---
id: "action-maintain-data-quality"
type: "action-item"
source_timestamps: ["§ 1. Create and maintain high-quality data."]
tags: ["data-governance", "operations"]
related: ["concept-broken-data-foundation"]
action: "Establish permanent operating disciplines to maintain data quality continuously."
outcome: "Prevention of context rot and data drift, ensuring AI models remain accurate and trusted over time."
sources: ["tail1"]
sourceVaultSlug: "hbr-seg-tail1"
originDay: 1
articleStem: "hbr-tail-107-lenovo-ai-supply-chain"
sourceUrl: "https://hbr.org/2026/05/how-lenovo-built-an-ai-powered-supply-chain"
sourceTitle: "How Lenovo Built an AI-Powered Supply Chain"
---
# Treat data quality as a permanent discipline

**Action:** Establish permanent operating disciplines to maintain data quality continuously.

**Do this because:** Do not treat data standardization as a one-time project that ends once the AI is deployed. Establishing high-quality data (via [[action-fix-data-infrastructure]]) is only the *first* step; maintaining it must become an ongoing, permanent operational discipline. Without this, the [[concept-broken-data-foundation]] pathology quietly re-emerges.

**Expected outcome:** Prevention of context rot and data drift, ensuring AI models remain accurate and trusted over time — protecting the [[concept-single-instance-data]] foundation that [[concept-ichain-architecture]] depends on.