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How do you match data quality to business outcomes?

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Data quality is not an abstract concept, but a key factor in achieving your business goals. Whether you want to improve customer satisfaction, optimize operations, or increase revenue, you need data that is accurate, complete, relevant, and timely. But how do you measure and improve data quality in a way that aligns with your desired outcomes? In this article, we will explore some practical steps and best practices for matching data quality to business outcomes.

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Amit Sharma

QA Manager / Data quality Architect
Thilankka Marasinghe

Data Governance Manager at Suncorp Group | Data Enablement| Data Culture | Data Governance |CDE Guy??? ??

1 Define your business objectives and metrics

The first step is to clearly define your business objectives and the metrics that will indicate your progress and success. For example, if your objective is to reduce customer churn, you might use metrics such as retention rate, lifetime value, or satisfaction score. These metrics should be SMART: specific, measurable, achievable, relevant, and time-bound. They should also be linked to the data sources and attributes that influence them, such as customer profiles, transactions, feedback, or interactions.

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2 Assess your current data quality level

The next step is to assess your current data quality level for the data sources and attributes that relate to your business metrics. You can use a data quality assessment framework, such as the one proposed by DAMA (Data Management Association), which evaluates data quality based on six dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness. You can also use data quality tools, such as data profiling, data cleansing, or data validation, to identify and fix data quality issues, such as errors, duplicates, or missing values.

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3 Establish data quality targets and thresholds

The third step is to establish data quality targets and thresholds that reflect your business expectations and requirements. Data quality targets are the desired levels of data quality for each dimension and attribute, while data quality thresholds are the minimum acceptable levels of data quality that will not compromise your business outcomes. For example, if your target for customer satisfaction score is 90%, you might set a threshold of 80%, below which you will take corrective actions. Data quality targets and thresholds should be realistic, achievable, and aligned with your business objectives and metrics.

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4 Monitor and report data quality performance

The fourth step is to monitor and report data quality performance on a regular basis, using data quality dashboards, scorecards, or reports. These tools should provide a clear and comprehensive view of your data quality level for each dimension, attribute, source, and metric, as well as the trends, gaps, and impacts of data quality issues. They should also enable you to compare your data quality performance against your targets and thresholds, and to identify the root causes and consequences of data quality problems.

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Thilankka Marasinghe

Data Governance Manager at Suncorp Group | Data Enablement| Data Culture | Data Governance |CDE Guy??? ??
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With data quality reports , I found that its important to provide the DQ metric in the context of a business outcome. At a previous company in the investment industry, we initially developed a suite of data quality reports across critical data elements. We found that most users did not find it useful. We then reframed the DQ metrics in the context of impact on funds under management. We saw an immediate increase in report utilization. User feedback also indicated that they could better relate to them when provided context

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5 Implement data quality improvement initiatives

The final step is to implement data quality improvement initiatives that will help you close the gaps between your current and desired data quality levels, and to enhance your business outcomes. Data quality improvement initiatives can include data quality policies, standards, roles, and responsibilities, as well as data quality projects, programs, or activities, such as data governance, data stewardship, data integration, or data enrichment. Data quality improvement initiatives should be prioritized, planned, executed, and evaluated based on their expected benefits, costs, risks, and dependencies.

By following these steps and best practices, you can match data quality to business outcomes, and ensure that your data is fit for purpose, value, and use.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Amit Sharma

QA Manager / Data quality Architect
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We should check the most critical data assets. Place data quality checks on these data assets. Whenever a data quality check fails or if there is a data issue on these assets we should do a detailed RCA and then put in place certain processes or checks to ensure that issue does not occur again. Once these checks are in place then Data Quality monitor can be used to track the data quality improvement of these data assets. The identification of these data assets should be done with the help of business stakeholders so that from day one we are focussed on those data sets which are having a high impact on revenue. IN this way we can achieve high data quality , which will be driven by business outcomes.

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Thilankka Marasinghe

Data Governance Manager at Suncorp Group | Data Enablement| Data Culture | Data Governance |CDE Guy??? ??
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We often discuss the cost of bad data, it is also important to consider the cost of maintaining data quality and on going remediation needs to be factored into any data quality business case. The important point to note is that bad data can never be eliminated. Therefore the business case for DQ should include consideration for FTE requirements to maintain data quality controls and provide ongoing remediation support.

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How do you match data quality to business outcomes?

1

2

3

4

5

6

1 Define your business objectives and metrics

2 Assess your current data quality level

3 Establish data quality targets and thresholds

4 Monitor and report data quality performance

5 Implement data quality improvement initiatives

6 Here’s what else to consider

Data Governance

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How do you match data quality to business outcomes?

1

2

3

4

5

6

1 Define your business objectives and metrics

2 Assess your current data quality level

3 Establish data quality targets and thresholds

4 Monitor and report data quality performance

5 Implement data quality improvement initiatives

6 Here’s what else to consider

Data Governance

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