Data Analyst

Bring a data task and Opulent works in a data-analysis lane tuned for fast queries, analysis, and visualizations

Data Analyst is a capability lane inside Opulent — not a separate product or agent. When you bring a data-shaped task (a database, a CSV, a spreadsheet, a chart, a KPI, a SQL or calc question), your Session runs in a data-analysis lane that's fast, concise, and tuned for querying databases, analyzing data, and creating visualizations.

When to use the data-analysis lane

The data-analysis lane is ideal when you need to:

• Query databases: Write and execute SQL queries against your connected data sources
• Analyze data: Explore patterns, calculate metrics, and investigate trends across datasets, CSVs, and spreadsheets
• Create visualizations: Generate professional charts and graphs using seaborn
• Answer data questions: Get quick, accurate answers about your data and KPIs
• Generate insights: Discover patterns, anomalies, and actionable findings

Starting a data-analysis Session

From the web app

1. Go to the Opulent home page at platform.opulentia.ai
2. Start a new Session
3. Describe a data-related question or task
4. Opulent recognizes data work and runs the Session in the data-analysis lane — no separate agent to pick

From Slack

You can start a data-analysis Session directly from Slack by mentioning Opulent with a data question:

@Opulent What were our top 10 customers by revenue last month?

Opulent detects the data question, runs the Session in the data-analysis lane, and replies in-thread with the results.

Prerequisites

Before running a data-analysis Session, you'll need to connect at least one data source through the Connector Marketplace (approved MCP servers plus native connectors). Common integrations include:

• Database connectors: Redshift, PostgreSQL, Snowflake, BigQuery, and other SQL databases
• Analytics connectors: Datadog, Metabase, and other observability platforms

Without a connected data source, Opulent will notify you and ask you to connect one before proceeding.

Learn how to connect databases and other data sources

How it works

Database Knowledge

The data-analysis lane maintains a Database Knowledge note that holds schema documentation for your connected databases. This Knowledge is automatically referenced before running queries, letting Opulent quickly identify the right tables and columns. (This is Knowledge — stable schema facts — not Memory.)

Example prompts

Here are some effective ways to work in the data-analysis lane across different query types:

Simple lookups

• "How many active users did we have last week?"
• "What's our daily revenue trend for the past month?"
• "Which customers have the highest usage?"

Aggregations and metrics

• "What's the average session duration by plan tier for the past 30 days?"
• "Show me total revenue grouped by region and product line for Q4"
• "Calculate the 95th percentile response time for each API endpoint this week"

Joins and cross-table analysis

• "Join our users table with the orders table and show the top 20 customers by lifetime value"
• "Correlate signup source with 30-day retention — which acquisition channels have the best retention rates?"
• "Combine session data with billing records to find accounts with high usage but low spend"

Filtering and segmentation

• "Show me only enterprise customers who signed up after January 2025 and have more than 100 sessions"
• "Filter error logs to 5xx errors from the payments service in the last 48 hours"
• "Break down consumption by enterprise vs. self-serve customers, excluding trial accounts"

Time-series analysis

• "Plot weekly active users over the past 6 months — highlight any weeks with more than 10% change"
• "Show me a month-over-month comparison of signup rates for 2025 vs. 2024"
• "What's the daily trend for API calls over the past 90 days? Overlay a 7-day moving average"

Investigations and anomaly detection

• "Why did signups drop last Tuesday? Check if there were any related incidents or deployments"
• "Are there any anomalies in our error rates this week?"
• "Compare this month's metrics to the same period last year and flag significant deviations"

Multi-step analysis

• "Analyze user retention by cohort for Q4, then identify which cohorts have the steepest drop-off and suggest possible causes"
• "Find the top 10 users by session count, show their activity over time, and flag any that look like potential churns"

Supported data sources

The data-analysis lane connects to your data through the Connector Marketplace. You can connect multiple data sources and query across them. Below are some of the most common data sources available in the Connector Marketplace — this is not an exhaustive list.

SQL databases

Data source	Connector	Setup
Amazon Redshift	Redshift	Connection string + credentials
PostgreSQL	PostgreSQL	Connection string
Snowflake	Snowflake	Account + credentials
Google BigQuery	BigQuery	OAuth or service account
MySQL	MySQL	Connection string
SQL Server	SQL Server	Connection string
Neon	Neon	OAuth
Supabase	Supabase	Personal access token
Cloud SQL (PostgreSQL, MySQL, SQL Server)	Cloud SQL	OAuth

Analytics and observability platforms

Data source	Connector	Setup
Datadog	Datadog	API key + app key
Metabase	Metabase	OAuth
Grafana	Grafana	URL + service account token
Sentry	Sentry	OAuth

Connecting a data source

1. Navigate to Settings > Connections > MCP servers
2. Find your data source and click Enable
3. Provide any required credentials (connection strings, API keys, or OAuth)
4. Start a data-analysis Session — Opulent automatically discovers your connected data sources

Need a data source that isn't in the Marketplace? Use Add Your Own to connect any MCP server by providing its configuration directly.

Full setup instructions for each data source

Best practices

Be specific about metrics

Instead of asking vague questions, define exactly what you want to measure:

"What's our 7-day active user count, defined as users who started at least one session?"

"How are our users doing?"

Specify time periods

Always include the time range you're interested in. Opulent defaults to UTC when interpreting relative dates.

"Show me daily revenue for the past 30 days"

"Show me revenue"

Request specific output formats

Tell Opulent how you want to see results — as a table, chart, or summary:

"Plot a line chart of weekly signups for the past quarter, with a table of the raw numbers below"

"Get signup numbers"

Define business logic upfront

If your metrics have specific definitions, state them in your prompt to avoid ambiguity:

"Show monthly churn rate, where churn is defined as accounts with zero sessions in the past 30 days that had at least one session in the prior 30 days"

"What's our churn rate?"

Ask for comparisons and context

Adding comparison periods or benchmarks makes results more actionable:

"Show this week's daily active users compared to the same week last month, and highlight any days with more than 15% deviation"

"Show daily active users"

Iterate on results

You can ask follow-up questions in the same Session to drill deeper:

1. Start broad: "What are our top 10 customers by revenue this quarter?"
2. Drill down: "For the top 3, show me their monthly revenue trend over the past year"
3. Investigate: "Customer X had a revenue spike in March — what drove that?"

Validate the SQL

Opulent always includes the SQL query it used. Review it to ensure the logic matches your expectations, especially for complex analyses involving joins, filters, or aggregations.

Output formats

The data-analysis lane returns results in several formats depending on the type of analysis:

Tables

For data lookups and aggregations, results are returned as formatted tables:

| Customer       | Revenue   | Sessions | Avg Duration |
|----------------|-----------|----------|--------------|
| Acme Corp      | $125,400  | 1,247    | 34 min       |
| Globex Inc     | $98,200   | 983      | 28 min       |
| Initech        | $87,600   | 876      | 41 min       |

Charts and visualizations

When you request visual analysis or the data is best understood graphically, Opulent generates charts using seaborn. Common chart types include:

• Line charts — time-series trends, comparisons over time
• Bar charts — categorical comparisons, rankings
• Heatmaps — correlation matrices, activity patterns
• Scatter plots — relationship analysis between two metrics

Summaries and insights

For investigation-style prompts, Opulent provides a structured response that includes:

• Analysis summary — a plain-language answer to your question
• SQL query — the exact query used, so you can verify the logic
• Key numbers — the most important metrics highlighted
• Data insights — patterns, anomalies, or notable findings
• Metabase link — if your organization has Metabase connected through the Connector Marketplace, Opulent may include a link to an interactive dashboard for further exploration

Knowledge management

The data-analysis lane can persist learnings across Sessions using the Knowledge system. When it discovers:

• New schema information or table relationships
• Business logic or metric definitions
• Data quality patterns or caveats

It saves these to Knowledge notes so future Sessions benefit from what was learned. (Durable schema and metric facts live in Knowledge — kept distinct from Memory, which holds preferences and project decisions.)

Understand how Opulent's Knowledge system works

The data-analysis lane vs a general Session

The same Opulent handles both — the difference is emphasis, not a separate SKU:

Capability	Data-analysis lane	General Session
SQL query execution	Optimized	Supported
Data visualizations	Built-in seaborn support	Manual setup
Database schema awareness	Pre-loaded Knowledge	On-demand exploration
Response style	Concise, metrics-focused	Detailed explanations
Code changes	Not primary focus	Full support
Connectors	Required	Optional

The data-analysis lane is tuned for data work. For tasks involving code changes, deployments, or general software engineering, just describe that task — the same Opulent Session handles it.