October 2022
Beginner to intermediate
174 pages
4h 48m
English
Content preview from Data Quality Engineering in Financial Services
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Chapter 5. Data Quality Metrics and Visualization
This chapter demonstrates how to visualize the data quality metrics that were generated in Chapter 4, after applying the DQS framework to data volumes.
Data Quality Metrics
As discussed, data quality metrics are the results generated from data quality measurements, defined in DQS, that are applied to your data. In Chapter 4, we applied the completeness DQS to the raw security master data volume. The statistics from the completeness data validation are 25 datum records comprised of 11 data elements for a total of 275 datum values. This yields 248 valid, 22 invalid, and 5 suspect data quality metrics for the completeness dimension. The metrics for all DQS applied to all data elements in the raw security master data volume are summarized in Table 5-1.
| Dimension | Data element | Valid | Invalid | Suspect |
|---|---|---|---|---|
| Completeness | Ticker | 22 | 3 | 0 |
| Issue Name | 22 | 3 | 0 | |
| Exchange | 20 | 5 | 0 | |
| Bid | 23 | 2 | 0 | |
| Ask | 21 | 4 | 0 | |
| Spread | 25 | 0 | 0 | |
| Market Cap | 25 | 0 | 0 | |
| Market Cap Scale | 23 | 2 | 0 | |
| Price to Earnings (PE) | 22 | 3 | 0 | |
| Consensus Recommendation | 21 | 0 | 4 | |
| Consensus Date | 24 | 0 | 1 | |
| Timeliness | Consensus Date | 15 | 6 | 4 |
| Accuracy | Ticker | 16 | 9 | 0 |
| Issue Name | 11 | 14 | 0 | |
| Exchange | 20 | 5 | 0 | |
| Precision | Bid | 17 | 2 | 6 |
| Ask | 17 | 4 | 4 | |
| Spread | 17 | 3 | 5 | |
| PE | 20 | 3 | 2 | |
| Conformity | Issue Name | 19 | 6 | 0 |
| Market Cap Scale | 22 | 3 | 0 | |
| Consensus Recommendation | 19 | 2 | 4 | |
| Metrics totals | 441 | 79 | 30 |
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