---
title: "Guide: Data Quality"
description: "Prepare data pragmatically before integration"
canonical_url: "https://meingpt.com/en/docs/integrations/guide-data-quality"
language: en
---

# Guide: Data Quality

## Goal

Deliver fast without building on low-quality data.

## SharePoint / file shares at large scale

When connecting large SharePoint estates:

- **do not ingest everything at once**: select relevant sites/scopes first
- **reduce duplicates/outdated content**: less noise, better retrieval
- **use clear metadata/naming conventions**: better findability

Pragmatic sequence:

1. define top use cases
2. map only relevant data scopes
3. expand step by step after validation

## SAP / ERP with many tables

For very large table landscapes (e.g. SAP):

- do not start with full coverage
- curate tables by use case
- assign business owners per data domain

Recommendation:

- start with a small core set
- validate answer quality
- expand table scope in controlled increments

## Minimum standards for structured data

- stable keys/IDs available
- consistent date fields
- null/empty handling is understood
- field semantics are documented
- clear update cadence (e.g. hourly/daily)

## Minimum standards for document data

- clear titles/file names
- current versions over shadow copies
- avoid legacy archives in first scope
- consistent folder/metadata structure

## Go/No-Go checklist before pilot

- Is first scope clearly bounded?
- Are data owners assigned?
- Are 1-2 high-value use cases explicitly defined?
- Is it clear which data is intentionally excluded from phase 1?

If these points are clear, pilot speed and stability improve significantly.