Data Asset Access Governance Through Interconnection Graphs
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Solution Overview
Problem
Existing data governance systems lack effective methods for managing and governing digital assets, leading to inefficiencies in data management, security, and compliance, as well as unnecessary resource expenditure due to the lack of a visual or structural representation of data interconnectedness.
Innovation Solution
A data governance graph is implemented to represent interconnections between data sets based on common traits, using a computer-implemented method to generate and display interconnections and recommendations for additional data sets with common traits, utilizing a graphical user interface to visualize data policies, procedures, and usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data governance systems are used without visual representation, then data management processes are simplified, but data management efficiency and understanding of data interconnectedness deteriorate
Solution Approach 1:
The patent introduces a data governance graph as an intermediary visual representation layer between users and the underlying data assets. This graph intermediates complex data relationships by displaying nodes representing data assets and edges representing relationships between them, allowing users to visually understand data interconnectedness without directly managing the complexity of the underlying data governance system.
2Measurement precision
If no visual representation of data interconnectedness is provided, then system complexity is reduced, but classification accuracy and security deteriorate
Solution Approach 1:
The patent employs visual differentiation in the data governance graph by using different colors or visual attributes for nodes and edges to indicate various data asset types, relationship types, security classifications, or compliance statuses. This visual encoding enables users to quickly classify and understand data assets and their relationships, improving classification accuracy through intuitive visual cues.
3Loss of time
If manual data asset management is used without automated visualization, then resource expenditure is reduced, but time consumption and efficiency deteriorate
Solution Approach 1:
The data governance graph system operates autonomously by automatically discovering data assets, determining their relationships, and generating the visual graph representation without requiring manual intervention. The system self-updates the graph as new data assets are added or relationships change, eliminating the need for manual data governance documentation and significantly reducing time consumption.
4Reliability
If comprehensive data asset tracking is implemented, then security and compliance improve, but resource expenditure increases
Solution Approach 1:
The data governance graph serves multiple functions simultaneously: it visualizes data assets and relationships for understanding, enables classification and discovery, supports security and compliance monitoring, and facilitates data asset tracking. By consolidating these multiple governance functions into a single visual representation system, the patent reduces the need for separate manual processes and tools, thereby reducing overall resource expenditure while maintaining comprehensive security and compliance oversight.
Data Source
AI summary
Systems and methods are disclosed for creating a governance graph representing data set interconnection. The data set interconnections may be based on common fields, sources, databases, applications, or patterns of usage. For example, the interconnections may be direct connections, where one data set is directly downstream from another data set. Alternatively, the interconnections may be indirect connections based patterns showing the data sets are commonly used together. For example, given data sets “A”, “B”, and “C”, if “B” is directly connected to “A” because it is downstream from “A”, and a particular group of users commonly use “B” and “C” together, “A” may be indirectly related to “C” based on the pattern of usage. In this example, the governance graph is configured to indicate the connection between “A” and “B” is stronger than the connection between “A” and “C”, whilst still showing said connection.