Data Quality Engineering in Financial Services by Brian Buzzelli

Author:Brian Buzzelli
Language: eng
Format: epub, mobi
Publisher: O'Reilly Media, Inc.
Published: 2022-10-26T00:00:00+00:00

Level 0 is the lowest fit-for-purpose level and represents the initial raw vendor data ingestion commonly managed by a data management, data stewardship, or enterprise data management function. At this level, the data is raw and uncleansed, and the function applies various DQS to the data to validate its quality relative to the DQS of the downstream consumers.

Levels 1 and 2 represent the fit-for-purpose levels based on DQS and acceptable data quality levels for the Research business function and the use of data volumes for generating internal analytics. The required data quality level is medium, which means the business function is operationally functional with lower quality data. However, the business impact is medium—the time of high-value resources is wasted and analytics may be inaccurate or of poor quality, but there is likely no financial, regulatory, or client impact. Medium-level business impact typically requires triage to identify the data anomalies and determine the remediation actions necessary to improve the data quality.

Levels 3, 4, and 5 represent the fit-for-purpose levels based on DQS and acceptable data quality levels for the Portfolio Management, Trading, Compliance, Investment Operations, Business Development, Client Services, Performance Measurement, and Marketing business functions. The required data quality level is high, which means the business function is in an operational failure state with lower quality data. The business impact of incomplete or inaccurate data can include financial penalties, loss of client trust, regulatory violations, and reputational damage. High-level business impact requires an immediate response to identify the data anomalies and determine the remediation actions necessary to improve the data quality.

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