# Data Exploration Phase 1 Structural Understanding

> Sub-skill of data-exploration: Phase 1: Structural Understanding (+2).

- Skill: `vamseeachanta/data-exploration-phase-1-structural-understanding` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vamseeachanta/data-exploration-phase-1-structural-understanding`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/data-exploration-phase-1-structural-understanding/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: vamseeachanta (https://skillmd.com/u/vamseeachanta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vamseeachanta/data-exploration-phase-1-structural-understanding

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# Phase 1: Structural Understanding (+2)

## Phase 1: Structural Understanding


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


## Phase 2: Column-Level Profiling


For each column, compute:

**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
```


## Phase 3: Relationship Discovery


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