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 |
Automated mitigation1 |
|---|---|---|
| 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 Engine 2. Implementation ready in Q1 2022 3. 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.