant, making it suitable for healthcare organizations and other enterprises with strict data handling requirements.

This aligns well with Looker's existing compliance capabilities. Google supports HIPAA compliance through a Business Associate Agreement for Looker services, and Looker's semantic layer ensures that all data queries adhere to predefined business rules and logic.

Without governance, even accurate AI analytics creates audit risk. Kaelio addresses this by maintaining full lineage and auditability for every query, ensuring that compliance and oversight requirements are met.

Key takeaway: Kaelio provides the accuracy and transparency that enterprise Looker deployments require, while preserving your existing governance framework.

How Does Looker's Built-In Conversational Analytics Work?

Looker's native Conversational Analytics is powered by Gemini for Google Cloud and is now generally available to all Looker platform users. The feature allows users to ask questions directly against Looker Explores and receive instant visualizations.

Key capabilities include:

Multi-domain analysis: Users can ask questions that integrate insights from up to five distinct Looker Explores, spanning multiple business areas
Semantic layer grounding: Conversational Analytics is grounded in Looker's semantic layer, ensuring every metric, field, and calculation is centrally defined and consistent
Code Interpreter: For advanced analytics, the Code Interpreter translates natural language questions into Python code for tasks like time series forecasting
Data agents: Customizable AI-powered assistants that can be tailored to specific business domains

Where teams still hit bottlenecks

Despite these capabilities, Looker's native conversational analytics has limitations that enterprise teams should understand:

Row caps: Conversational Analytics can return a maximum of 5,000 rows per query, which may constrain large-scale analysis
Visualization limitations: The feature doesn't yet support prediction, forecasting, or advanced statistical analysis including correlation and anomaly detection
Early-stage accuracy concerns: As an early-stage technology, Gemini for Google Cloud products can generate output that seems plausible but is factually incorrect, requiring validation of results

For teams requiring higher accuracy, larger result sets, or advanced statistical capabilities, third-party solutions like Kaelio may provide a more complete solution.

How Can Looker Teams Roll Out Conversational Analytics Safely?

Google Cloud recommends a phased approach for implementing Conversational Analytics in Looker:

Phase 1: Curate data and define the initial scope

Start with one or two well-structured Explores that are based on relatively clean data and provide clear business value. Use Looker's permission system to limit initial user access to a pilot group.

Phase 2: Configure agents and validate internally

Create Conversational Analytics agents based only on the curated Explores from Phase 1. Thoroughly test with users who are familiar with the data before broader rollout.

Phase 3: Expand adoption and iterate

Use feedback to make refinements to LookML and agent instructions. Prioritize data cleanup efforts based on real user questions.

LookML hygiene that drives NL accuracy

The accuracy of conversational analytics depends heavily on your LookML model quality. Follow these best practices:

Define the relationship parameter for all joins to ensure proper metric aggregation
Define a primary key within each view, including derived tables
Use the synonym LookML parameter to define synonyms for field names or values, helping the AI understand business terminology
Use substitution operators to reference database tables and fields, making models easier to maintain
Refine labels, descriptions, and hidden parameters to provide better context for natural language interpretation

Why a Strong Semantic Layer Super-Charges Conversational Accuracy

A semantic layer creates a consolidated representation of an organization's data, making data understandable in common business terms. For conversational analytics, this layer serves as the foundation for accurate query interpretation.

Looker's semantic layer supports data governance by ensuring that all data queries adhere to predefined business rules and logic, reducing the risk of data misinterpretation. When combined with conversational analytics, this means every natural language query is translated into SQL that reflects your organization's official metric definitions.

For teams using dbt alongside Looker, the dbt Semantic Layer eliminates duplicate coding by centralizing metric definitions. When a metric definition changes in dbt, it's refreshed everywhere it's invoked, creating consistency across all applications including conversational interfaces.

Kaelio is designed to work with both LookML and dbt semantic layers, inheriting these definitions rather than creating its own competing layer. This approach prevents metric drift and maintains the single source of truth your data team has already established.

What ROI Benchmarks Should You Expect from Conversational Analytics?

Organizations report $3.70 return per dollar invested in conversational analytics, with analysts saving 20 hours monthly on routine tasks.

When evaluating tools, consider these metrics:

SQL accuracy: Look for platforms achieving 95%+ accuracy on your typical query patterns. Accuracy drops significantly on complex, multi-table queries
Time to insight: Measure how quickly business users can get answers compared to the current process of submitting requests to data teams
Support ticket reduction: Forrester research shows organizations can achieve a 20% reduction in support requests when employees can self-serve answers
ROI benchmarks: Enterprise AI tools typically deliver 116-141% ROI over three years when properly implemented

The most meaningful ROI comes from reduced bottlenecks. When business users can answer their own questions, data teams can focus on higher-value analytics work rather than fielding repetitive ad hoc requests.

Key Takeaways & Next Steps

Conversational analytics is becoming essential for Looker users who want to democratize data access while maintaining governance. Here's how to move forward:

Evaluate your current state: Assess your LookML model quality and semantic layer completeness. Poor data hygiene will limit any conversational tool's effectiveness.
Start small: Follow the phased rollout approach, beginning with well-structured Explores and a pilot user group.
Consider your requirements: Looker's native Conversational Analytics works well for teams fully committed to the Google Cloud ecosystem. For organizations needing higher accuracy, HIPAA compliance, or integration across multiple BI tools, Kaelio provides a natural language interface that sits on top of your existing data stack without replacing it.
Prioritize transparency: Choose tools that show how answers are calculated. By using Looker's semantic layer, organizations can reduce the risk of data misinterpretation and ensure AI-generated insights are aligned with business objectives.

Kaelio is designed specifically for enterprises with complex data governance needs. If you're a Looker user looking to add conversational analytics while preserving your existing semantic layer and compliance requirements, request a demo to see how Kaelio can help your team get trusted answers faster.

About the Author

Former AI CTO with 15+ years of experience in data engineering and analytics.

Frequently Asked Questions

What is conversational analytics in Looker?

Conversational analytics in Looker allows users to ask data-related questions in natural language and receive immediate, visual answers without writing SQL or navigating BI tools. It translates plain English queries into governed SQL that respects existing LookML models and business definitions.

How does Kaelio enhance conversational analytics for Looker users?

Kaelio acts as a natural language interface that sits on top of existing data infrastructure, including Looker. It generates governed SQL that respects LookML models and business logic, providing transparency and maintaining governance, which is crucial for enterprise environments.

What are the limitations of Looker's native conversational analytics?

Looker's native conversational analytics has limitations such as a maximum of 5,000 rows per query, lack of support for advanced statistical analysis, and early-stage accuracy concerns that require validation of results.

How does Kaelio ensure compliance and governance in analytics?

Kaelio is HIPAA and SOC 2 compliant, ensuring that all queries maintain full lineage and auditability. It respects existing governance frameworks, making it suitable for regulated industries and enterprises with strict data handling requirements.

What ROI can organizations expect from implementing conversational analytics?

Organizations can expect a $3.70 return per dollar invested in conversational analytics, with analysts saving 20 hours monthly on routine tasks. Proper implementation can lead to a 20% reduction in support requests and 116-141% ROI over three years.