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Information Risk and Data Quality Management

6 important questions on Information Risk and Data Quality Management

Which business impact of poor data quality exist?

All to support data quality analysis process, to help distinguish between material business impacts and those that are not:
- Financial impact (e.g. missed opportunities, delayed cash flow)
- Confidence-based impacts (e.g. low confidence in forecasting, improper decisions)
- Satisfaction impacts (e.g. customer of employee)
- Productivity impacts (e.g. increased workloads)
- Risk impacts associated with credit assessment (e.g. throughput time)
- Compliance is jeopardized (e.g. government regulations)

Despite focus on financial impact, risk and compliance are often largely compromised by data quality issues.

Increased risk of erred data?

- Data entry errors
- Missing data
- Duplicate records
- Inconsistent data
- Nonstandard format
- Complex data transformations
- Failed identity management process
- Undocumented, incorrect or misleading metadata

How to get to such a scorecard?

After having identified the dimensions of data quality, thereby the business user expectations. We map the information policies and their corresponding business rules to those dimensions.

We distill out information requirements, we also capture assertions about the business user expectations for the result of the operational processes. Important is to interview the business users to determine the acceptability thresholds. Integrating these thresholds with the methods of measurement completes the construction of the data quality control.

What about operational data governance?

The manifestation of the processes and protocols necessary to ensure that an acceptable level of confidence in the data effectively satisfies the organization's business needs. Defining roles, responsiblilities and accountabilities. The data quality scorecard!

Goal: catches issues as early as possible, avoid and or minimize downstream impacts. SLA, service-level agreements specify reasonable expectations for response and remediation. This is more then data validation, as inspection is an ongoing process to:
- Reduce errors
- Enable identification of data flaws
- Institute a mitigation

Enable trust that business impact can be minimized when caught early.

Higher level data?

The need to present higher-level data quality scores introduces a distinction between two types of metrics. Base-level or complex metrics? The latter is a rolled-up score computed as a function of applying specific weights to a collection of existing metrics.

How to manage a scored card view?

Hierarchy of metrics related to various levels of accountability for support of the organizations business objectives.
- Reflect business relevance
- No contextual use, same measurement
- Level of detail needed for specific governance role

The question on the page originate from the summary of the following study material:

Part 1: Foundations of Risk Management

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