Data Quality Visualization Suggestions for Oil and Gas Users
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Solution Overview
Problem
In oil and gas data management systems, it is challenging to efficiently match users with data collections that require quality improvement due to the vast amount of data and multiple entities managing different data sets, leading to potential unawareness of quality issues among users.
Innovation Solution
A method that tracks user interaction with the data management system to dynamically suggest and generate data quality visualizations, prompting users to address quality issues through visualizations such as graphs or charts, thereby improving data quality awareness and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data management systems store vast amounts of oil and gas data with quality information, then data completeness and accuracy are improved, but user awareness of quality issues deteriorates due to the difficulty of matching users with relevant data collections
Solution Approach 1:
The system segments the vast data collection into user-specific views by tracking individual user interactions and generating personalized visualization suggestions. This divides the overwhelming global data landscape into manageable, relevant portions for each user, making quality issues visible without requiring users to monitor all data collections.
Solution Approach 2:
The system implements a feedback loop by tracking user interactions with data and quality visualizations, then using this information to dynamically generate and update visualization suggestions. This closed-loop feedback mechanism ensures users receive targeted quality information based on their specific data access patterns and responsibilities.
2Reliability
If users manually monitor multiple data collections for quality issues, then data quality management is improved, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by proactively generating and presenting data quality visualization suggestions to users before they would need to manually search for quality issues. This advance preparation of quality information eliminates the need for users to spend time manually monitoring multiple data collections.
Solution Approach 2:
The system enables self-service data quality monitoring by automatically tracking user interactions and generating personalized visualization suggestions without requiring user initiation or manual effort. Users simply access their suggested visualizations, which are automatically updated based on their interaction patterns.
3Loss of information
If the system generates comprehensive data quality visualizations for all users, then data quality awareness is improved, but system complexity and computational resources deteriorate
Solution Approach 1:
The system applies local quality by generating visualization suggestions tailored to each user's specific data access patterns and responsibilities rather than providing uniform comprehensive visualizations to all users. This localized approach reduces system complexity by only generating relevant visualizations for each user context.
Solution Approach 2:
The system implements partial action by generating a selective subset of quality visualizations based on tracked user interactions rather than comprehensively monitoring all data collections for all users. This partial approach maintains adequate quality awareness while reducing computational complexity.
Data Source
AI summary
A method, apparatus, and program product dynamically suggest and/or generate data quality visualizations for users of an oil and gas data management system based upon tracked user interaction with the data management system. By tracking user interaction with various data maintained by the oil and gas data management system, a user responsible for particular data may be prompted to view a visualization associated with the quality of such data, thereby improving the user experience and in many cases leading to data quality issues being addressed in a more responsive and efficient manner.