Learn how to use Dataset Completeness Measures and Table Completeness to identify gaps and track completion progress
Ensuring the completeness of datasets is crucial to creating accurate insights. This guide explores two powerful features—Dataset Completeness Measures and Table Completeness—designed to empower users in assessing and enhancing the comprehensiveness of their data by identifying gaps.
How they work
Dataset Completeness Measures
These are highly configurable and can be applied to the entirety of a dataset as the simplest way to get started, or using advanced options to look at specific aspects of a dataset. The value generated is determined by figuring out the proportion of data points that have been set for the specified criteria.
With advanced options, you are able to configure specific Measured quantities to include, limit the measure to specific variable categories and limit the measure to a specific time range.
As a dataset moderator, you can see the Dataset Completeness Measures but if you wish to make these publicly visible within a dashboard or in an external website, you can copy an embedded HTML code for that use case.
Learn more about working with Dataset Completeness Measures here.
Table Completeness
This feature is available inherently on aggregate self service dataset tables with no configuration required. It is visible to dataset template contributors to the side of the table title and indicates what proportion of the specific table has been completed at a glance.
