Service guides / AI agents

Data quality checks · AI agents

Project status assistance: data quality checks

Agree what a valid source record looks like. Start with sample task records and reporting rules and plan a status brief linked to underlying tasks.

Available remotely for local and international enquiries. Platform choice and final scope depend on your requirements.

Explore the service →Discuss this project

The project in view

Start with
sample task records and reporting rules.
Proposed result
a status brief linked to underlying tasks.
A decision to resolve
stale records being described as current.

This is a planning example. It does not describe a completed client engagement.

Data quality checks for this project

  • Check sample task records and reporting rules for completeness, conflicting versions and consistent references.
  • Add a separate validation rule for stale records being described as current.
  • Keep unresolved records separate from a status brief linked to underlying tasks; record the reason rather than silently filling a gap.

Questions to answer together

  • Which fields, references or measurements must be present?
  • How are duplicates, contradictions and missing values reported?
  • Which corrections require confirmation from the source owner?

What to have at the end

A validation checklist with examples of valid, rejected and unresolved records.

Record the decision on stale records being described as current alongside the source material. If it is still unresolved, identify who can answer it and what work depends on the answer.

Prepare your enquiry

Use this as a starting point. Add your current process, available examples and constraints before sharing.

Email Ian

Send your completed brief to hello@iansamonte.com or visit the main page to discuss your project.