Data Explore

data-explore

When to use

Use this the moment you are handed a dataset (file, table, query result) and need to understand it before trusting it. Run it before analysis — skipping profiling is how wrong numbers reach stakeholders.

Not for: answering the business question itself (use data-analyze), significance testing (use data-statistical-analysis), or QA of a finished analysis against stakeholder-readiness (use data-validate).

Method

1. Shape. Rows, columns, and the grain — what one row represents. Confirm the grain explicitly; most errors trace to a wrong grain assumption.
2. Schema and types. Each column's stored type vs its semantic type (dates as strings, IDs as numbers).
3. Completeness. Per column: null count and percentage. Flag mostly-empty columns and columns that should never be null but are.
4. Distributions. Numeric: min, max, mean, median, outlier flags. Categorical: cardinality and top values with frequencies. Dates: range and gaps.
5. Duplicates. Check fully duplicated rows and duplicates on the presumed key. Decision point: if the key is not unique, the real grain is finer than assumed — stop and re-derive it.
6. Integrity. Orphan foreign keys, impossible values (negative ages, future dates), inconsistent categories ("US" vs "USA").
7. Reconcile one total against a known external value to confirm the extract is complete.

Example

A 100k-row orders extract assumed one row per order. Duplicate check on order_id:

SELECT order_id, COUNT(*) c
FROM orders GROUP BY order_id HAVING COUNT(*) > 1;

Returns 12k keys with 2+ rows. The real grain is order-line, not order — any per-order SUM would have double-counted. Verdict: usable with caveats, aggregate to order_id first.

Pitfalls

• Assuming the grain from the table name instead of proving it with a key-uniqueness check.
• Reading null percentage without asking which columns are not allowed to be null.
• Treating high-cardinality free-text ("USA", "U.S.A.", "us") as clean categories.
• Profiling a partial extract and never reconciling the total, so completeness is unknown.

Output format

Overview:       rows, columns, grain, date range (first line = grain)
Column profile: | name | type | %null | cardinality/range | notable |
Quality flags:  ranked issues (dupes, nulls, impossible, inconsistent) + counts + severity
Verdict:        ready / ready-with-caveats / not-usable — and what to fix

Never proceed to analysis silently past a quality flag — surface it.