Conversational Analytics in Looker: The Complete Guide for Businesses

This guide explains how conversational analytics functions inside Looker and how organizations can use it to provide easier access to information. The goal is to offer a clear understanding of the capabilities, underlying concepts and practical considerations involved in adopting this approach.

Conversational analytics allows users to type questions in plain language and receive structured responses. Looker supports this while maintaining the consistency of its semantic layer. This ensures that insights remain aligned with established definitions, even when accessed through a natural language interface.

---

Table of Contents : 1.What Conversational Analytics in Looker Means

2.Why Conversational Analytics Matters

3.Prerequisites for Successful Deployment

4.How Conversational Analytics Works in Looker

4.1Workflow: How a Query Moves Through Looker

4.2Architecture Overview

4.3Data Agents and Their Role in AI Driven Analytics in Looker

4.4Conversational Analytics in Slack, Teams and Embedded Applications

4.5Looker and Looker Studio in Context for Conversational BI Solutions

5.Applications Across Business Functions

6.Security and Governance Considerations

7.Limitations and Practical Expectations

8.SquareShift’s Conversational Analytics Offerings

9.Conclusion

---

1.What Conversational Analytics in Looker Means

Conversational analytics in Looker is a natural language interface that lets users type questions and receive insights as tables, visualizations or summaries. The responses follow the logic defined in LookML, which ensures that data continues to reflect consistent metric definitions and business rules. The interface maintains conversational context and allows users to drill down into Looker Explores when they need additional detail. 

2.Why Conversational Analytics Matters

Many business teams want quicker paths to information. Conversational analytics provides this by eliminating some of the steps required to explore data. It supports users who are not familiar with SQL and helps analysts by reducing routine questions that can be answered quickly.

At the same time, the LookML model keeps the results aligned with established definitions. This helps maintain reliability even as access becomes easier.

Many organizations also pair conversational analytics with broader business intelligence modernization efforts. As teams introduce natural language access to insights, they often evaluate how Looker can support a more scalable analytics foundation. Learn more about Looker BI migrations here.

3.Prerequisites for Successful Deployment

Before enabling conversational analytics, certain foundations must be in place.

• Clear metric definitions The LookML semantic layer should contain accurate and consistent measures and dimensions. Conversational responses depend on these definitions.

• Organized Explores Explores must be structured and relevant. Users rely on these models when asking conversational questions.

• Stable joins and relationships Clean data relationships reduce confusion when queries span multiple tables.

• Appropriate user permissions Row level security, folder access and user attributes determine what information users can view. Conversational analytics respects these configurations automatically.
  

These prerequisites help ensure that conversational analytics behaves predictably.

4.How Conversational Analytics Works in Looker

When a user asks a question, Looker identifies the relevant fields, measures and filters based on the LookML model and any applicable Data Agent instructions. It then generates and executes the query on the connected database. The result is formatted within the conversational interface. Users can continue refining questions because Looker retains context throughout the session.

If deeper investigation is needed, users can open the Explore associated with the result. This gives them the flexibility to move between conversational and traditional analytical modes.

4.1 Workflow: How a Query Moves Through Looker

A conversational query typically follows this sequence:

1. The user enters a natural language question.

2. Looker interprets the query by referencing the semantic layer.

3. Data Agent instructions influence field selection and filtering if they exist.

4. Looker constructs the underlying query.

5. The database executes the query.

6. The result is returned and displayed as a chart, table or summary.

7. The system stores conversational context for follow-up questions.

8. The user may open the linked Explore to extend the analysis.

This workflow ensures a consistent and governed interaction.

4.2 Architecture Overview

A conversational analytics setup in Looker can be understood in terms of a few key components.

• User interface Users type queries into Looker's conversational panel or an integrated tool such as Slack or Teams.

• Semantic layer LookML provides the metric definitions and relationships that guide how the query is interpreted.

• Data Agents Agents add domain specific instructions for improved interpretation.

• Database layer Looker generates SQL based on the interpretation and runs it against the analytical warehouse.

• Optional MCP layer If AI tools such as Gemini or Claude are used, the Model Context Protocol facilitates communication between the AI client and Looker. The MCP server passes instructions to Looker and returns results to the AI client.
  

This modular architecture supports governed conversational analytics across environments.

4.3 Data Agents and Their Role in AI Driven Analytics in Looker

Data Agents allow teams to define how Looker should interpret conversational inputs. They help align everyday business language with the terms used in the LookML model. For instance, if users commonly refer to locations in a way that differs from the technical field name, a Data Agent can associate the conversational term with the correct dimension.

Data Agents can also define preferred fields, default filters and grouping instructions. These rules guide how queries are formed when the user provides minimal detail. The effect is a more reliable and context-aware interaction with the data.

When Data Agents are shared, the access model remains intact. Shared users do not gain access to additional data unless their permissions already allow it. Agent instructions remain hidden and only the owner can modify them. This helps maintain governance while supporting broader usage.

For a broader view of how AI-driven analytics is shaping decision-making beyond conversational use cases, explore our perspective on AI-driven analytics in Looker.

4.4 Conversational Analytics in Slack, Teams and Embedded Applications

Conversational analytics does not need to stay inside the Looker interface. Many organizations want to work with data inside collaboration tools such as Slack or Microsoft Teams. Looker supports this pattern through its agent-based architecture described in your internal materials.

In this setup, a conversational agent receives the user’s message inside the communication platform, interprets the request and executes the corresponding query through Looker. The result is sent back to the same channel. All Looker governance, permissions and user attributes remain in effect, since the agent queries Looker on behalf of the user.

Agentic BI can support interactions from Slack and other applications, connect to multiple enterprise systems and perform actions through role-based access to various sources.

4.5 Looker and Looker Studio in Context for Conversational BI Solutions

Looker and Looker Studio both provide conversational interfaces but operate differently.

Looker uses its semantic layer to interpret all conversational queries. This ensures that definitions and calculations remain consistent across dashboards, Explores and conversational responses.

Looker Studio allows conversational analytics across a variety of data sources including Looker Explores, BigQuery tables, Google Sheets and CSV files. This broad accessibility offers flexibility, although it does not preserve semantic governance in the same way. Organizations choose between them based on their need for consistency and oversight. 

5. Applications Across Business Functions

Conversational analytics supports a variety of analytical tasks in day to day work.

• Marketing Teams can inquire about campaign performance, channel trends or audience behaviors during planning sessions or performance reviews.

• Ecommerce Managers can quickly request information about sales, product performance or daily trends without waiting for dashboard updates.

• Operations Operational leads can ask about delays, workload distribution or exceptions in a straightforward way during coordination activities.

• Finance Users can access summaries of expenses, revenue or cost patterns as part of review cycles.

• SaaS and multi tenant environments Looker enforces tenant-level isolation through user attributes and row level security. Conversational analytics respects these rules and returns only the data that the user is authorized to view. This is important for platforms that embed Looker inside their product.

These use cases illustrate how conversational analytics reduces friction without replacing dashboards.

6. Security and Governance Considerations

Looker applies the same governance principles to conversational analytics that it uses in the rest of the platform.

This includes:

• Row level security Results are limited based on user attributes assigned in the Looker Admin panel. Groups can receive specific values, and LookML Explore filters enforce those rules.

• Folder and content permissions Users only see content they are allowed to access. Folder-level permissions determine the dashboards, Looks and explores available to each user.

• Explore-level permissions Organizations can restrict which Explores users can access. If an Explore is not available to a user, conversational answers based on that Explore will not appear.

• Tenant-level isolation For SaaS platforms, user attributes can define the tenant identifier. LookML references these attributes to ensure that all queries are automatically scoped to the correct tenant. BigQuery connections can also be configured per tenant.

This model ensures that conversational analytics remains secure and compliant.

7.Limitations and Practical Expectations

Conversational analytics offers convenience but it is not intended to replace all analytical workflows. Its effectiveness depends on several practical conditions:

1. The LookML model must be consistent, accurate and well defined.

2. Ambiguous questions may require users to refine their phrasing.

3. Data Agents can guide interpretation but cannot correct modeling issues.

4. Complex analytical workflows may still be better served by dashboards or SQL.

5. Conversational analytics in Looker relies on Looker Explores, which may limit certain exploratory scenarios.

Understanding these limitations helps teams deploy the capability effectively.

8.SquareShift’s Conversational Analytics Offerings

SquareShift provides structured programs that help SMEs adopt conversational analytics with clarity and control.

BI Conversations Program

This program begins with discovery, data quality checks and model curation. It continues with Data Agent configuration, testing and rollout. The goal is to create a conversation ready dataset that supports accurate and consistent interactions.

Agentic BI Program

This program defines an analytical workflow that benefits from automation. It prepares the semantic layer, identifies required agents and implements them using Vertex AI. It also integrates the Conversational Analytics API to enable more advanced multi step functionality.

Both programs support stable and predictable deployment. To explore the full suite of conversational analytics services and deployment options, view our conversational analytics offering for Looker.

9.Conclusion

Conversational analytics in Looker creates a clear and approachable way for users to work with data through natural language. It builds on the LookML semantic layer, supports consistent interpretation of metrics and operates reliably across the Looker interface, collaboration platforms and AI assisted workflows.

Organizations benefit most when conversational analytics is introduced with well-defined models, appropriate security controls and a thoughtful rollout plan. With these foundations, teams gain steady and predictable access to information without increasing analytical complexity.

SquareShift supports this shift by helping organizations prepare the semantic layer, refine their conversational datasets and introduce agent driven interactions in a structured and dependable manner. The combined approach offers a practical pathway for adopting conversational analytics in a way that aligns with enterprise requirements and Looker’s expanding direction toward agent supported analysis.

FAQ

What is conversational analytics in Looker?

Conversational analytics in Looker lets users ask questions in plain language and get insights as charts, tables, or summaries. It uses the LookML semantic layer to ensure consistent metrics and business rules, enabling reliable and governed access to business-critical data for faster decision-making.

How does Looker handle natural language queries?

When you type a question, Looker interprets it using the semantic layer and optional Data Agent rules, generates a SQL query, executes it, and returns results while maintaining conversational context for follow-ups. This approach ensures answers are accurate, consistent, and aligned with organizational definitions.

Do I need technical skills to use conversational analytics in Looker?

Not necessarily. Conversational analytics is designed for business users and analysts. You don’t need SQL knowledge to ask questions, though a well-structured LookML model ensures accurate results. It empowers non-technical users to explore data confidently and make informed business decisions without relying on data engineers.

Can conversational analytics work in Slack, Teams, or other apps?

Yes. Looker supports conversational BI through integrated agents in platforms like Slack or Microsoft Teams. All security rules and permissions remain intact while providing natural language access to insights. Users can interact with live data directly within collaboration tools, making analysis faster and more seamless across teams.

What are the prerequisites for successful deployment?

Successful deployment requires clear metric definitions, organized Explores, stable table relationships, and proper user permissions. These foundations ensure reliable, predictable answers. Establishing these prerequisites also reduces errors, simplifies adoption, and creates a smooth experience for users when leveraging conversational analytics across business functions.

What are the limitations of conversational analytics in Looker?

Conversational analytics is convenient but not a replacement for all workflows. Ambiguous questions may require clarification, complex analyses may need dashboards or SQL, and results depend on accurate LookML models and Data Agent configuration. Users should understand its boundaries to maximize value while avoiding reliance on it for highly complex analytics.

How can SquareShift help with conversational analytics in Looker?

SquareShift prepares the semantic layer, configures Data Agents and supports structured rollout through conversational and agent based programs.

Can SquareShift support AI assisted or multi agent workflows in Looker?

Yes. SquareShift implements agent driven architectures that use Vertex AI and the Conversational Analytics API for multi step analytical tasks.