AI Sustainability Benchmarking Dashboard for Large Data Summaries
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Solution Overview
Problem
Entities face challenges in efficiently managing, summarizing, and visualizing large volumes of complex sustainability data, making it difficult to assess and compare their sustainability performance effectively.
Innovation Solution
A benchmarking system that utilizes a computer vision model to extract and split sustainability data into subsets, followed by an AI model for summarization and generation of a sustainability dashboard, enabling efficient data management and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If comprehensive sustainability data is collected from multiple sources, then the quantity and completeness of sustainability information increases, but the complexity and difficulty of managing and summarizing the data increases
Solution Approach 1:
The system segments the comprehensive sustainability data into multiple subsets based on different categories, sources, or time periods. This segmentation allows the data to be managed and processed in smaller, more manageable units while maintaining the overall comprehensiveness of the sustainability information.
Solution Approach 2:
The system introduces an intermediary processing layer that includes automated summarization algorithms and data integration modules. This intermediary layer acts as a mediator between the raw comprehensive data and the final visualizations, automatically managing the complexity of data synthesis and presentation.
2Quantity of substance
If sustainability data is collected from multiple sources, then the comprehensiveness of sustainability information increases, but the time required to summarize and process the data increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and summarizing sustainability data from multiple sources before it is needed for analysis or visualization. This includes automatically extracting key metrics, categorizing data, and creating summary statistics in advance, so that when analysis is required, the data is already prepared and ready for quick processing.
Solution Approach 2:
The system maintains continuous operation by continuously collecting, processing, and updating sustainability data from multiple sources. This continuous workflow ensures that the most current and relevant sustainability information is always available without requiring periodic manual intervention to update or reprocess the data.
3Productivity
If traditional methods are used to visualize sustainability data, then the system complexity remains low, but the efficiency and effectiveness of data visualization decreases
Solution Approach 1:
The system replaces traditional manual or simple automated visualization methods with AI-powered generative models that can automatically create sophisticated visualizations. This substitution uses machine learning algorithms to interpret sustainability data and generate appropriate chart types, layouts, and presentations without requiring complex manual configuration or specialized skills.
Solution Approach 2:
The system dynamically changes visualization parameters such as chart types, color schemes, data granulation levels, and presentation formats based on the characteristics of the sustainability data and the needs of the users. This adaptive parameter adjustment allows the system to optimize visualization efficiency and effectiveness for different data types and user requirements without requiring manual reconfiguration.
Data Source
AI summary
A tangible, non-transitory, computer-readable medium includes instructions that, when executed by processing circuitry, are configured to cause the processing circuitry to transmit a set of sustainability data to a computer vision model for extraction into a textualized set of sustainability data, divide the textualized set of sustainability data into one or more subsets of textualized sustainability data, transmit the one or more subsets of textualized sustainability data to an artificial intelligence (AI) model, transmit at least one instruction to the AI model to elicit summarization the one or more subsets of textualized sustainability data into a summarized dataset, and transmit at least one instruction to the AI model to cause generation of a sustainability dashboard comprising at least one metric selected from the summarized dataset.