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The degree to which all required data elements and records are present, with minimal missing values.
DESCRIPTION
Completeness measures whether datasets contain all necessary records and fields for intended use. It focuses on missing values, sparse attributes, and unpopulated mandatory fields. Organizations set thresholds, rules, and checks to ensure comprehensive coverage. Completeness is assessed during data profiling, ingestion validation, and quality monitoring. It complements accuracy, validity, and timeliness to ensure fitness for purpose.
ALTERNATIVE DEFINITIONS
Wikipedia
Completeness is the degree to which all required data is present.
Concise, canonical phrasing for the dimension.
IBM
Completeness refers to whether your data contains all the required information—such as missing values or records that are absent.
Highlights missing values and absent records as indicators.
Talend
Completeness indicates whether all necessary data points are available and populated so analysis or operations can be performed.
Connects completeness to readiness for analysis and operations.
RELATED TERMS
Validity Consistency Timeliness Uniqueness Data Profiling Data Quality Rules Coverage DAMA-DMBOK
COMPETING TERMS
Missingness Accuracy
RELATIONS
- Narrower: Data Completeness
KNOWLEDGE AREAS
Data Quality Data Management Metadata Management
TARGET AUDIENCE
Data Steward Data Architect Data Analyst Data Management Consultant Chief Data Officer
EXTERNAL SOURCES
- Wikipedia - Data quality
- ISO - ISO/IEC 25012 data quality model
- Great Expectations - Expectations for missing values
TOP QUESTIONS
Why is Completeness important in data quality?
Incomplete data undermines analyses and operations. Ensuring completeness prevents gaps in reporting, ML features, and downstream processes.
Category: Relevancy
How do you measure Completeness?
Define required fields and records, compute missingness rates, and set thresholds. Use profiling tools and quality rules to monitor coverage.
Category: Requirements
What are common mistakes when addressing Completeness?
Focusing only on nulls, ignoring conditional requirements, and failing to consider business context can produce false confidence in completeness.
Category: Mistakes
What risks stem from poor Completeness?
Decision errors, model bias, and operational failures. Missing data can cascade across pipelines, compounding issues.
Category: Risks
What are alternatives or related dimensions to Completeness?
Accuracy, Validity, and Consistency complement completeness. Missingness explicitly measures absence and sparsity.
Category: Alternatives
How to manage Completeness across pipelines?
Implement validation at ingestion, enforce schemas, monitor null rates, and add backfill processes for known gaps.
Category: Management
Where to start improving Completeness?
Profile critical datasets, identify mandatory fields, and define rules with Data Stewards. Prioritize high-impact missingness fixes.
Category: Topic
What’s a big misunderstanding about Completeness?
Many equate completeness with 100% populated fields. Business context often determines which gaps matter and which don’t.
Category: Misunderstanding
What’s a hot topic related to Completeness?
Data contracts and schema evolution enforce expectations, improving completeness for event-driven and streaming pipelines.
Category: Topic