Recent acquisitions by a global financial services client necessitated that the new businesses be consolidated onto the existing enterprise analytical data platform. That existing platform included the database engine as well as extract, transform and load (ETL) and data presentation tools. The client's widely distributed business decision-makers required flexible access to the integrated analytical data from multiple operational data repositories. The acquisitions were significantly different from the business on which the enterprise platform was originally based therefore each data source presented its own data integration challenges.
Solution:
CapTech Data Architects familiar with both industry best practices and technologies quickly assessed the client's current state and direction. CapTech determined that although both enterprise platforms were in place with standards for optimal use of those platforms, the consolidation path was not simple. Further, the team's developers were not familiar with existing standards, guidelines and expectations.
Based on these findings, CapTech evaluated all proposed data models and designs to ensure sound solutions adhered to current client standards. Our architects recommended the optimized use of enterprise tools before any proposed solutions were implemented. As gaps or conflicts were uncovered within existing standards, CapTech crafted the documentation necessary for resolution and facilitated the knowledge transfer to all stakeholders. CapTech consulted with data modelers to apply optimizations for the database platform, and with developers to ensure available tools were properly used in accordance with client standards and industry best practices. Finally, our Data Architects collaborated with end-users to manage expectations and ensure compliance with both internal and external requirements.
Tools:
Teradata
Ab Initio
SAP BusinessObjects
ERwin Data Modeler
Results:
Greater understanding of integrated customer performance data.
Reduced cost of overall ownership by leveraging shared enterprise data management tools and sharing tool knowledge with enterprise peers.
Mentored data modelers and developers better understand tool capabilities and program expectations.