LLM Dashboard Builder for KPI Visualization Selection
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Solution Overview
Problem
Identifying and efficiently generating data visualizations of key performance indicators (KPIs) is computationally expensive due to numerous KPIs with varying data source definitions, units, directions, and parameters, leading to the omission of important data in organizational decision-making.
Innovation Solution
A dashboard builder tool utilizing large language models (LLMs) processes natural language inputs to identify applicable KPIs, map or generate data visualizations, and update dashboards based on system prompts, enabling efficient and holistic data visualization generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional methods are used to identify and generate data visualizations of numerous KPIs with varying parameters, then comprehensive data coverage is achieved, but computational cost and time consumption increase significantly
Solution Approach 1:
The system extracts only the most relevant KPIs from the large set of available KPIs by using LLMs to interpret natural language questions and identify which KPIs are applicable. This extraction process filters out unnecessary KPIs while retaining the essential ones needed to answer the specific question, thus reducing computational overhead while maintaining data comprehensiveness.
Solution Approach 2:
The system performs preliminary action by pre-identifying and caching applicable KPIs and their corresponding visualizations before generating the final dashboard. The LLMs analyze the natural language input and pre-select relevant KPIs, and the system caches these results for future use, avoiding redundant computation when similar questions are asked.
2Measurement precision
If traditional methods are used to manually search and create data visualizations for each KPI, then customization and interpretability are improved, but productivity and efficiency decrease
Solution Approach 1:
The system enables self-service by automatically generating data visualizations without requiring manual intervention. The LLMs interpret natural language questions, identify relevant KPIs, select appropriate visualization types, and generate the visualizations automatically. This self-service capability maintains interpretability through natural language processing while dramatically improving productivity by eliminating manual searching and creation processes.
3Loss of information
If all available KPIs are processed and visualized, then data completeness is maintained, but device complexity and computational resources increase
Solution Approach 1:
The system extracts only the subset of KPIs that are relevant to the specific natural language question asked. The LLMs analyze the question semantics and extract only the applicable KPIs, filtering out the majority of irrelevant KPIs from the large available set. This extraction maintains data completeness for the specific query while significantly reducing computational resource requirements.
Solution Approach 2:
The system applies partial action by processing only the necessary portion of KPIs needed to answer the specific question rather than processing all available KPIs. The LLMs determine the minimal set of KPIs required, and the system generates visualizations only for these partial results, avoiding the excessive computational burden of processing the entire KPI universe.
4Adaptability or versatility
If manual methods are used to identify applicable KPIs and create visualizations, then adaptability to specific use cases is improved, but ease of operation and user effort increase
Solution Approach 1:
The system performs self-service by automatically adapting to specific use cases through natural language processing. Users simply ask questions in natural language, and the LLMs interpret the intent, identify relevant KPIs, and generate appropriate visualizations without requiring users to manually configure or search for applicable data. This maintains full adaptability to diverse use cases while dramatically reducing user effort to near-zero.
Data Source
AI summary
The present discussion relates to using a natural language request to generate a dashboard, the natural language request specifying a use case of the dashboard. Such techniques may also include receiving a system prompt comprising one or more key performance indicators (KPIs), identifying, using a large language model (LLM), an applicable KPI of the one or more KPIs based on the use case of the dashboard, identifying a data visualization for the applicable KPI, generating a dashboard including the data visualization for the applicable KPI.