Background

Following the integration of two organizations with distinct data platforms and business models, the client faced challenges in ensuring consistent master data management and high data quality across critical domains such as HR, customers, products, and suppliers. Data was fragmented across SharePoint and legacy systems, limiting analytics capabilities and introducing inconsistencies and reporting delays.

The organization required a scalable cloud-based platform to integrate, cleanse, govern, and manage master data while enabling reliable analytics and stewardship processes. 

Objectives

• Establish a scalable enterprise Data Quality and Master Data Management platform
• Integrate and standardize HR and master data from distributed sources
• Implement automated data quality profiling, cleansing, and validation
• Enable governed master data lifecycle management (MDM)
• Improve analytics and reporting through trusted, centralized data
• Automate infrastructure provisioning and deployment processes

Solution

Implemented an end-to-end data integration and quality pipeline integrating SharePoint, IICS, AWS S3, and Amazon Redshift to ingest, cleanse, validate, and store HR and master data in a centralized repository. 

Developed a Data Quality framework with automated profiling, validation rules, and monitoring dashboards (Power BI) enabling business-managed data quality controls and stewardship visibility. 

Delivered a cloud-native Master Data Management (MDM) platform using Informatica MDM 360 to manage relationships and golden records across core domains such as customer, product, and supplier. 

Designed and built an Enterprise Data Management Infrastructure-as-Code (IaC) framework using AWS CDK and Azure DevOps to automate provisioning of Informatica and AWS infrastructure components (agents, connections, security, pipelines). 

Implemented secure data integration and sharing mechanisms including encryption, access management, APIs, and automated deployment pipelines supporting MDM and governance use cases. 

Challenges

• Integrating heterogeneous data sources across merged organizations
• Maintaining data quality during continuous HR and master data changes
• Establishing governed master data ownership and relationships
• Automating complex cloud and Informatica infrastructure setup
• Enabling analytics access while preserving security and compliance

Results

• Achieved over 30% reduction in data quality errors through automated rules
• Established centralized and governed master data domains
• Improved reporting reliability via trusted Redshift data repository
• Reduced manual data cleansing and infrastructure setup effort
• Enabled scalable, reusable MDM and data quality platform foundation

Lessons Learned

• Automated data quality controls significantly improve trust in analytics
• Master data governance must be embedded in integration pipelines
• Infrastructure-as-Code accelerates platform scalability and consistency
• Business-friendly data quality dashboards drive stewardship adoption
• Leveraging familiar client technologies accelerates implementation

Conclusion

The Data Quality & Master Data Management Platform delivery established a scalable, automated cloud foundation for governing and managing enterprise master data. By integrating data quality, MDM, and infrastructure automation, the initiative significantly improved data accuracy, reporting reliability, and operational efficiency across critical business domains.