Data Governance Graphs for Visualizing Asset Interconnection Strength
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data governance systems lack effective methods for visually representing the interconnectedness of digital assets, leading to inefficiencies in data management, security, and compliance, as well as unnecessary resource expenditure in understanding data structures.
Innovation Solution
A data governance graph is created to visually represent interconnections between data sets based on common traits, usage patterns, and policies, using a computer-implemented method to generate and display a governance graph that indicates connection strengths through line thickness and length.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional data governance systems are used without visual representation, then data management processes become complex and resource-intensive, but implementing visual representation requires additional system complexity and development resources
Solution Approach 1:
The patent creates a visual copy (graphical representation) of the data asset interconnections that mirrors the actual data relationships. This visual copy allows users to understand and navigate data connections without directly interacting with the complex underlying data infrastructure, thereby improving ease of operation while containing system complexity through abstraction.
Solution Approach 2:
The visual representation system acts as an intermediary layer between users and the complex data governance infrastructure. It mediates by translating complex data relationships into intuitive visual formats, allowing users to interact with simplified representations rather than the full complexity of the data ecosystem directly.
2Reliability
If comprehensive data interconnection analysis is performed to understand all data relationships, then complete data governance is achieved, but significant computational resources and time are consumed
Solution Approach 1:
The system performs partial analysis by focusing on visualizing and analyzing only the most relevant data interconnections rather than comprehensively processing all possible data relationships. The graphical representation highlights key connections and relationships that are most important for governance decisions, avoiding the need to analyze every single data connection in detail.
Solution Approach 2:
The patent segments the complex data ecosystem into discrete visualizable units (nodes and edges in a graph). By breaking down the entire data infrastructure into individual data assets and their connections, the system can analyze and present information in manageable segments rather than as one overwhelming comprehensive analysis.
3Loss of information
If detailed visual representation of all data connections is implemented, then complete understanding of data ecosystem is achieved, but system complexity and computational requirements increase significantly
Solution Approach 1:
The visual representation system applies local quality by providing different levels of detail and visualization for different parts of the data ecosystem. Rather than uniformly representing all data connections with the same level of detail, the system can highlight specific relationships or areas of interest with enhanced visualization while keeping other areas more simplified, thereby maintaining information visibility while controlling system complexity.
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.