Evolution of BI Data Products, Current Challenges, and Generative AI Applications

In today's data‑driven industry, Business Intelligence (BI) products play an increasingly important role in enterprise decision‑making. The presentation outlines the evolution of BI data products, current challenges in fine‑grained data analysis, ABI strategies to address these challenges, and the rise of large language models (LLMs) for a new chapter in data analysis.

1. Evolution of BI Data Products

BI originated from Decision Support Systems (DSS) and has progressed through four stages: the 1970s‑80s era of isolated reporting, the 1990s‑2000s era of data warehouses and OLAP, the 2000s‑2010s BI 2.0 era of improved development efficiency and real‑time collaboration, and the 2020s‑present BI 3.0 era where intelligent analysis and generative AI (GBI) emerge.

2. Fine‑Grained Data Analysis and Product Dilemmas

Challenges include data integration and metric standardization, real‑time data processing, rapid analysis and execution, diverse product categories, varied user personas, and fast‑changing market competition. Traditional dashboard products face high learning costs, limited customization, and difficulty maintaining consistent metric definitions.

3. ABI Strategies to Overcome Dilemmas

ABI (Analysis & Business Intelligence) focuses on data efficiency and democratization, offering lightweight visual modeling, standardized metric services, real‑time data access, low‑threshold analysis tools, collaborative knowledge bases, and AI‑assisted analyst capabilities.

4. Generative BI Tool Architecture

The modern data stack‑based generative BI tool consists of four layers: data ingestion, data preparation, intelligent analysis, and data application. It integrates standard and non‑standard data, uses a compute‑storage engine (CK + lakehouse), provides smart query routing, visual modeling, multi‑dimensional drilling, and supports low‑code visual dashboards.

5. Rise of Large Models in Data Analysis

Large language models such as GPT‑4, Claude, Llama, ChatGLM, and Baichuan enable natural language understanding, intelligent reasoning, code generation (Python, R), and new interaction paradigms that lower the barrier for data analysis.

6. ChatBI: Conversational BI Assistant

ChatBI combines GPT‑based LLMs with internal knowledge bases and data tools via LangChain agents, allowing users to ask natural‑language questions, retrieve knowledge, execute secure data queries, and receive visual results within minutes, dramatically reducing analysis time from hours or days.

7. Q&A Highlights

Difference between visualization platforms and analysis platforms.

Models used: local ChatGLM2 and future ChatGLM3.

Ensuring answer accuracy through metric services and consistent data definitions.

ChatBI architecture includes knowledge base, intent recognition, and data engine integration.

The session concludes with a thank‑you note and references to related past talks.