Explore Data

# /explore-data - Profile and Explore a Dataset

> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).

Generate a comprehensive data profile for a table or uploaded file. Understand its shape, quality, and patterns before diving into analysis.

## Usage

``` /explore-data <table_name or file> ```

## Workflow

### 1. Access the Data

**If a data warehouse MCP server is connected:**

1. Resolve the table name (handle schema prefixes, suggest matches if ambiguous) 2. Query table metadata: column names, types, descriptions if available 3. Run profiling queries against the live data

**If a file is provided (CSV, Excel, Parquet, JSON):**

1. Read the file and load into a working dataset 2. Infer column types from the data

**If neither:**

1. Ask the user to provide a table name (with their warehouse connected) or upload a file 2. If they describe a table schema, provide guidance on what profiling queries to run

### 2. Understand Structure

Before analyzing any data, understand its structure:

**Table-level questions:** - How many rows and columns? - What is the grain (one row per what)? - What is the primary key? Is it unique? - When was the data last updated? - How far back does the data go?

**Column classification** — categorize each column as one of: - **Identifier**: Unique keys, foreign keys, entity IDs - **Dimension**: Categorical attributes for grouping/filtering (status, type, region, category) - **Metric**: Quantitative values for measurement (revenue, count, duration, score) - **Temporal**: Dates and timestamps (created_at, updated_at, event_date) - **Text**: Free-form text fields (description, notes, name) - **Boolean**: True/false flags - **Structural**: JSON, arrays, nested structures

### 3. Generate Data Profile

Run the following profiling checks:

**Table-level metrics:** - Total row count - Column count and types breakdown - Approximate table size (if available from metadata) - Date range coverage (min/max of date columns)

**All columns:** - Null count and null rate - Distinct count and cardinality ratio (distinct / total) - Most common values (top 5-10 with frequencies) - Least common values (bottom 5 to spot anomalies)

**Numeric columns (metrics):** ``` min, max, mean, median (p50) standard deviation percentiles: p1, p5, p25, p75, p95, p99 zero count negative count (if unexpected) ```

**String columns (dimensions, text):** ``` min length, max length, avg length empty string count pattern analysis (do values follow a format?) case consistency (all upper, all lower, mixed?) leading/trailing whitespace count ```

**Date/timestamp columns:** ``` min date, max date null dates future dates (if unexpected) distribution by month/week gaps in time series ```

**Boolean columns:** ``` true count, false count, null count true rate ```

**Present the profile as a clean summary table**, grouped by column type (dimensions, metrics, dates, IDs).

### 4. Identify Data Quality Issues

Apply the quality assessment framework below. Flag potential problems:

- **High null rates**: Columns with >5% nulls (warn), >20% nulls (alert) - **Low cardinality surprises**: Columns that should be high-cardinality but aren't (e.g., a "user_id" with only 50 distinct values) - **High cardinality surprises**: Columns that should be categorical but have too many distinct values - **Suspicious values**: Negative amounts where only positive expected, future dates in historical data, obviously placeholder values (e.g., "N/A", "TBD", "test", "999999") - **Duplicate detection**: Check if there's a natural key and whether it has duplicates - **Distribution skew**: Extremely skewed numeric distributions that could affect averages - **Encoding issues**: Mixed case in categorical fields, trailing whitespace, inconsistent formats

### 5. Discover Relationships and Patterns

After profiling individual columns:

- **Foreign key candidates**: ID columns that might link to other tables - **Hierarchies**: Columns that form natural drill-down paths (country > state > city) - **Correlations**: Numeric columns that move together - **Derived columns**: Columns that appear to be computed from others - **Redundant columns**: Columns with identical or near-identical information

### 6. Suggest Interesting Dimensions and Metrics

Based on the column profile, recommend:

- **Best dimension columns** for slicing data (categorical columns with reasonable cardinality, 3-50 values) - **Key metric columns** for measurement (numeric columns with meaningful distributions) - **Time columns** suitable for trend analysis - **Natural groupings** or hierarchies apparent in the data - **Potential join keys** linking to other tables (ID columns, foreign keys)

### 7. Recommend Follow-Up Analyses

Suggest 3-5 specific analyses the user could run next:

- "Trend analysis on [metric] by [time_column] grouped by [dimension]" - "Distribution deep-dive on [skewed_column] to understand outliers"