Thales launched a NextGen IS/IT initiative to integrate a Data Catalog and Master Data Management system, transforming data into a strategic asset, driving quality, lineage, and consistency across customer information.
Business Challenge
As part of its digital transformation, Thales set out to strengthen its business processes and increase the value of both internal and customer data. The challenge was complex and multi-dimensional:
- Aligning the Data Factory with IS/IT teams to ensure seamless integration.
- Building an enterprise-wide glossary through the Enterprise Architecture function.
- Developing a Data Catalog that could unify source applications and provide complete data lineage.
- Addressing quality gaps in PLM data through detailed analysis.
- Designing an MDM mechanism that could connect and manage five different CRM systems.
Comprehensive Solution
To tackle these challenges, a structured roadmap was executed with strong collaboration across business and IT stakeholders:
Strategic Analysis & Workshops
- Conducted workshops to assess the existing data organization across business units, IS/IT, and value towers.
- Defined priorities around organization, data modeling, data quality, and governance.
Data Quality & Governance
- Identified critical data quality issues and proposed a strategy for a business-driven data organization.
- Introduced KPIs and monitoring on Elastic to ensure ongoing quality assurance.
Unified Catalog & MDM Design
- Executed a pilot project on DataGalaxy to establish a centralized catalog and glossary.
- Designed and deployed the Semarchy MDM platform to manage customer master data repositories.
Business Impact
The deployment delivered significant value across multiple dimensions:
- Unified Master Data: Established Semarchy MDM to consolidate and manage customer repositories across CRMs.
- Cultural Shift: Strengthened the role of data culture and governance in daily operations.
- Centralized Catalog: Launched a DataGalaxy pilot, providing enterprise-wide visibility and data lineage.
- Data Quality Monitoring: Implemented monitoring on Elastic and PLM to detect and resolve issues early.
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