Data Normalizer for Dynamic Multi-Tenant CDM
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Solution Overview
Problem
Existing Contact Data Management (CDM) systems face significant challenges with data duplication and inconsistency due to disparate data sources, leading to errors, redundant resources, and decreased productivity, as current de-duplication technologies are ineffective in handling massive data sets and cannot be dynamically updated to accommodate infinite data sources and unique tenant requirements.
Innovation Solution
A data normalizer tool that can interface with various CDM systems, normalize data specific to tenant needs, and be dynamically updated to address evolving data entry variations, allowing administrators to customize normalization schema and process data from infinite disparate sources, including web-based services and local area network configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current de-duplication technologies are used, then data duplication can be reduced, but they cannot effectively handle massive data sets and cannot be dynamically updated to accommodate infinite data sources
Solution Approach 1:
The system employs dynamic normalization schemas that can be updated in real-time to adapt to new data sources and evolving data entry variations. The normalization engine dynamically adjusts its behavior based on incoming data characteristics, enabling it to handle infinite data sources while maintaining effective de-duplication capabilities.
Solution Approach 2:
The patent changes the parameters of data normalization by allowing flexible configuration of normalization rules and schemas. This enables the system to adapt to different data formats and sources without requiring complete system redesign, thus maintaining both reliability and versatility.
2Quantity of substance
If data is collected from multiple disparate sources, then data completeness improves, but data consistency and accuracy deteriorate due to different data entry variations
Solution Approach 1:
The system applies local normalization quality by customizing normalization rules for different data sources and fields. Each data source can have its specific normalization requirements applied, ensuring that data consistency is maintained while preserving the completeness of data from diverse sources.
Solution Approach 2:
The normalization engine incorporates feedback mechanisms that continuously monitor data quality and adjust normalization rules accordingly. This feedback loop ensures that data consistency is maintained while collecting data from multiple sources, as the system learns from patterns in the incoming data.
3Measurement precision
If normalization schemas are customized for each tenant, then data accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the normalization functionality into separate, modular schemas that can be independently configured for each tenant. This segmentation allows high data accuracy through custom normalization while managing system complexity through modular architecture, as each tenant's requirements can be addressed by independent schema files.
Solution Approach 2:
The patent uses schema copying mechanisms where normalization schemas can be replicated and adapted for different tenants. This allows data accuracy to be maintained through customized schemas while reducing overall system complexity by reusing proven normalization patterns across multiple tenants.
4Measurement precision
If de-duplication processes are performed repeatedly, then data accuracy improves, but productivity decreases due to time consumption
Solution Approach 1:
The system performs preliminary normalization actions before de-duplication to prepare and standardize data in advance. This preliminary processing reduces the complexity of subsequent de-duplication operations, enabling repeated processes to maintain both high accuracy and improved processing speed through efficient data preparation.
Data Source
AI summary
A data normalizer for processing data for use by a CDM system which is capable of dynamically updating user-defined normalization criteria and which is further capable of operating in a multi-tenant environment in which each tenant has distinct data normalization policies.