Data quality profiling is designed to automatically evaluate the quality of data as it passes through various stages of OneView360°. Four primary data quality check points are available in OneView360°, and each check point has a specific purpose.

These checkpoints include:

• Data Capture – used to identify missing or late files
• Post-Standardization – used to analyze data based on history and to recognize data values and distributions
• Pre-SSOT Load – used for the validation of data enhancement completeness
• Post Assembly and Pre-Data Distribution – profiling is used to evaluate completeness, trends, pattern and historical distribution of values.

A sample of analyis that can be performed on record, data element, data element parent level, or a specific data element value includes the following and combinations thereof:

• too few or too many records or data element have been received
• unique occurrences requirements have not been met
• the pattern distribution varies from expectations
• inbound data is inconsistent with historical receipts
• data value tolerances are not met
• data values do not match historically receive values
• data values do not match predefined set of values
• changes in distributions occur

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