A mid-sized bank came to the realization that poor data management was adversely affecting their efficiency as an organization. During a process re-engineering initiative, the organization became aware that countless hours were spent fixing data quality issues and performing complex, manual data processes prone to error across multiple departments. The leadership team knew they needed to resolve this issue but were not sure where to get started.

Challenges

Poor data quality, largely stemming from duplicate data, manual processes and poor front-end validations, was creating delays across the organization. The leadership team recognized they did not have the internal data knowledge to resolve their data issues, but the culture supported a holistic data overhaul. A data strategy and roadmap to guide their future data structure was needed.  

The creation of a 5-year roadmap gave the bank a path to move towards a data driven decision culture.

Solution

Phase 1 of the project included comprehensive interviews across several lines of business to understand the scale and scope of the pervasive issues to create a current state analysis. This phase also included a review of the data architecture and applications in use by the bank. The information collected gave our team a better idea of the current state of the organization.  

Phase 2 encompassed the creation of a 5-year roadmap to move the bank towards a data driven decision culture. The roadmap included tracks for technical infrastructure, data governance and management, business intelligence and machine learning proofs of concept.