Data Quality Dashboards - aka The Snitch Report

In this episode of the Clearly Podcast, the hosts discuss data quality dashboards. These dashboards monitor data entry to flag errors and ensure data accuracy, especially in systems with significant human input. Key points include:

• Definition and Use: Data quality dashboards highlight data entry errors, allowing for prompt correction. Errors disappear from the report once fixed, providing satisfaction to users.
• Routine Recommendation: These dashboards are essential, particularly where user input is high.
• Risks of Poor Data Quality: Inaccurate data can lead to flawed reports and decisions. Regular monitoring helps maintain data integrity.
• Application Redesign: Dashboards can reveal systematic issues, sometimes leading to application redesigns. Simplifying the interface or automating processes can prevent errors.
• Building the Dashboard: Start by identifying key issues through stakeholder discussions. Focus on major errors first, like dates and large transactions. Decide whether to automate error resolution or require manual approval.
• Senior Management Buy-In: Effective communication ensures the dashboard is seen as a tool for improvement, not punishment. This promotes user engagement and data quality.
• Continuous Improvement: If errors persist, it may indicate a deeper application issue requiring broader solutions.

The hosts emphasize the importance of data quality dashboards in maintaining accurate data, which is crucial for reliable reporting and decision-making.

Transcript

Concise Podcast Transcript

Andy:
Welcome to the Clearly Podcast. My name is Andy Clark.

Tom:
I'm Tom Gough.

Greg:
And I'm Greg Bounds.

Andy:
Today, we’re talking about data quality dashboards, also known as Snitch Reports. Greg, can you explain what a data quality dashboard is and how it's used?

Greg:
Sure. A data quality dashboard monitors data entry quality, especially when human input is involved. It flags errors like incorrect categories or missing fields to ensure data accuracy. Once errors are fixed, they disappear from the report, providing satisfaction to those correcting the mistakes.

Andy:
Is this something you routinely recommend to clients?

Greg:
Yes, especially when there is significant user input. Automating error detection helps everyone by addressing data quality issues promptly.

Tom:
Naming and shaming isn’t the goal. It’s about accountability and empowering users to correct their data. Proper data quality ensures accurate reports, which is crucial for decision-making.

Andy:
What are the risks of not using such dashboards?

Tom:
Without data quality dashboards, errors can proliferate, leading to inaccurate reports. This can be especially problematic with dates or large quantities.

Andy:
How often should these reports be checked?

Greg:
It depends on the importance of the data. Weekly checks are common, but some clients may look at them daily or monthly. Regular monitoring helps maintain data integrity.

Andy:
Can data quality dashboards lead to application redesigns?

Tom:
Yes, they can highlight systematic issues that require redesigns. Over-validation can frustrate users, leading to workarounds and further errors. Simplifying the user interface or automating certain processes can prevent errors.

Andy:
How do you start building a data quality dashboard?

Greg:
Begin by talking to stakeholders to identify common issues. Focus on big wins first, like dates and large transactions. Create flags for suspicious data, and decide whether issues will be resolved automatically or require manual approval.

Tom:
Senior management buy-in is crucial. Communicate that the dashboard is a tool for improvement, not punishment. It should help users do their jobs more efficiently by reducing errors and rework.

Andy:
Have you encountered situations where fixing one issue reveals another?

Greg:
Yes, this can happen. If issues keep arising, it may indicate a deeper problem with the application. In such cases, you might need to escalate the issue for a more comprehensive solution.

Tom:
Constant errors might suggest the application itself is flawed. If replacing it isn’t feasible, focus on improving training and processes to mitigate these issues.

Andy:
Any final thoughts on data quality dashboards?

Greg:
They’re invaluable for maintaining data integrity. Engage stakeholders, focus on key issues, and use the dashboard to guide continuous improvement.

Tom:
Communication and buy-in are key. Ensure everyone understands the dashboard’s purpose and how it benefits them.

Andy:
Make data quality a routine part of every project. Thanks for listening. We’ll be back next week with another data-related topic. Have a great week!

Tom:
Cheers. Time to go and snitch on someone.