From Manufacturing to Customer Experience –A Global Manufacturer Transformed Data Governance

Using AI-powered data governance to detect anomalies, align master data, and drive operational efficiency across systems.

Background:

A global manufacturer and supplier launched an ambitious Enterprise Retail Automation project aimed at modernizing its IT ecosystem, from production to customer experience. However, persistent data issues disrupted operations, causing production delays and fulfillment errors despite cloud migration and system upgrades.

At the heart of the problem were deep-rooted data quality anomalies across multiple master data domains:📦 Item | 🏭 Supplier | 🧾 Customer | 💰 Finance | 👥 HR

The company needed to unify and clean data across fragmented systems and shift to a proactive, exception-based data governance model.

The Challenge:

The client faced several critical issues:

• ❗ Recurring Data Anomalies: Despite system modernization, legacy data problems persisted.

• 🧩 Inconsistent Master Data: Key domains like item and supplier data showed gaps and mismatches.

• 🔄 System Mismatches: Cross-system discrepancies (e.g., EBS vs CPQ, EBS vs Snowflake) created process breakdowns.

• ⛔ Operational Disruptions: Poor-quality data led to production stoppages and fulfillment delays.

• 🧠 Manual Governance Processes: The Data Governance Office (DGO) lacked visibility and automation.

Solution: AI-Driven Data Governance With Routine

The manufacturer deployed Routine as the central Data Quality (DQ) engine, enabling intelligent, automated data governance across all systems:

🔎 Continuous Data Monitoring

Routine autonomously scanned enterprise systems to flag data anomalies in real time, enabling preemptive action.

🔁 Automated Cross-System Audits

Data from ERP, CPQ, and cloud systems like Snowflake were continuously compared. Discrepancies were automatically detected and flagged for review.

🎯 Exception-Based Management

Rather than combing through entire datasets, the DGO team focused only on exceptions, enabling high-efficiency governance.

Results:

✔️ Improved Data Quality

Proactive alerts and automation enabled the swift resolution of data issues, reducing production losses and fulfillment failures.

🚀 Streamlined Data Transformation

Exception-based audits cut manual effort, accelerated transformation workflows, and reduced downtime.

📊 Enhanced Data Governance Efficiency

Routine empowered the DGO to focus on root-cause issues, dramatically improving governance oversight and speed.

Conclusion:

As enterprise systems evolve, legacy data problems often persist beneath the surface. This global manufacturer proved that data governance must evolve, too. With Routine, they gained the automation, visibility, and control needed to align data from manufacturing to customer experience, ensuring that system modernization truly delivers business value.

📌 Is your data slowing down transformation?

📅 Schedule a Demo with Discover Alpha and modernize your governance strategy today.