--- title: "How to Determine Data Quality: A Step-by-Step Guide for Data Engineers" url: https://daily.dev/posts/how-to-determine-data-quality-a-step-by-step-guide-for-data-engineers-ub47uglxu source_url: https://www.decube.io/post/how-to-determine-data-quality-a-step-by-step-guide-for-data-engineers type: article source: "Decube" published: 2026-08-10T06:05:12.728Z updated: 2026-08-10T06:05:37.529Z tags: ["big-data", "data-engineering", "data-quality", "data-observability"] reading_time: 11 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # How to Determine Data Quality: A Step-by-Step Guide for Data Engineers **[Decube](https://daily.dev/sources/decuberss)** · 11 min read · 0 upvotes · 0 comments ## Summary A structured guide covering how data engineers can assess and maintain data quality across four core dimensions: accuracy, completeness, consistency, and timeliness. It outlines seven key metrics to track (accuracy rate, completeness ratio, consistency score, timeliness metric, uniqueness count, validity rate, and error rate), five measurement techniques (data profiling, automated quality checks, audits, statistical analysis, and user feedback), and solutions to six common data quality issues including duplicates, null values, and inconsistent formats. The guide references a statistic that 85% of data lake failures stem from poor data quality and highlights use cases in financial services and telecommunications. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What percentage of data lake failures are caused by poor data quality? 85% of data lake failures stem from poor data quality. This statistic underscores why data quality is treated as a strategic priority rather than a purely technical concern, particularly in sectors like financial services and telecommunications where data integrity directly affects credit scoring, regulatory compliance, customer segmentation, and service delivery. _Teams managing data lakes track findings like this on daily.dev to stay ahead of data quality risks._ ### What are the key metrics data engineers should use to measure data quality? Seven core metrics cover data quality assessment: accuracy rate (target above 95% in financial services), completeness ratio (target above 98% for critical fields in telecom), consistency score, timeliness metric, uniqueness count (to detect duplicates), validity rate (conformance to defined formats), and error rate (incorrect entries vs. total entries). Monitoring all seven allows proactive identification and resolution of quality issues. _Data engineers benchmarking these metrics find relevant practices and tooling discussions on daily.dev._ ## Similar posts on daily.dev - [How to Test Data Quality: A Step-by-Step Guide for Data Engineers](https://daily.dev/posts/how-to-test-data-quality-a-step-by-step-guide-for-data-engineers-z8tbr2a7i) · Decube · 0 upvotes · 0 comments - [Define Data Quality: Essential Steps for Data Engineers](https://daily.dev/posts/define-data-quality-essential-steps-for-data-engineers-ptswisty9) · Decube · 0 upvotes · 0 comments - [Understanding the Meaning of Data Quality for Data Engineers](https://daily.dev/posts/understanding-the-meaning-of-data-quality-for-data-engineers-wtslfrtxn) · Decube · 0 upvotes · 0 comments - [10 Essential Data Quality Metrics Every Data Engineer Must Track](https://daily.dev/posts/10-essential-data-quality-metrics-every-data-engineer-must-track-i9kukbqkt) · Decube · 1 upvotes · 0 comments - [Master Data Quality Accuracy: Essential Practices for Data Engineers](https://daily.dev/posts/master-data-quality-accuracy-essential-practices-for-data-engineers-tbzqrfvrm) · Decube · 0 upvotes · 0 comments --- Tags: [#big-data](https://daily.dev/tags/big-data), [#data-engineering](https://daily.dev/tags/data-engineering), [#data-quality](https://daily.dev/tags/data-quality), [#data-observability](https://daily.dev/tags/data-observability) [View this post on daily.dev](https://daily.dev/posts/how-to-determine-data-quality-a-step-by-step-guide-for-data-engineers-ub47uglxu)