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Situation

The client is a global manufacturer and supplier that embarked on an Enterprise Retail Automation project to transform their IT infrastructure from manufacturing to customer experience. However, data challenges arose during the implementation, leading to production losses and fulfillment issues. Despite migrating data to modern applications and the cloud, data quality issues persisted, hindering operational efficiency.

The client’s primary objective was to address data anomalies in key master data domains such as Item, Supplier, Customer, Finance, and HR. They sought to transform data from multiple systems, each plagued by significant data quality issues, and streamline their data governance operations through automation.

Solution

To tackle the data challenges and enhance data governance, the following solutions were implemented:

  • Data Governance with Routine: Routine was deployed as a central Data Quality (DQ) engine to continuously identify data issues across all data sources. By leveraging Routine’s advanced algorithms, the client gained real-time insights into data quality anomalies and received alerts when problems were detected. This enabled proactive data governance and allowed for timely remediation.
  • Automation of Cross-System Audits: Routine’s capabilities were utilized to automate cross-system audits, comparing data between systems and identifying discrepancies. For example, audits were performed between the Enterprise Business System (EBS) and Configure, Price, Quote (CPQ) system, as well as between EBS and Snowflake, a cloud-based data warehouse. Exception-based management facilitated by Routine allowed the client to focus on resolving specific data discrepancies, streamlining the data transformation process.

Results

The implementation of data governance solutions and leveraging Routine yielded significant benefits for the client:

  • Improved Data Quality: Routine’s continuous monitoring and identification of data anomalies enabled the client to proactively address data quality issues. This resulted in improved accuracy and reliability of master data, minimizing production losses and fulfillment issues.
  • Streamlined Data Transformation: By automating cross-system audits and exception-based management, the client achieved more efficient and streamlined data transformation processes. Data discrepancies were quickly identified, allowing for targeted remediation efforts and reducing the risk of operational disruptions.
  • Enhanced Data Governance Efficiency: Routine’s centralized DQ engine and automation capabilities empowered the client’s Data Governance Office (DGO) team to work more efficiently. The team could focus on resolving critical data issues and implementing proactive measures, reducing manual effort and improving overall governance effectiveness.

By leveraging Routine as a central data governance engine, automating cross-system audits, and enhancing data governance efficiency, the global manufacturer and supplier successfully addressed data quality challenges, improving their IT transformation project’s outcomes and operational efficiency.

Read more case studies

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