DataLab: A Unified Platform for LLM-Powered Business Intelligence (2412.02205v3)

Abstract: Business intelligence (BI) transforms large volumes of data within modern organizations into actionable insights for informed decision-making. Recently, LLM-based agents have streamlined the BI workflow by automatically performing task planning, reasoning, and actions in executable environments based on natural language (NL) queries. However, existing approaches primarily focus on individual BI tasks such as NL2SQL and NL2VIS. The fragmentation of tasks across different data roles and tools lead to inefficiencies and potential errors due to the iterative and collaborative nature of BI. In this paper, we introduce DataLab, a unified BI platform that integrates a one-stop LLM-based agent framework with an augmented computational notebook interface. DataLab supports various BI tasks for different data roles in data preparation, analysis, and visualization by seamlessly combining LLM assistance with user customization within a single environment. To achieve this unification, we design a domain knowledge incorporation module tailored for enterprise-specific BI tasks, an inter-agent communication mechanism to facilitate information sharing across the BI workflow, and a cell-based context management strategy to enhance context utilization efficiency in BI notebooks. Extensive experiments demonstrate that DataLab achieves state-of-the-art performance on various BI tasks across popular research benchmarks. Moreover, DataLab maintains high effectiveness and efficiency on real-world datasets from Tencent, achieving up to a 58.58% increase in accuracy and a 61.65% reduction in token cost on enterprise-specific BI tasks.

• The paper introduces DataLab, a unified platform that integrates domain knowledge, inter-agent messaging, and cell-based context management for BI tasks.
• The paper employs a structured communication mechanism with a finite state machine and shared buffer to enhance agent collaboration in complex BI workflows.
• The paper demonstrates state-of-the-art performance, achieving up to 58.58% accuracy improvement and a 61.65% reduction in token costs on enterprise datasets.

Overview of DataLab: A Unified Platform for LLM-Powered Business Intelligence

The paper on "DataLab: A Unified Platform for LLM-Powered Business Intelligence" presents a sophisticated approach towards integrating LLMs within business intelligence (BI) workflows. The authors develop and evaluate a platform that leverages LLM-based agents to unify BI tasks in a single environment, addressing limitations in existing systems that often treat such tasks in isolation.

Contributions and Technical Details

The core contribution of the paper is the development of DataLab, which supports various BI tasks through a singular framework. This is achieved by integrating three critical components:

1. Domain Knowledge Incorporation: To enhance LLMs' understanding of enterprise-specific data, the authors propose an automated method to generate, organize, and utilize domain knowledge. This includes constructing a knowledge graph to map out the relationships and usage patterns of databases, tables, and columns.
2. Inter-Agent Communication: DataLab employs a structured communication mechanism to facilitate efficient information exchange among multiple agents required for complex BI tasks. This mechanism uses a finite state machine (FSM) and a shared information buffer, which enables control over communication flows and enhances agents' collaborative performance.
3. Cell-based Context Management: By employing directed acyclic graphs (DAGs) to represent notebook cell dependencies, this module adapts the context dynamically according to task requirements. This approach effectively minimizes unnecessary token usage and enhances the workflow efficiency of LLMs in multi-modal notebook environments.

Experimental Evaluation

The paper's experimental section demonstrates the efficacy of DataLab across various BI tasks, such as NL2SQL, NL2DSCode, NL2VIS, and NL2Insight. The platform consistently achieves state-of-the-art performance on popular benchmarks, with notable increases in execution accuracy and reductions in computational costs. For instance, the results highlight DataLab's ability to boost accuracy by up to 58.58% and reduce token costs by 61.65% on enterprise-specific datasets from Tencent.

Implications and Future Directions

The implications of this research are significant, both from a practical and theoretical perspective. Practically, DataLab offers a streamlined environment for BI tasks that can improve productivity and decision-making efficiency within organizations. Theoretically, the work opens new avenues for investigating how LLMs can be further refined and integrated into domain-specific applications, potentially enhancing their reasoning and collaborative capabilities.

Future research could expand on this work by exploring more complex inter-agent communication strategies, scaling the platform for larger enterprise environments, and refining domain knowledge extraction and utilization techniques. Additionally, adapting the system for real-time BI applications where data streams continuously presents a challenging yet promising direction.

In summary, DataLab represents a cohesive step towards harnessing LLMs within the BI domain, showcasing the potential of such integration in optimizing and unifying BI processes across complex organizational structures.