How to Set Up ThoughtSpot Spotter

ThoughtSpot Spotter lets analysts and operators query company data using plain English. You connect a data source, whether a CSV file or a cloud data warehouse like Snowflake or BigQuery, select the tables you want to analyze, and type questions into a chat interface. Spotter generates charts and summaries from those questions automatically. No SQL knowledge is required to run analysis or build a dashboard.

What ThoughtSpot Spotter Does

ThoughtSpot is an analytics platform originally built around a natural language search bar. You type a business question like "show me revenue by region for Q1" and the platform returns a chart or table. Spotter, the latest iteration released as an agentic AI analyst, extends this to a multi-turn conversational format where you can drill into results, ask follow-up questions, and refine outputs without restarting each time.

The platform differs from traditional BI tools like Tableau or Power BI in that it does not require building reports or dashboards in advance. The analysis happens on demand. Spotter reads your data directly from the connected source and generates answers in real time.

The free tier supports up to 1 million rows and five user accounts. Cloud data warehouses are the primary connection type for enterprise use, but CSV uploads and Google Sheets are available for teams that want to evaluate the platform without a warehouse setup.

Step 1: Sign Up for the Free Trial

Go to thoughtspot.com and click the option to start a free trial. You will be asked for a work email address and basic company details. No credit card is required for the free tier.

Once logged in, the ThoughtSpot home interface appears. For first-time users, a prompt offers to connect you directly to Spotter. Click "Try Spotter on your own data" to begin the data connection flow.

Step 2: Connect Your Data

ThoughtSpot supports three methods of connecting data to Spotter.

Upload a CSV file. Click "Upload a CSV file" and drag your file into the upload area. After upload, ThoughtSpot displays a preview showing column names and detected data types. You can rename columns to make them more readable, for example changing "cust_rev_q1" to "Customer Revenue Q1," and correct any data type mismatches before confirming. Click Next to finalize.

Connect a Google Sheet. Authorize ThoughtSpot to access your Google account through standard OAuth, then select the Sheet and tab you want to analyze. No API keys are required.

Connect a cloud data warehouse. Choose your warehouse type from the supported list: Snowflake, Google BigQuery, Amazon Redshift, Databricks, and Azure Synapse are available in the current release. For Snowflake connections, you provide your account identifier, username, password, and the target database and schema. For BigQuery, you enter the project ID and upload a service account JSON key.

Once connected, ThoughtSpot shows the available tables within the connection. You can select a single table for immediate querying, or select multiple tables and define their relationships before starting.

Step 3: Model Your Data

Most setup guides skip this step, but it has a significant effect on result quality. Before querying, ThoughtSpot's data model editor lets you define which columns are measures (numbers to aggregate) and which are dimensions (categories to group by).

Marking a column like "sale_amount" as a measure and "product_category" as a dimension means Spotter aggregates the measure and slices it by the dimension without needing explicit instructions in every query. Without this configuration, Spotter infers types from column names and values, which works for clean data but can produce unexpected aggregations for columns with ambiguous names.

To configure this, go to the Data section in the top navigation after connecting your source, select the table, and edit the column type definitions. For a table with ten to twenty columns, this takes roughly five minutes. Investing time here consistently improves the accuracy of Spotter's responses, particularly for columns that combine numeric IDs with financial figures.

According to ThoughtSpot's documentation, teams that label measures and dimensions before querying see a measurable reduction in follow-up clarifications from Spotter compared to teams that skip the modeling step.

Step 4: Ask Questions in Spotter

With data connected, open Spotter by clicking the icon in the left navigation or selecting it from the search bar. The interface is a chat window. Type your question in plain English.

Example queries that work reliably:

• "What are total sales by product category for the last 90 days?"
• "Show me customers with revenue above $10,000 sorted by purchase date"
• "Which region had the highest number of returns in Q4?"

Spotter returns a chart or table. Each response includes the underlying data and a "Why this chart?" explanation that describes the logic used to select the visualization type.

You can follow up without restarting. If Spotter returns a bar chart, typing "now show this as a line chart grouped by month" updates the chart. You can also ask Spotter to explain a trend: "why did sales drop in October?" triggers a comparison of October against adjacent months and surfaces anomalies or correlations when they exist in the data.

For questions that span multiple tables, Spotter uses the relationship definitions from Step 3 to join data automatically. Multi-table joins work without writing SQL, provided the relationships were defined before querying.

Step 5: Pin Results to a Liveboard

Answers in Spotter are not saved by default. To keep a chart for recurring use, click the pin icon on any result and select an existing Liveboard or create a new one.

A Liveboard is ThoughtSpot's version of a persistent dashboard: a view of multiple pinned charts that updates each time the underlying data changes. Unlike static dashboards built by dragging and dropping components, Liveboards pull live data from the connected source on each view.

You can share a Liveboard with other users by clicking Share in the top right of the Liveboard view and entering their email addresses. Shared users see the same real-time data in their own accounts. The free tier supports up to four additional shared users.

What Analyst Studio Adds

In early 2026, ThoughtSpot released a general availability update to Analyst Studio, the platform's environment for data preparation work that precedes querying. Three additions are relevant to non-enterprise teams.

SpotCache creates data snapshots that Spotter queries without hitting the connected warehouse. For teams on metered cloud warehouse plans where each query carries a cost, SpotCache allows unlimited querying of a cached version without additional warehouse charges.

The spreadsheet interface provides a grid view for data manipulation including column derivations, formula calculations, and customer cohorting. This covers cases where analysts need to transform data before analysis without building a separate pipeline.

Agentic data prep accepts plain-language instructions for transformations. A prompt like "create a column classifying customers as high, medium, or low value based on total spend" generates the logic and applies it to the data model automatically. This extends natural language interaction beyond querying and into the data preparation layer.

Limitations

The free tier caps at 1 million rows, which covers most departmental datasets but requires an upgrade for large transaction or event tables. Row limits apply to the data loaded into ThoughtSpot, not the total size of the connected warehouse.

ThoughtSpot performs best with clean data and descriptive column names. Abbreviated column names like "fld_amt_3" require manual renaming before Spotter can interpret them correctly. Ten minutes spent on column labeling before querying eliminates most cases of incorrect output.

The platform does not support native Python or R execution for custom statistical analysis. Teams that need regression modeling, clustering, or forecasting beyond trend detection will need a separate tool for that work. For teams whose primary workflow is uploading a file and running ad-hoc analysis rather than connecting a warehouse, VSLZ handles that workflow from a single file upload without requiring a data modeling step.

Pricing for paid plans starts at $25 per user per month billed annually. The free trial is full-featured but limited to five users and 1 million rows.

Summary

ThoughtSpot Spotter reduces the barrier to data analysis for teams that do not have SQL skills or the time to build dashboards. The setup process, including connecting a data source, labeling column types, and running first queries, takes under thirty minutes for straightforward datasets. The Liveboard feature makes it practical for recurring reporting without rebuilding charts each session. The 2026 Analyst Studio update extends the platform into data preparation, making it a more complete option for teams that currently split work between a data prep tool and a BI layer.