Completeness

As explained in Data Without Labels

Completeness is a data-quality attribute indicating that required values, records, variables, and time periods are present.

Completeness is a data-quality attribute indicating that required values, records, variables, and time periods are present. A complete dataset avoids missing important data points and supports more reliable outputs. section 1.2.3

Sales data across a year

A sales dataset covering one year is complete when it contains values for all 12 months; missing months create holes in the data source. section 1.2.3

Figure 1.5 Data quality is of paramount importance; attributes of good-quality data are shown.

Use the data-quality attributes diagram to relate completeness to validity, accuracy, representativeness, and availability. section 1.2.3

How Data Without Labels covers it

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.

How it connects

Connects completeness with data quality, validity, accuracy, representativeness, and availability.

liveBookmanning.com

Read Data Without Labels on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  The Well-Grounded Data AnalystCompleteness is the extent to which the expected records or attributes in a dataset are present and available.
• 

  liveBookmanning.com
  Statistics Slam DunkIncomplete data contains missing, blank, or otherwise unavailable values for one or more recorded fields.
• 

  liveBookmanning.com
  Building LLM Applications with DSPyCompleteness is the degree to which a summary includes the important information from its source document.
• 

  liveBookmanning.com
  Effective Data AnalysisCompleteness is the degree to which all required data is present for the intended analysis or operation.
• 

  liveBookmanning.com
  Think Distributed SystemsThe property of a mental model in which every relevant fact of the system is represented in the model.

Compare how 6 books explain completeness →

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.

This explainer was generated by Manning's knowledge graph from the text of Data Without Labels. The section links point to the parts of the book it draws on.