Ad-hoc Data Exploration

Learn how analysts use Slateo to go from question to answer quickly with natural language queries and intelligent suggestions. This guide walks through a real-world exploration workflow.

Overview

Ad-hoc data exploration is the most common use case for Slateo. Instead of writing SQL from scratch or waiting for someone else to pull data, analysts can ask questions in natural language and get immediate results.

This guide shows how a typical analyst uses Slateo to:

• Ask questions in plain English
• Iterate on results with follow-up questions
• Explore data without knowing the exact schema
• Rely on auto-saved query history
• Share findings with stakeholders

The workflow

A typical ad-hoc exploration session follows this pattern:

1. Ask a question - Start with a natural language query
2. Review results - Check if the data answers your question
3. Refine - Ask follow-ups to drill deeper or adjust the query
4. Auto-save - Query history is saved automatically as you work
5. Share - Send results to stakeholders via Slack or email

Starting with a question

Open the Slateo query editor and type your question. The agent understands context about your data warehouse and can translate natural language into SQL.

Example questions

Here are real questions analysts ask:

What were our top 10 customers by revenue last quarter?

Show me daily active users for the past 30 days

Which products had the biggest drop in sales this month?

The agent will:

• Identify relevant tables and columns
• Generate appropriate SQL with filters and aggregations
• Execute the query and return results
• Explain what it found

Refining results

After seeing initial results, you often need to dig deeper. Instead of writing a new query from scratch, ask follow-up questions in the same conversation.

Follow-up examples

Can you break that down by product category?

Show me the same data but for the previous quarter

Filter to only customers who spent more than $10,000

The agent maintains context from your conversation, so it knows which query you're referring to and can modify it accordingly.

Iterating quickly

This conversational approach is faster than traditional SQL workflows:

• Traditional: Write query → Run → See results → Edit SQL → Run again → Repeat
• With Slateo: Ask question → See results → Ask follow-up → See refined results

You spend less time writing SQL and more time analyzing data.

Auto-save and sharing

Slateo auto-saves your query work so you can focus on analysis instead of manual save steps.

Auto-saved queries appear in your workspace and can be:

• Re-run with updated data
• Shared with team members
• Referenced in reports
• Scheduled for regular execution

Sharing results

Share findings directly from Slateo:

• Slack: Post results to a channel with @slateo share to #analytics
• Email: Export as CSV and attach to an email
• Reports: Add the query to a dashboard for ongoing monitoring

Best practices

Follow these tips for effective ad-hoc exploration:

Start broad, then narrow

Begin with a general question, then use follow-ups to drill into specifics:

1. "Show me sales by region"
2. "Focus on the Northeast region"
3. "Break down by product category"
4. "Show only categories with declining sales"

Use relative time periods

Instead of hardcoding dates, use relative terms:

• "last 30 days" instead of "2025-02-25 to 2025-03-27"
• "previous quarter" instead of "Q4 2024"
• "year over year" instead of specific date ranges

This makes queries reusable and easier to understand.

Name queries clearly

When organizing queries, use descriptive names that explain:

• What the query shows
• When the data is from (if relevant)
• Why you created it

Good: "Weekly Active Users - Mobile App - Last 90 Days" Bad: "Query 47"

Ask for explanations

If results are surprising, ask the agent to explain:

Why did sales drop in March?

What's causing the spike in user signups?

The agent can analyze patterns and suggest potential causes.

Build a query library

Save frequently-used queries so your team can:

• Find answers without asking the same questions
• Learn from each other's exploration patterns
• Build on existing work instead of starting from scratch

What's next?

Now that you understand ad-hoc exploration, learn about:

• Setting up Slateo for your data team - Best practices for workspace structure and permissions
• Building and sharing reports - Turn queries into dashboards
• Using Slateo with Slack - Answer data questions without leaving chat