Data Quality Assessment Reference

This table lists the 14 data quality assessment checks automatically performed by the Inspirient Automated Analytics Engine as well as the current status of automated mitigations.

Cate­gory Data quality
assessment check
Com­plete­ness Check for missing values in columns
Check missing dimensions for expected analytical use cases2
Cor­rect­ness Check for anomalous categorical values
Check for anomalous date/time values
Check for anomalous numeric values
Integ­rity Check for erroneous data, i.e., values that do not conform to the inferred data type
Check for existence of primary key column
Check table meets structural best practices3
Rele­vance Assess the relevance of each dimension2
Time­li­ness Assess if data are up to date3
Check time-span of data meets expectations3
Unique­ness Check for duplicate records
Usabi­lity Assess readability of categorical values3
Assess readability of column headers
1. Checked items are automatically handled with an appropriate mitigation by the Inspirient Automated Analytics Engine2. Implementation ready in Q1 20223. Implementation planned for Q2 2022

Missing a data quality check?

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The Inspirient Automated Analytics Engine automates the entire data analytics process end-to-end: From the assignment of input data, pattern and outlier detection, automated visualization of patterns, weak points and opportunities to automatic generation of textual explanations and recognition of the underlying relationships and rules. Most other analytics solutions rarely include these textual explanations and observations regarding the underlying data relations, which are both critical to provide a deeper level of analysis and more actionable conclusions.

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