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Data quality · Quality Foundations

Data quality and the six dimensions

Easyquality-01
data qualitydimensionsaccuracycompletenessfreshness

Question

What is data quality, and what are the six common quality dimensions?

Solution

Data quality means data is fit for its intended use: dashboards, ML features, billing, compliance, or ops decisions. "Fit" is not absolute. A table can be perfect for nightly finance and still fail for real-time fraud.

The six dimensions interviewers expect:

1. Accuracy: values match reality / source of truth (order total is correct). 2. Completeness: required fields and rows are present (no missing customer_id where needed). 3. Consistency: same fact agrees across systems (warehouse revenue matches billing for that day). 4. Timeliness / freshness: data arrives soon enough for the use case (SLA age). 5. Validity: values follow rules and formats (status in allowed set; email looks like an email). 6. Uniqueness: no unwanted duplicates at the declared grain (order_id unique in fct_orders).

Example: fct_orders
  accuracy:     amount matches Stripe charge
  completeness: order_id / amount / ordered_at not null
  consistency:  sum(amount) ≈ Stripe payout for the day
  freshness:    max(ordered_at) within 1 hour of now
  validity:     currency in {USD, EUR, INR}
  uniqueness:   one row per order_id

Interview tip: Define quality as "fit for purpose," list the six dimensions with one concrete check each, and say which dimensions matter most depend on the consumer.

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