01Business problem
The bespoke, home-grown and on-site data platform could not keep pace with the data needs of organic customer growth and external acquisitions. Without a scalable, standardised platform, data quality issues multiplied with every new acquisition.
02Intervention
A gap analysis of the current landscape, the conceptual data model and the bespoke master data set-up, followed by a new data architecture aligned with the company strategy, the ongoing IT projects and a clear master data management direction, with tooling recommendations and an RFP-based tool selection.
03Role of data & AI
This one is about data before AI: master data that is right the first time is the foundation any AI in the group will run on. The approach shifts the organisation from correcting data after the fact to preventing errors at the source.
04Adoption
The architecture and the MDM direction were aligned with the running IT projects, so the internal IT and data teams owned the roadmap from the start and ran the tool selection with us.
05Measurable impact
A clear, business-aligned path from a bespoke platform to a standardised, cloud-based approach, designed to absorb organic and acquired data growth.