Clear DefinitionsData Quality Metrics
1 min read
Pronunciation
[day-tuh kwah-li-tee meh-triks]
Analogy
Like a restaurant health inspection score that tells you how safe and reliable the kitchen’s practices are, data quality metrics rate the health of your data.
Definition
Quantitative measures used to assess the accuracy, completeness, consistency, and timeliness of blockchain and off‑chain data. They ensure analytics and decision‑making rely on trustworthy information.
Key Points Intro
Data quality metrics evaluate the reliability and fitness of data for blockchain analytics and operations.
Key Points
Accuracy: Degree to which data reflects real-world values (e.g., correct transaction amounts).
Completeness: Percentage of required fields or records present (e.g., missing blocks or events).
Consistency: Uniformity across datasets (e.g., same schema across shards or nodes).
Timeliness: Latency from data generation to availability (e.g., block indexing lag).
Example
Technical Deep Dive
Implement data validation pipelines using Spark or Flink to compute SLA metrics on streaming on‑chain event data. Use checksums and schema validators (Avro, Protobuf) to detect missing or malformed records. Store metric time series in Prometheus for alerting.
Security Warning
Relying on stale or inconsistent data can lead to incorrect financial decisions or risk assessments; set alert thresholds.
Caveat
High-quality data pipelines add complexity and cost; balance thoroughness with performance.
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