Converse with Looker Studio data

Preview

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This page guides you through navigating to Conversational Analytics in Looker Studio and starting and conversations with your data. To learn how to connect to a data source, see the Set up Conversational Analytics in Looker Studio documentation page.

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Navigate to Conversational Analytics

You can access Conversational Analytics from Looker Studio in the following ways:

• Navigate directly to Conversational Analytics.
• Choose Conversational Analytics from the navigation panel of Looker Studio.
• Choose Conversation from the Create menu of Looker Studio if you are in your Sandbox workspace.

Note: If data agents have been created for your instance, navigate to the Data sources tab on the Chat with your data page to use Conversational Analytics.

Start a conversation

Sets of questions that you ask about a dataset are organized by conversation. Splitting work into multiple conversations can be useful for organizing lines of inquiry. To create a new conversation, follow these steps:

1. Click + Create conversation within Conversational Analytics.

2. Select the data source that you want to investigate or the data agent that you want to use for your conversation:

   • Data source: To start a conversation based on an existing data source, select the Data source panel, and then select a data source. To create a new data source, select Connect to data.

   • Data agent: To start a conversation with an existing data agent, select Agents, and then select a data agent. To create a new data agent, select + Create agent.

3. To start the conversation, enter your question and press return (Mac) or Enter (PC).

You can return to the conversation from the schedule Recent section.

Ask questions

You can ask questions to get insights from your data. You can use suggested questions as a starting point for exploring data and building familiarity with Conversational Analytics.

Ask questions about a data source

Note: Conversational Analytics uses the Production Mode of content when querying Looker.

Once you have created a conversation, you can ask questions about the data in the send_spark Ask a question field within the conversation.

The questions don't need to be in a specific format or use a specific syntax. However, they do need to relate to the data source that you've selected. Conversational Analytics may rephrase your question after you've written a query, and the rephrased question will be displayed in the conversation window following your original question. For example, Conversational Analytics might rephrase the question "What is the mean of user ages?" to "What is the average user age?"

Conversational Analytics will take previous questions and answers into account as you continue the conversation. You can take previous answers and build on them by further refining results or changing the visualization type.

Note: You cannot reference previous questions and answers from other conversations, only the one that you are actively in.

For more guidance on creating questions, see Limitations on questions.

Converse with a Looker data source

Once you've created a new conversation that is connected to a Looker Explore, you can ask questions about your Looker data.

When you converse with your data, the collapsible storage Data panel shows the name of the Looker Explore that is being used by the conversation. The storage Data panel also provides the following options:

• open_in_new View fields: View the Explore in Looker in a new browser window by clicking open_in_new View fields.
• chat_spark New conversation: Start a new conversation with the Looker Explore that the current conversation is using.

Open in a Looker Explore

To open the query results as an Explore within the connected Looker instance, click explore Open in Explore within the query results.

Converse with BigQuery data

Once you've connected to a BigQuery data source, you can ask questions about your BigQuery data.

When you converse with your data, the collapsible storage Data panel shows the name of the BigQuery table that the conversation is using. The storage Data panel also provides the following options:

• open_in_new View fields: View the table in BigQuery in a new browser tab.
• chat_spark New conversation: Start a new conversation with the BigQuery data that the current conversation is using.

Manage queries within a conversation

When you converse with data, you can manage the conversation by stopping an active query response while it is running or by deleting the most recent question and its response.

Stop a query response

To stop running a query after you've sent a message, click Stop response. Conversational Analytics stops running the query and displays the following message: The query was cancelled.

Delete the most recent question

To delete the most recent question and its response, follow these steps:

1. Hold your cursor over the most recent question, and then click delete Delete message.
2. In the Permanently delete message? dialog, click Delete to permanently delete the question and its response.

Understand query results and calculations

When you ask questions about your data in Conversational Analytics, the response might include a visualization, a data table, or other details, depending on your specific query and the connected data.

In addition to this query response, Conversational Analytics provides the following options for understanding query results and calculations:

• Details about how the answer was calculated
• If available, the option to view additional insights about the response

Determine how an answer was calculated

To see how Conversational Analytics arrived at an answer or created a visualization, click keyboard_arrow_down How was this calculated? within the query results.

When you click How was this calculated?, Conversational Analytics displays the following tabs:

• Code: Displays the SQL query that was run to generate the result. If you connect Conversational Analytics to a BigQuery table, the Code tab shows the generated BigQuery SQL.

  Note: If you have enabled the Code Interpreter by turning on the Advanced analytics option for Conversational Analytics, then the Code tab displays the additional generated Python code for any advanced queries.

• Text: Provides a plain text explanation of the steps that Conversational Analytics took to arrive at the given answer. This explanation includes the raw field names that were used, the calculations that were done, the filters that were applied, the sort order, and other details.

Get additional insights

When Conversational Analytics is able to provide additional data insights about a response, an Insights keyboard_arrow_down button will appear. Click Insights keyboard_arrow_down to see additional information about your query. Insights only analyzes the data that was returned by your prompt and won't run additional queries to fetch additional data. Insights can be a useful source for ideas for follow-up questions to continue the conversation.

The following is an example of some insights that might be returned by the prompt "How many users are in each state?":

• A general summary of high and low data volume areas. For example:
  • "California, Texas, and Ohio are key states for business operations based on the data provided."
  • "England and specific regions in China, namely Anhui and Guangdong, show significant business activity."
  • "Some states, including Mie, Akita, and Iwate, have minimal presence based on the data."
• An assessment of the variability of the dataset. For example, "The data indicates varying operational scales across different locations."

Manage conversations

You can change the name of conversations, delete conversations, or restore them from the trash folder.

Name a conversation

Conversational Analytics automatically generates a conversation title that is based on your first question and response. To change the generated name, follow these steps:

1. Click the title at the top of the conversation page.
2. Enter a new conversation name.
3. To save your changes, click elsewhere on the page, or press return (Mac) or Enter (PC).

Delete a conversation

To move a conversation to the trash, open the conversation and click Move to trash.

Note: Conversations that remain in the trash for more than 30 days are automatically deleted.

Restore or permanently delete a conversation

To restore or permanently delete a conversation from the trash, follow these steps:

1. Within Conversational Analytics, select Trash in the left navigation panel to view the list of conversations that have been moved to the trash.
2. In the Trash section, click the name of the conversation that you want to restore or permanently delete.
3. In the Are you sure? dialog, select one of the following options:
   • Cancel: Cancels the action.
   • Restore: Restores the conversation. The conversation can be accessed from the Recent section of the left navigation menu within Conversational Analytics.
   • Delete forever: Permanently deletes the conversation.

Search conversations

To search for a specific conversation by title, follow these steps:

1. In the Search Conversational Analytics search bar, enter your search query. As you type, a list of conversations with titles that match your search query will appear.
2. Select a conversation from the search results to open that conversation.

Known limitations

Conversational Analytics has the following known limitations:

• Limitations on visualizations
• Limitations on data sources
• Limitations on questions

Limitations on visualizations

Conversational Analytics leverages Vega-lite for conversation chart generation. The following Vega chart types are fully supported:

• Line chart (one or more series)
• Area chart
• Bar chart (horizontal, vertical, stacked)
• Scatter plot (one or more groups)
• Pie chart

The following Vega chart types are supported, but you may encounter unexpected behavior when rendering them:

• Maps
• Heatmaps
• Charts with tooltips

Chart types that exist outside the Vega catalog are not supported. Any charts that are not specified in this section are considered unsupported.

Limitations on data sources

• For Looker data sources, the following limitations apply:
  • Conversational Analytics cannot set the value of a filter-only that is defined using the LookML parameter parameter.
  • Conversational Analytics can return a maximum of 5,000 rows per query.
• For BigQuery data sources, the following limitations apply:
  • You can converse with only one BigQuery table at a time. To converse with a different BigQuery table or with a data agent that uses a different BigQuery table, start a new conversation.
  • Conversational Analytics doesn't support BigQuery's Flexible Column Names feature.
• Conversational Analytics doesn't work well with data sources that have field editing in reports disabled because this setting prevents Conversational Analytics from creating calculated fields.

Limitations on questions

Conversational Analytics supports questions that can be answered by a single visualization, for example:

• Metric trends over time
• Breakdown or distribution of a metric by dimension
• Unique values for one or more dimensions
• Single metric values
• The top dimension values by metric

Conversational Analytics doesn't yet support questions that can only be answered with the following types of complicated visualizations:

• Prediction and forecasting
• Advanced statistical analysis, including correlation and anomaly detection

More advanced questions, such as forecasting, can be answered when the Code Interpreter is enabled.

Sample conversation

The following sample conversation shows how a user can interact with Conversational Analytics in a natural, back-and-forth way. In this example, the user asks the following question: "Can you plot monthly sales of hot drinks versus smoothies for 2023, and highlight the top selling month for each type of drink?" Conversational Analytics responds by generating a line graph that displays the monthly sales of hot drinks and smoothies for 2023, highlighting July as the month with the highest sales for both categories.

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As this sample conversation illustrates, Conversational Analytics interprets natural language requests, including multi-part questions that use common terms like "sales" and "hot drinks," without requiring users to specify exact database field names (like Total monthly drink sales) or define filter conditions (like type of beverage = hot). Conversational Analytics describes its key findings, explains its reasoning, and provides an answer that includes text and, where appropriate, a chart. To encourage deeper analysis, Conversational Analytics may also suggest follow-up questions.

Related resources

• Conversational Analytics overview: The landing page for Conversational Analytics contains setup requirements, known limitations, supported question types, and more.

• Create and converse with data agents: With data agents, you can customize the AI-powered data querying agent by providing context and instructions that are specific to your data, which helps Conversational Analytics generate more accurate and contextually relevant responses.

• Enable advanced analytics with the Code Interpreter: The Code Interpreter within Conversational Analytics translates your natural language questions into Python code and executes that code. Compared to standard SQL-based queries, the Code Interpreter's use of Python enables more complex analysis and visualizations.