Coherence
The degree to which datasets can be combined.
- •Ability to create a unified, consistent view of business entities (e.g., 360-degree customer view, end-to-end product lifecycle).
- •Enhanced data integration capabilities for comprehensive and reliable enterprise-wide analysis.
- •Improved consistency and comparability in reporting and analytics across different datasets and business units.
- •Streamlined data exchange and improved interoperability between internal systems and with external partners.
- •Difficulty in combining data from different sources, leading to a fragmented and incomplete understanding of the business.
- •Inconsistent or conflicting insights when analyzing disparate datasets, leading to confusion and distrust.
- •Increased complexity, cost, and time required for data integration and reconciliation efforts.
- •Siloed information hindering strategic alignment and operational coordination.
Story
Logistics: Both inventory and order datasets use the same standardized 'ProductSKU' and 'LocationID', allowing seamless combination for stock analysis across locations.
Retail: All customer-facing systems (CRM, PoS, Online Store) use a globally unique 'CustomerMasterID', enabling a 360-degree customer view.
Healthcare: All clinical datasets within an organization use a common Master Patient Index (MPI) identifier, ensuring data can be reliably combined for patient care and research.
Logistics: Sales data uses 'CustomerID' (integer) while shipping data uses 'Customer_Ref_Code' (alphanumeric string) for the same customer, preventing easy joins.
Retail: Product data from the e-commerce platform uses 'SKU_Online' while the ERP system uses 'MaterialNumber', hindering unified inventory views.
Healthcare: Datasets from different hospital departments use varying patient identifier schemes, making it difficult to create a longitudinal patient record.