Master Data Layer for Fragmented Customer Data Harmonization
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Solution Overview
Problem
Large business enterprises face inefficiencies in managing fragmented customer data across disparate systems, leading to prolonged resolution times, extended call durations, and inaccuracies in next best action solutions due to vendor-specific data formats.
Innovation Solution
A master data management platform tags business enterprise data with unique entity identifiers, generating multi-domain master records and customer data products using generative AI, which are then used to create next best action and predictive analytics solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If business enterprise data is stored in multiple vendor-specific systems, then data can be maintained in proprietary formats, but data fragmentation occurs leading to prolonged resolution times and extended call durations
Solution Approach 1:
The patent introduces a data lake as an intermediary layer between vendor-specific systems and AI/ML models. This data lake standardizes and harmonizes data from multiple proprietary formats into a unified structure, enabling efficient access without losing vendor-specific format capabilities. The intermediary layer resolves the contradiction by allowing data to remain in original formats while providing standardized access points.
Solution Approach 2:
The patent segments the data architecture into distinct layers: vendor-specific systems, a standardized data lake layer, and AI/ML consumption layers. This segmentation allows each layer to operate independently with its own format requirements, preventing format fragmentation from propagating to the AI models while maintaining resolution efficiency.
2Adaptability or versatility
If business enterprise data is scattered across multiple systems, then each system can maintain its proprietary format, but data inconsistencies and redundancies occur
Solution Approach 1:
The data lake serves as a mediator that enforces data consistency standards while accepting proprietary formats from source systems. It applies harmonization rules to eliminate redundancies and inconsistencies, ensuring reliable data for AI/ML models without requiring changes to the original vendor-specific systems.
Solution Approach 2:
The patent creates a universal data lake layer that can handle multiple proprietary formats simultaneously while maintaining a single standardized representation. This multi-functional layer accepts diverse input formats and outputs consistent standardized data, resolving the contradiction between format diversity and data consistency.
3Adaptability or versatility
If AI models access disparate data formats directly, then vendor-specific data can be utilized, but NBA solution generation becomes inefficient
Solution Approach 1:
The data lake acts as an intermediary between vendor-specific data sources and AI/ML models. It pre-processes and standardizes data from proprietary formats into unified structures that AI models can consume efficiently, eliminating the need for models to handle format conversion and improving NBA solution generation productivity.
Solution Approach 2:
The patent implements preliminary data harmonization and standardization in the data lake layer before data reaches AI/ML models. By pre-processing data into consistent formats and structures, the system eliminates runtime format conversion overhead, significantly improving NBA solution generation efficiency.
4Adaptability or versatility
If fragmented data is used for customer interactions, then vendor-specific data can be maintained, but customer experience and operational efficiency deteriorate
Solution Approach 1:
The data lake serves as a mediator that preserves vendor-specific data in its original systems while providing a harmonized view for operational processes. It translates proprietary formats into standardized structures for customer interaction systems, maintaining operational efficiency without losing vendor-specific data capabilities.
Data Source
AI summary
Solutions are disclosed that synergize fragmented data for use by business enterprise operations. Examples use a master data management (MDM) platform to tag business enterprise data, such as customer relations management (CRM), billing, and enterprise resource planning (ERP) data with unique entity identifiers (IDs) and generate multi-domain master records. A customer data platform (CDP) is built that includes customer data products such as customer disconnection, lead scoring, and market segmentation. A data services layer has artificial intelligence (AI), generative AI, and an API layer, that permit efficient and accurate generation of next best action (NBA) and predictive analytics solutions, as well as access to the customer data products by a business-to-business (B2B) website server that leverages the data from the plurality of customer data products to improve B2B customer experience.