01 / Business context
Operational requirement
The customer data environment involved 5,000+ tables and 100,000+ attributes. This scale made the relationship between data structure, platform integration and how people use information an important part of the work.
Build a customer data foundation that connects enterprise information and analytics, while aligning the surrounding workflows, organizational responsibilities and staff capabilities with the platform.
02 / Contribution & delivery scope
Scope at a glance
Illustrative scope map based on the described work.
- Worked on SAP customer data platform implementation across multi-cloud infrastructure, connecting the technical foundation with the requirements of a substantial enterprise data environment.
- Applied AI/ML to customer analytics, bringing data integration and analytical use together within the solution’s design and delivery.
- Reengineered workflows and addressed organizational structure and staff capabilities, linking platform changes with the practical work needed to use the resulting information.
03 / Implementation outcome
The work established a basis for integrated customer information and AI/ML analytics, supported by changes to workflows and organizational capabilities. Technology and business operating practices were addressed together.
04 / Application perspective
Relevance for future engagements
These principles can inform discussions with industrial groups and large cooperatives about connecting information across functions or member units. The appropriate platform, data scope and organizational approach would depend on each enterprise’s needs.