As a result of its extended growth and successful M&A strategy, a multinational specialty insurer with multiple lines of business acquired and operated numerous technology systems to maintain policy, broker, insured, and other information. But supporting multiple policy administration and financial systems was becoming increasingly difficult and prone to data quality and consistency issues, including: duplicate broker and insured information, which forced downstream reconciliation and slowed migration to new systems; manual entry of policy submission data into multiple systems, which caused errors, inconsistencies, and reporting-team impacts; and inconsistent use of data across systems and departments, which led to confusion about basic data definitions and conflicting data rollups and hierarchies. Recognizing that there were no formally established data governance or data management processes, including no master data management structures or data stewardship platforms or processes, the CIO asked Trexin to lead a comprehensive Data Governance assessment, strategy, and roadmap project to create an executable plan for implementing a Data Governance program that would resolve current issues while positioning the enterprise for future growth.
Structured as a 3-phase, 7-week effort that leveraged Trexin’s Data Governance Assessment Framework, the project progressed from current-state evaluation to future-state visioning to implementation planning. More specifically, this approach included:
- Establishing clear business objectives and value for the program
- Reviewing the existing gaps with data governance from a people, process, and technology perspective
- Profiling key data elements, identifying issues, and prioritizing actions
- Identifying key data structures and codes that needed data governance
- Developing processes, technology, and organizational recommendations to implement a full Data Governance program
Trexin’s future-state vision and implementation plan included a data governance organizational/operating model with key role and responsibility definitions (including the addition of a new Data Steward role), data governance process definitions, itemization of key entities requiring master data and reference data management, performance metric definitions to measure data governance success, a conceptual architecture with integration options, and four technology/tool options with estimated costs and resource requirements to facilitate management of master data and reference data.