data-quality

name	data-quality
description	Quality dimensions quick reference and remediation decision tree for tabular data assessment

Data Quality Assessment with qsv

For the full step-by-step profiling workflow, use the /data-profile command. This skill provides quick-reference guidance for quality assessment and remediation decisions.

Quality Dimensions (Quick Reference)

Dimension	Key Question	Primary Check	Red Flag
Completeness	Missing values?	stats — nullcount, sparsity	Sparsity > 0.5
Uniqueness	Unwanted duplicates?	stats --cardinality vs row count	Key column cardinality < row count
Validity	Correct formats/types?	stats — type; validate schema.json	String type on numeric column
Consistency	Uniform formats?	frequency — case variants; sniff — encoding	Same value in different cases
Accuracy	Plausible values?	stats — min/max/stddev	Values > 3 stddev from mean
Column Name Quality	Headers safe & descriptive?	safenames --verify	Spaces, special chars, or duplicates in headers
Conformity	Values follow standards?	searchset with domain regex	Non-standard codes (country, state, zip, phone)
Referential Integrity	Foreign keys valid?	joinp --left-anti	Orphaned references across related files
Injection Safety	Malicious payloads?	searchset with injection regex	Formula/SQL injection patterns in cells
Documentation	Dataset described?	describegpt --all	No Data Dictionary or Description

Remediation Decision Tree

When a quality issue is found, choose the right fix:

Problem	Severity	Fix Command	When to Skip
Ragged rows	High	fixlengths	Never — breaks downstream tools
Wrong encoding	High	input	File is already UTF-8 (check with sniff)
Unsafe column names	Medium	safenames	Headers already safe (no spaces/special chars)
Leading/trailing whitespace	Medium	sqlp with TRIM(col)	Stats show no difference between min/max lengths and trimmed values
Duplicate rows	Medium	dedup (or extdedup for >1GB)	stats --cardinality on key columns shows all unique
Inconsistent case	Low	sqlp with UPPER(col) or LOWER(col)	frequency shows no case variants
Empty values	Low	sqlp with COALESCE(NULLIF(col, ''), 'N/A')	Nulls are semantically meaningful
Non-conforming values	Medium	searchset + search --flag	No domain standard applies
Orphaned foreign keys	Medium	joinp --left-anti	Single-file dataset with no references
Injection payloads	High	searchset with injection regex + sanitize	Data is internal-only and never opened in spreadsheets or loaded into databases
Invalid rows	Low	validate schema.json + filter	No schema available

Fix Ordering

Always apply fixes in this order to avoid cascading issues:

1. input          (encoding — must be UTF-8 before anything else)
2. safenames      (headers — fixes names before column references)
3. fixlengths     (structure — ensures consistent field counts)
4. sqlp with TRIM()    (whitespace — clean values before dedup)
5. dedup          (duplicates — remove after trimming so "foo " and "foo" match)
6. validate       (validation — check against schema last)

Stats Cache as Quality Dashboard

After running stats --cardinality --stats-jsonl (basic moarstats auto-runs), read the .stats.csv cache to assess quality in one pass:

Cache Column	Quality Signal
nullcount	Completeness — 0 is ideal
sparsity	Completeness — ratio of nulls (0.0–1.0)
cardinality	Uniqueness — compare to row count
type	Validity — check expected types
min / max	Accuracy — plausible range?
mean / stddev	Accuracy — outlier detection (>3σ)
outliers_total_cnt	Accuracy — from moarstats; outlier count per column
mode	Consistency — dominant value expected?

Advanced Stats (via moarstats --advanced)

Run moarstats --advanced to enrich the cache with distribution shape metrics:

Cache Column	Quality Signal
kurtosis	>3 heavy tails (outlier-prone), <3 light tails; >10 = extreme outliers
bimodality_coefficient	>=0.555 suggests bimodal distribution (possible mixed populations)
jarque_bera_pvalue	<0.05 = NOT normally distributed; flag analyses assuming normality
gini_coefficient	Near 1 = extreme concentration; near 0 = uniform
shannon_entropy	Low = concentrated values; high = diverse
winsorized_mean	Compare to mean — large difference signals outlier influence
median_mean_ratio	<0.8 or >1.2 = significantly skewed; mean may be misleading
range_stddev_ratio	Very high = extreme outliers relative to variability
cv	>100% = high relative variability; data is highly spread relative to mean
mad_stddev_ratio	>0.8 = stddev is reliable; <<0.8 = outliers inflating stddev
mode_zscore	Far from 0 = mode is atypical; possible mixed populations
trimean	Robust central tendency: (Q1 + 2*median + Q3)/4; compare to mean for skew detection
midhinge	Midpoint of middle 50%: (Q1+Q3)/2; robust center measure
robust_cv	MAD/abs(median); outlier-resistant coefficient of variation
theil_index	Inequality measure (0=equal); decomposable into within/between group; only for positive values
mean_ad	Average absolute distance from mean; less sensitive to outliers than stddev
simpsons_diversity_index	Probability two random values differ (0-1); more intuitive than entropy