data-quality-and-reconciliation

Name: data-quality-and-reconciliation
Availability: InStock
Author: scumunna

Detect and explain conversion data discrepancies across DSP, GA4, and Campaign Manager 360.

1|Updated Jun 24, 2026

Search Tags:#data integrity #statistical analysis #data reconciliation #conversion analysis #platform discrepancies

Ready to run

npx skills add https://github.com/scumunna/programmatic-skills --skill data-quality-and-reconciliation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-quality-and-reconciliation
Source: https://github.com/scumunna/programmatic-skills/tree/main/skills/data-quality-and-reconciliation
Command: npx skills add https://github.com/scumunna/programmatic-skills --skill data-quality-and-reconciliation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses discrepancies in conversion and delivery numbers across various platforms, helping to maintain data integrity and facilitate informed decision-making.

Core Features & Use Cases

Data Reconciliation: Compares conversion and delivery numbers across platforms like DSPs, GA4, and Campaign Manager 360.
Discrepancy Banding: Establishes acceptable ranges for differences to identify anomalies or potential issues.
Pre-Ship Data Quality Check: Ensures reports are free of errors before they reach the client.
Anomaly Detection: Uses statistical methods to detect true anomalies that may indicate issues.
Use Case: When a user encounters inconsistencies in conversion data across different platforms, this Skill can be used to identify the cause and determine if it is a legitimate discrepancy or a tracking error.

Quick Start

Use the data-quality-and-reconciliation skill to reconcile conversion numbers between GA4 and DSP.

Frequently Asked Questions about data-quality-and-reconciliation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I reconcile conversion data discrepancies between DSP and GA4?▼

To reconcile conversion data discrepancies between DSP and GA4, compare platform-specific metrics using statistical anomaly detection with seasonal and weekday adjustments to determine if differences are legitimate or tracking errors.

Why do conversion numbers differ across Campaign Manager 360 and my DSP?▼

Conversion numbers differ across Campaign Manager 360 and DSPs due to platform-specific metrics and attribution models. Establishing acceptable discrepancy bands helps identify true anomalies versus normal variation.

What is the best way to detect true anomalies in cross-platform delivery numbers?▼

The best way to detect true anomalies in cross-platform delivery numbers is using statistical analysis that applies seasonal and weekday adjustments to establish acceptable discrepancy bands for accurate comparison.

Can I use statistical anomaly detection for pre-ship data quality checks on conversion reports?▼

Yes, you can use statistical anomaly detection for pre-ship data quality checks to ensure conversion reports are free of errors and identify potential tracking issues before they reach the client.

Does data reconciliation require understanding attribution models to explain platform discrepancies?▼

Yes, data reconciliation requires understanding attribution models because platform-specific metrics and attribution differences directly cause conversion data discrepancies across DSP, GA4, and Campaign Manager 360.