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Understanding Data Quality and Why Teams Struggle with It

Data quality: the catch-all term for business logic, reliability, validity, and consistency.

Struggling with data quality? Learn why data teams face challenges and how it impacts business performance. Understanding Data Quality and Why Teams Struggle.

Understanding Data Quality and Why Teams Struggle with It

Data Quality Fundamentals

What is Data Quality?
  • Data quality refers to the extent to which data serves its intended purpose or adheres to internal standards.

  • A data quality issue arises when data no longer fulfills its intended use case or meets internal standards.

  • Data quality incidents occur when events decrease the degree to which data satisfies external use cases or internal standards.

There are ten dimensions of data quality:

Intrinsic dimensions (independent of use cases):

  • Data integrity: Ensuring data accuracy and consistency.

  • Accuracy: Precision and correctness of data.

  • Completeness: Having all necessary data elements.

  • Consistency: Uniformity across data sources.

  • Freshness: Timeliness of data.

  • Privacy/security: Protecting sensitive information.

Extrinsic dimensions (dependent on use cases):

  • Relevance: Data’s suitability for specific purposes.

  • Reliability: Trustworthiness of data.

  • Timeliness: Data’s relevance within a specific timeframe.

  • Usability: Accessibility and ease of use.

  • Validity: Data conforms to defined rules and constraints.

Why Does Data Quality Matter?
  • Business Performance: High-quality data leads to better decision-making, increased revenue, cost reduction, and risk mitigation.

  • Poor Data Impact: Low-quality data results in poor profitability and increased business risk.

  • Personalization: Quality data is crucial for effective personalization efforts, as over 75% of consumers expect personalized interactions.

Real-World Examples:

  • Zillow’s machine learning algorithm issues led to over $300 million in losses.

  • Public Health England underreported 16,000 COVID-19 infections due to table row limitations.

  • The Mars Climate Orbiter was lost in space due to a discrepancy between metric and imperial units.

Challenges in Data Quality:

  • Machine and Human Errors:

  • Data quality problems stem from both machine errors (e.g., software sprawl, data proliferation) and human errors (manual data entry).

  • Data teams often lack essential metadata for efficient and effective work.

In summary, prioritizing data quality is essential for informed decision-making, business success, and exceptional customer experiences. Data teams must master data quality standards to deliver reliable and trustworthy data to stakeholders.



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