Understanding Results

When the AI Analyst answers your question, it returns a structured response with multiple components. This page explains what each part means and how to use it.

Response Components

A typical response includes:

Component	Description
Answer	Natural language summary of the results, or a short confirmation for raw data pulls
Chart	Visual representation of the data (when applicable)
Data Table	The underlying numbers in tabular format
SQL Query	The exact query used to retrieve the data
Files	CSV, SQL, and chart attachments when applicable

The Answer

The AI Analyst summarizes findings in plain language. This is designed to give you the key insight without needing to parse raw data. Example:

Your total revenue last week was $142,350, down 8% from the previous week. The top channel was Paid Social at$ 52,100 (37% of total).

The answer prioritizes:

The specific number you asked for
Relevant context (comparisons, breakdowns)
Highlighting notable patterns

For raw data pulls, the answer is intentionally shorter. It confirms what was pulled, the time range covered, and the fields included without adding trends or recommendations you did not ask for.

Charts

When your question lends itself to visualization, the AI Analyst generates a chart. Common chart types:

Chart Type	Used For
Bar chart	Comparisons across categories (channels, products, campaigns)
Line chart	Trends over time (daily revenue, weekly orders)
Table	Detailed breakdowns with multiple metrics

Charts are generated as images and posted directly in the Slack thread.

If a chart doesn’t appear, the AI Analyst determined the data was better presented as text, a table, or a CSV. Raw data pulls skip charts by default.

Data Tables

For questions that return multiple rows, you’ll see a data table showing the raw results. This is the actual output from BigQuery, limited to a reasonable preview size. Example:

channel	revenue	orders
Paid Social	52,100	412
Email	38,200	298
Organic Search	28,750	215

You can download the full dataset as a CSV file attached to the response.

Raw Data Pull Tables

When you ask to pull or export data, the table preview shows a sample of the returned rows. The full result is attached as results.csv.

Raw pulls work best when you name the exact fields you need. For example: “Pull order ID, order date, customer email, UTM source, UTM medium, UTM campaign, and net revenue for last week.”

The SQL Query

Every data response includes the SQL query used. This is useful for:

Verification — Confirm the AI understood your question correctly
Learning — See how to write similar queries yourself using SourceMedium table schemas
Iteration — Copy and modify the query in BigQuery for deeper follow-up analysis

Use the AI as a SQL tutor. If you’re learning BigQuery, comparing the generated SQL against our Table Schemas is the fastest way to master your data structure. You can copy any query directly into the BigQuery console to test your own modifications.

Example:

SELECT
  sm_channel AS channel,
  SUM(order_net_revenue) AS revenue,
  COUNT(DISTINCT sm_order_key) AS orders
FROM `your_project.sm_transformed_v2.obt_orders`
WHERE is_order_sm_valid = TRUE
  AND DATE(order_processed_at_local_datetime) BETWEEN '2024-01-08' AND '2024-01-14'
GROUP BY 1
ORDER BY 2 DESC

If the results don’t match your expectations, check the SQL to see how the AI Analyst interpreted your question. Date ranges and filters are common sources of misunderstanding.

File Downloads

Responses may include downloadable files:

File	Contents
`query.sql`	The SQL query used
`results.csv`	Full data export (not truncated)
`chart.png`	The visualization as an image

These files are attached to the Slack message and can be downloaded directly.

For raw data pulls, results.csv is attached automatically even if you did not explicitly ask for a CSV.

Follow-Up Responses

In the same Slack thread, you can ask the AI Analyst to summarize, reframe, or give an opinion based on prior analysis:

Summarize this for leadership
What should we do next?
Break that down by channel
Explain the takeaway in plain English

Summary and opinion responses use retained thread context. They do not run fresh SQL unless your follow-up asks for a new computation or breakdown.

In Slack channels, mention the bot when replying in a thread. In direct messages, you can continue the thread normally.

When Things Look Wrong

If results don’t match your expectations:

Check the date range

Review the SQL query to confirm the time period matches your intent.

Verify the metric

Make sure the AI Analyst used the metric you expected (e.g., gross revenue vs. net revenue).

Check data freshness

Ask “How is my data health?” to confirm the underlying tables are up to date.

Rephrase and retry

If the AI misunderstood, rephrase with more specificity and try again.

Troubleshooting

Common issues and how to resolve them.

Data Health Check

Verify your data is fresh before querying.

Going Deeper

Once you have results, you can explore further:

BigQuery Essentials

Learn to run and modify queries directly in BigQuery.

Looker Studio Guide

Build custom visualizations from your data.

Getting Started

Workflows

Diagnostics

Agent Skills

Reference

Response Components

The Answer

Charts

Data Tables

Raw Data Pull Tables

The SQL Query

File Downloads

Follow-Up Responses

When Things Look Wrong

Troubleshooting

Data Health Check

Going Deeper

BigQuery Essentials

Looker Studio Guide

​Response Components

​The Answer

​Charts

​Data Tables

​Raw Data Pull Tables

​The SQL Query

​File Downloads

​Follow-Up Responses

​When Things Look Wrong

Troubleshooting

Data Health Check

​Going Deeper

BigQuery Essentials

Looker Studio Guide

Response Components

The Answer

Charts

Data Tables

Raw Data Pull Tables

The SQL Query

File Downloads

Follow-Up Responses

When Things Look Wrong

Going Deeper