How do you communicate and resolve data quality errors and discrepancies in a system implementation?

Data quality and accuracy are crucial for any system implementation project, as they affect the reliability, usability, and performance of the new system. However, data quality issues can arise from various sources, such as data migration, integration, validation, or user input. How do you communicate and resolve data quality errors and discrepancies in a system implementation? Here are some tips to help you manage data quality effectively.

1 Identify data quality requirements

Before you start the system implementation, you need to define the data quality requirements for the new system. These are the standards and criteria that the data must meet to be fit for the intended purpose and users. Data quality requirements may include aspects such as accuracy, completeness, consistency, timeliness, uniqueness, and validity. You can use a data quality framework or a data quality plan to document the data quality requirements and how they will be measured and monitored.

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Colin Thrasher, MBA

Executive Director, Financial Services @ Online Business Systems | Masters in Business Administration
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If integration to other systems is in scope for the project, include system configuration and data standards in the source systems as well.

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2 Establish data quality roles and responsibilities

Data quality management is a collaborative effort that involves different stakeholders, such as project managers, system developers, data owners, data custodians, data users, and data quality analysts. You need to assign clear roles and responsibilities for each stakeholder and communicate them effectively. For example, who is responsible for identifying, reporting, resolving, and verifying data quality issues? Who is accountable for ensuring data quality compliance and governance? Who is authorized to approve data quality changes and corrections?

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3 Implement data quality controls and checks

To prevent and detect data quality issues, you need to implement data quality controls and checks throughout the system implementation lifecycle. Data quality controls are the rules and procedures that ensure the data conforms to the data quality requirements. Data quality checks are the tests and audits that verify the data quality against the requirements. You can use various tools and methods to perform data quality controls and checks, such as data profiling, data cleansing, data validation, data reconciliation, and data quality dashboards.

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Tom Quinn

Security Manager | CISSP
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Implementing an automated method to extract, transform and test the quality of data allows teams to be kept up to date with the current volume of cleanliness issues easily. Being able to communicate clear metrics helps to illustrate the scale of the problem and progress in solving it.

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4 Communicate data quality issues and actions

When you encounter data quality issues, you need to communicate them promptly and transparently to the relevant stakeholders. You can use a data quality issue log or a data quality report to document the details of the issues, such as the source, impact, severity, priority, status, and resolution. You also need to communicate the actions that are taken or planned to resolve the issues, such as data correction, data enhancement, data deletion, or data reprocessing. You should also provide feedback and updates on the progress and outcomes of the actions.

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5 Learn from data quality feedback and improvement

Data quality management is not a one-time activity, but a continuous process of learning and improvement. You need to collect and analyze data quality feedback from the system users and stakeholders, such as data quality surveys, data quality metrics, data quality incidents, and data quality reviews. You should also identify the root causes of data quality issues and implement corrective and preventive actions to avoid recurrence. You should also document and share the data quality lessons learned and best practices for future system implementation projects.

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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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How do you communicate and resolve data quality errors and discrepancies in a system implementation?

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1 Identify data quality requirements

2 Establish data quality roles and responsibilities

3 Implement data quality controls and checks

4 Communicate data quality issues and actions

5 Learn from data quality feedback and improvement

6 Here’s what else to consider

System Implementations

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