Data Confidence Fabric for Enterprise Asset Trust
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Solution Overview
Problem
Implementing Data Confidence Fabric (DCF) functionality in enterprise contexts is challenging due to differences in usage patterns and data transit compared to edge contexts, requiring automated and reliable data confidence generation, trusted device integration, specific data type management, and compliance with enterprise policies.
Innovation Solution
A Data Confidence Fabric system is implemented within an enterprise setting, enabling trusted device inclusion and exclusion, automated metadata registration, specific file type management, and real-time compliance monitoring, using APIs, SDKs, and ledger technology to annotate and score data for trustworthiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Data Confidence Fabric functionality is implemented in enterprise contexts, then data trustworthiness measurement is improved, but implementation complexity increases due to different usage patterns and data transit compared to edge contexts
Solution Approach 1:
The patent creates a universal Data Confidence Fabric system that can operate across both edge and enterprise contexts by implementing a standardized architecture that handles diverse data types (structured, unstructured, semi-structured) and usage patterns through a common framework, allowing the same core functionality to serve multiple environments with different requirements
Solution Approach 2:
The system segments the data confidence generation process into distinct modular components including data annotation services, confidence scoring engines, ledger integration modules, and policy enforcement layers, allowing each component to be independently configured and managed according to specific enterprise requirements while maintaining overall system coherence
2Productivity
If automated data confidence generation is implemented, then data processing efficiency is improved, but system complexity increases due to automated metadata registration and compliance monitoring requirements
Solution Approach 1:
The patent implements self-service mechanisms where the Data Confidence Fabric automatically performs metadata registration, confidence scoring, and compliance checking without requiring manual intervention. The system autonomously monitors data transit, registers metadata with ledgers, and enforces policies, reducing operational complexity despite increased automation capabilities
Solution Approach 2:
The system incorporates continuous feedback loops where confidence scores are generated, validated against policies, and used to automatically adjust data handling decisions. This feedback mechanism streamlines processing by making real-time decisions based on automated confidence assessments, improving efficiency while managing complexity through rule-based automation
3Reliability
If trusted device inclusion and exclusion mechanisms are implemented, then data source reliability is improved, but operational complexity increases due to asset management requirements
Solution Approach 1:
The patent implements preliminary action by pre-configuring inclusion and exclusion lists of trusted assets before data processing begins. The system pre-establishes confidence thresholds, policy rules, and device trust credentials, allowing automated verification and decision-making during data transit without requiring complex real-time operational interventions
Data Source
AI summary
One example method includes checking an asset against an Inclusion List and/or an Exclusion List to determine if the asset is permitted to contribute data, generated by the asset, to an enterprise data confidence fabric, when the asset is present on the Inclusion List, or not present on the Exclusion List, designating the asset as a trusted asset and appending the data generated by the asset to a ledger of the enterprise data confidence fabric, updating a ledger content index to reflect the data that was appended to the ledger, and annotating the data generated by the asset with trust metadata.