Completeness
Completeness is the extent to which all elements required or relevant to a defined scope are present, represented, or accounted for.
The Well-Grounded Data Analyst
Completeness is the extent to which the expected records or attributes in a dataset are present and available.
- How it is used
- Assesses whether customer models and time series are suitable for specific analytical tasks.
- How deep it goes
- Works through missing attributes, unmatched records, geographic coverage, date ranges, and temporal gaps without offering a universal quality score.
- Examples
- Uses unmatched e-commerce customer IDs and traffic measurements across regions, locations, dates, and years.
- What you can do after
- Evaluate/understand whether available records and time periods support a proposed analysis, and communicate resulting limitations.
Read how The Well-Grounded Data Analyst explains completeness →
Statistics Slam Dunk
Incomplete data contains missing, blank, or otherwise unavailable values for one or more recorded fields.
- How it is used
- Guides data preparation by identifying records and seasons that could interrupt or weaken subsequent analysis.
- How deep it goes
- Demonstrates inspection and row filtering in R, but stops short of explaining imputation methods or completeness metrics.
- Examples
- Uses mostly blank draft records and low-count NBA seasons involving the Celtics and Lakers.
- What you can do after
- Apply it via R row deletion and vector filtering to create a more complete working dataset.
Building LLM Applications with DSPy
Completeness is the degree to which a summary includes the important information from its source document.
- How it is used
- Evaluates whether a generated summary covers the source document’s important ideas, complementing checks for factual grounding.
- How deep it goes
- Defines completeness as recall and demonstrates a DSPy judge that enumerates ideas, discusses overlap, and returns a score; full validation is deferred.
- Examples
- Uses a dialogue summary that scores 0.8 while omitting leadership-role and sports details.
- What you can do after
- Evaluate summary coverage with a DSPy LLM judge and combine its score with groundedness using an average or F1.
Read how Building LLM Applications with DSPy explains completeness →
Effective Data Analysis
Completeness is the degree to which all required data is present for the intended analysis or operation.
- How it is used
- Frames completeness as a data-quality check supporting reliable analytics, operations, and organizational decisions.
- How deep it goes
- Defines required-data checks and remediation options, but does not specify thresholds for acceptable missingness.
- Examples
- Uses page_views identifiers and timestamps, null-value tests, inferred means, and incomplete dataset rows.
- What you can do after
- Diagnose missing data and choose removal, inference, derivation, or pipeline validation before analysis.
Data Without Labels
Completeness is a data-quality attribute indicating that required values, records, variables, and time periods are present.
- How it is used
- Assesses whether a dataset contains the values, variables, records, and periods needed for reliable machine-learning outputs.
- How deep it goes
- Defines completeness through missing values and data points, without detailing implementation checks or remediation procedures.
- Examples
- Uses annual sales data containing values for all 12 months as the completeness example.
- What you can do after
- Evaluate datasets by identifying missing values, variables, records, or time periods before relying on their outputs.
Think Distributed Systems
The property of a mental model in which every relevant fact of the system is represented in the model.
- How it is used
- Uses completeness to judge whether a mental model contains all facts relevant to a reasoning task.
- How deep it goes
- Defines relevance precisely, illustrates omission, and connects completeness to transactional Undo recovery.
- Examples
- Uses an electric circuit, light-bulb brightness, and a transactional money-transfer endpoint.
- What you can do after
- Evaluate whether a model omits facts needed for a specific prediction or system guarantee.
Related concepts
- data qualitypart of · Data quality is the degree to which data is accurate, complete, consistent, relevant, and suitable for its intended use.
- database transactionproduced by · A database transaction is a logical unit of database operations whose changes are committed together or rolled back as a whole.
- DSPy programmeasured by · A DSPy program is an executable composition of one or more module instances that can be called with inputs declared by a signature.
- signatureused by · A signature is a declarative specification of the expected input and output fields of a language-model task or operation.
- metrickind of · A metric is a quantitative measure used to characterize or assess a property, condition, behavior, performance, or outcome.
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