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P1.T1.407. Data quality categories

Nicole Seaman

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AIM: Describe ways in which a business can be negatively impacted by poor quality data. Identify the most common issues which result in data errors.

Questions:

407.1. According to Loshin (author of Chapter 3, Information Risk and Data Quality Management), "Many data quality issues may occur within different business processes, and a data quality analysis process should incorporate a business impact assessment to identify and prioritize risks. To simplify the analysis, the business impacts associated with data errors can be categorized within a classification scheme intended to support the data quality analysis process and help in distinguishing between data issues that lead to material business impact and those that do not. This classification scheme defines six (6) primary categories for assessing either the negative impacts incurred as a result of a flaw, or the potential opportunities for improvement resulting from improved data quality." (Source: Anthony Tarantino Deborah Cernauskas, Risk Management in Finance: Six Sigma and Other Next‐Generation Techniques - David Loshin, Ch. 3: Information Risk and Data Quality Management (2009 by John Wiley & Sons, Inc.))

Which business impact category is best associated with "increased workloads, decreased throughput, increased processing time, or decreased end-product quality?" (Source: Anthony Tarantino Deborah Cernauskas, Risk Management in Finance: Six Sigma and Other Next‐Generation Techniques - David Loshin, Ch. 3: Information Risk and Data Quality Management (2009 by John Wiley & Sons, Inc.))

a. Financial impacts
b. Confidence-based impacts
c. Productivity impacts
d. Risk impacts


407.2. Which business impact category is best associated with "decreased organizational trust, inconsistent operational and management reporting, and delayed or improper decisions." (Source: Anthony Tarantino Deborah Cernauskas, Risk Management in Finance: Six Sigma and Other Next‐Generation Techniques - David Loshin, Ch. 3: Information Risk and Data Quality Management (2009 by John Wiley & Sons, Inc.))

a. Confidence-based impacts
b. Satisfaction impacts
c. Productivity impacts
d. Risk impacts


407.3. The author cites the following examples to which financial institutions are particularly sensitive with respect to risk and compliance impacts: Anti-money laundering aspects of the Bank Secrecy Act and the USA PATRIOT Act; Sarbanes-Oxley; Basel II Accords; Graham-Leach-Bliley Act; Credit Risk assessment; and System Develop Risks. While the source of these areas of risk differ, the author highlights two high-level similarities. First, as we might expect, each of these mandate the use or presentation of high-quality information which implies financial institutions must manage the quality of their organizational information.

Which is the second similarity?

a. They also require that data errors be classified into the category of financial impacts; e.g., operating costs, decreased revenues, delays in cash flow
b. They also require demonstration of the adequacy of internal controls, which oversee data quality, to external parties (e.g., auditors) such that they must have in place transparent, auditable governance processes
c. They also require that data errors be classified into the category of compliance impacts; e.g., government regulations, industry expectations, or self-imposed policies (such as privacy policies).
d. They also require that companies set aside a reserve liability to fund future contingent outflows that arise due to data quality issues

(Source: Anthony Tarantino Deborah Cernauskas, Risk Management in Finance: Six Sigma and Other Next‐Generation Techniques - David Loshin, Ch. 3: Information Risk and Data Quality Management (2009 by John Wiley & Sons, Inc.))

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