Flexible Behavioral Chain Framework for Enterprise Blockchain Data Integrity
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Solution Overview
Problem
Current methods for storing and analyzing customer journey data in siloed databases fail to maintain the integrity and traceability of raw data, making it difficult for enterprises to backtrack analytics or aggregation results to their original, unaltered form, which is essential for informed decision-making.
Innovation Solution
A flexible behavioral chain (FBC) framework for permission-based enterprise-focused blockchain applications that includes a chain framework module, customer value score derivation module, trigger management module, and machine learning update module, allowing for the creation and validation of new blocks in a blockchain system, which incorporates customer value scores and adjusts them dynamically based on customer interactions and events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If customer journey data is stored in silo databases and normalized over time, then data storage and processing is simplified, but the ability to recover or re-create the original source of truth is lost
Solution Approach 1:
The system performs preliminary actions by creating cryptographic hashes of the original raw customer journey data before any normalization or processing occurs. These hashes are stored in the blockchain ledger, preserving the source of truth in advance. This allows later verification and recovery of the original data state without compromising the ease of data processing.
Solution Approach 2:
The patent introduces cryptographic hashes as an intermediary between the raw data and the normalized data. The hash acts as a digital fingerprint that verifies the integrity and origin of the original data. This intermediary mechanism enables both easy data processing (through normalization) and preservation of source truth (through hash verification) simultaneously.
2Reliability
If blockchain is used to store data in full, then data integrity and traceability are maximized, but storage space and system complexity increase
Solution Approach 1:
The patent extracts only the essential cryptographic hashes and metadata from the full customer journey data and stores these extracted elements in the blockchain. The actual raw data remains stored in traditional databases. This extraction approach maintains data integrity and traceability through the blockchain's immutable hash records while avoiding the storage overhead and complexity of storing complete data sets on the blockchain.
3Productivity
If traditional databases are used for customer journey data, then storage and retrieval is efficient, but security and traceability are compromised
Solution Approach 1:
The system segments the data management architecture into two parts: traditional databases handle efficient storage and retrieval of customer journey data, while blockchain stores cryptographic hashes and metadata for security and traceability. This segmentation allows each system to excel at its specialized function without compromising the other.
Solution Approach 2:
The blockchain ledger provides continuous feedback about data integrity through its immutable hash records. Any attempt to alter normalized data can be detected by comparing against the stored hashes, creating a feedback mechanism that ensures security and traceability while maintaining efficient database operations.
Data Source
AI summary
Concepts and technologies disclosed herein are directed to a flexible behavioral chain (“FBC”) framework for permission-based enterprise-focused blockchain applications. According to one aspect disclosed herein, a chain framework module (“CFM”) executed by a FBC system can receive a new block request to add a new block to an FBC. The new block request can be created by a trigger management module (“TMM”) executed by the FBC system in response to a trigger, such as a customer touchpoint, a customer engagement, a subscribed event, and/or a virtual event. In response to the new block request, the CFM can generate the new block. A customer value score derivation module (“CVSDM”) executed by the FBC system can determine a customer value score (“CVS”) for a customer. The CFM can incorporate the CVS into the new block. The CFM can connect the new block to the FBC.