Enterprise Relationship Data Consolidation via Hierarchy Manager
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Solution Overview
Problem
Enterprises face challenges in obtaining a comprehensive view of customer interactions due to disparate data sources and different data models, leading to fragmented customer relationship data and missed opportunities for revenue and profitability.
Innovation Solution
A system that consolidates and normalizes relationship data across multiple sources, using a hierarchy manager to cleanse, standardize, and map data into an enterprise-normalized format, providing a 360° view of customer relationships and enabling cross-hierarchy navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If enterprises use multiple independent data sources for different business functions, then each application can be managed independently with specialized data models, but data becomes dispersed across multiple sources making it difficult to obtain a comprehensive view of customer relationships
Solution Approach 1:
The patent merges data from multiple independent data sources into a unified customer view by establishing entity relationships between records across different applications. The system consolidates customer data, account data, and interaction data from disparate sources while maintaining the independence of each source system, resolving the contradiction between application independence and data completeness.
Solution Approach 2:
The patent creates a universal data model that can represent customer relationships across multiple business functions and applications. This universal model serves as a common framework that accommodates data from different specialized applications, enabling comprehensive customer views without requiring changes to the independent application architectures.
2Productivity
If different applications use different data models to track customer interactions, then each application can optimize for its specific function, but reconciling relationship data between applications becomes difficult
Solution Approach 1:
The patent introduces an intermediary layer that translates between different application-specific data models and a unified customer relationship model. This intermediary handles the complexity of data reconciliation by mapping data from various sources using standardized entity relationships, allowing applications to maintain their optimized data models while enabling comprehensive data integration.
3Ease of manufacture
If enterprises maintain data in disparate data sources, then data can be stored in application-optimized formats, but opportunities for cross-sell and up-sell are lost due to incomplete customer views
Solution Approach 1:
The patent segments customer relationship data into distinct entity types (customers, accounts, interactions) while maintaining the organizational structure of source systems. This segmentation allows data to remain in application-optimized formats at the source while creating a consolidated view that reveals cross-sell and up-sell opportunities by connecting previously siloed information.
Data Source
AI summary
Some embodiments provide a system for processing relationship data that expresses relationship between various entities. In some embodiments, the entities are entities associated directly with an enterprise. The entities in some embodiments described below also include entities associated indirectly with the enterprise through other entities. However, one of ordinary skill will realize that some embodiments might only track relationships between entities directly associated with an enterprise. In some embodiments, the system consolidates disparate relationship data sets that relate to the same set of entities. For instance, in some embodiments, the system includes several data storages that store relationship data. For at least two entities, at least two different data storages store two different relationship data sets that differently express the relationship between the two entities. The system includes a hierarchy manager that receives the two different relationship data sets and consolidates the two different relationship data sets into one relationship data set that best expresses the relationship between the two entities.