What Is Data Quality?
Data quality determines how useful your data is for making decisions. Poor quality data produces wrong answers — regardless of how sophisticated your analytics are.
Key Takeaways
- Data quality refers to the accuracy, completeness, consistency, and timeliness of your data.
- Poor data quality leads to wrong insights and bad decisions — the 'garbage in, garbage out' principle.
- Improving data quality is usually a higher priority than adding new analytics capabilities.
What data quality means
Data quality has several dimensions. Accuracy: does the data correctly reflect reality? Completeness: are all required fields populated? Consistency: does the same data mean the same thing across systems? Timeliness: is the data current enough to be actionable? Uniqueness: are there duplicate records? A data set can fail on one or multiple dimensions — and each failure mode creates different problems downstream.
The garbage in, garbage out principle
No analytical tool, no matter how sophisticated, can produce good insights from bad data. If your Shopify orders are not syncing correctly to your accounting software, your revenue numbers won't match. If customer records are duplicated, your churn calculations are wrong. If product costs are not accurately recorded, your margin calculations are meaningless. The quality of your output is strictly bounded by the quality of your input data.
Common data quality problems for SMEs
Manual data entry errors (customer names, addresses, product codes entered inconsistently). Inconsistent product naming across systems (same product called 'T-Shirt Blue L' in one system and 'Blue T-Shirt / Large' in another). Missing data (cost prices not entered for some products, leaving margin calculations incomplete). Stale data (customer records not updated when details change). Siloed data (key information locked in systems that don't talk to each other).
How to improve it
Audit your key data sources for the most common errors. Fix structural problems at source — a dropdown menu prevents inconsistent product name entry that free text allows. Implement mandatory fields for critical data points. Establish a single source of truth for key entities (customers, products, suppliers). AskBiz's data quality module flags common inconsistencies in connected data and guides remediation.