Middesk | Data quality and strategy for modern business compliance

Improved auto-decision rates

Lower risk (fewer bad apples)

Better customer experience

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What makes high-quality KYB data?

Shopping for business identity data? Make sure these questions are on your list:

Is your data fresh?

A ripe and juicy red apple, on a white background

A half eaten, rotting apple core, on a white background

Freshness refers to the data being available and ready when needed. This can specifically include the recency of data and whether it reflects the most recent information available. Metrics like data freshness and monitoring lag help ensure that data is up-to-date and accessible promptly.

Is your data complete?

All six eggs fill their basket, on a white background

2 eggs left out of a 6 egg basket, on a white background

Completeness refers to the expectation that data is sufficiently filled to deliver meaningful inferences and decisions. Metrics like fill rate and qualification rate are used to assess completeness.

Is your data consistent?

A neat, vertical stack of oreos, on a white background.

A crumbly pile of oreos, on a white background

Consistency means that data values for the same entity or attribute should be the same across all data sources, datasets, and data points, without any discrepancies or contradictions. Metrics such as anomaly detection and manual inconsistencies highlight areas where consistency can be improved.

Is your data unique?

A fancy cupcake topped with icing and sprinkles, on a white background

A basic cupcake without icing or sprinkles, on a white background

Uniqueness refers to the extent to which each data record or entity is distinct and does not contain any duplicate or redundant data. Metrics like duplication count help identify redundancies, ensuring that data accurately represents a single instance or occurrence.