Automating data quality follow-up: who will fix the record?

In a proposed Syntalith automation project, we connect the existing error report with the tool used by the people who need to resolve it. The manager can see who is handling the issue, whose answer the correction depends on, and whether the next check still finds the problem. This fits teams that already know how to detect bad data but manually chase corrections across departments.

The administrator can edit the record; sales knows the answer

In this example, the administrator can update the customer record but does not know which account manager should be assigned. A designated person in sales makes that decision. The issue should reach them with the customer list and a reference to the check that found the gap. Once the answer is agreed, someone authorized to make changes can correct the data in the current system.

With those responsibilities visible, the manager does not have to ask both departments where the work has stopped. They can see whether sales has supplied the assignment, whether the administrator has made the change, and whether the data has been checked again. Your company defines responsibility for each type of error. The automation uses those decisions to route issues; it does not choose a customer's account manager.

A useful issue also explains why the correction is needed. Here, customer service needs to know who should handle a customer's request. Sales receives a concrete question connected to its work, rather than another spreadsheet describing an empty field in technical terms.

The next check need not create another task

If the next report flags the same customers, staff should see that result against the issue already being handled. A new task with the same list would split the replies and make it harder to establish which correction is underway. Linking repeated findings keeps earlier decisions together and shows that the problem persists.

A repeat should concern the same error for the same customers; similar descriptions can refer to different issues. Staff can receive one shared task for related items while still being able to open each record that needs correction.

If no department accepts responsibility, the manager needs to see that the assignment is unresolved. Sending the issue to a general inbox does not settle who will see the work through.

Show task completion alongside the data check

Marking a task complete means someone has reported finishing the work. A fresh data check establishes whether the identified gap has gone. After a correction, the workflow can trigger an agreed check or retrieve the next check's result for the affected records.

If the result still flags the gap, it returns to the same issue. The person handling the correction can investigate what remains undone. If the check has not run yet, the manager sees that the result is pending. This makes the difference between completed task work and a verified data correction visible without manually comparing two reports.

Check the action management in your current tool

Microsoft Purview supports assigning generated data quality actions to users and updating their status. Assigning and modifying actions requires the data quality steward role. This is an existing way to manage work on an issue, worth checking before commissioning a separate workflow.

If your team already uses such a tool, agreeing on responsible people and working practices may be enough. A separate integration makes sense when the report is produced in one place, departments manage tasks in another, and someone manually carries correction results between them. Compare that work with the capabilities of your current issue management system.

Known error types can be routed using agreed rules. AI may help organize free-text issue descriptions when they are difficult to interpret. With consistent check identifiers and known responsible departments, ordinary integration can handle this part of the work.

Start with an error that keeps coming back

An initial Syntalith scope can cover one recurring group of issues. Your team identifies the source checks, the people who determine the correct data, and where changes are made. We agree how to connect issues with their work and obtain the result of the next check. That lets you assess the full workflow using an issue that currently passes between departments.

Talk to Syntalith about automating data quality follow-up. Start by describing a recurring error, who receives the report, and where information about subsequent work gets lost. See the Syntalith pricing page.