Trends, Challenges, and Technical Practices of Modern Data Analysis and Indicator Platforms

The presentation begins by outlining the history of data analysis and business intelligence (BI), tracing its development from the 1950s through the 2000s, and noting the shift from IT‑centric reporting to business‑driven, fine‑grained analytical practices.

Key modern trends are identified: precision (ensuring data consistency across tools), agility (supporting rapidly changing business metrics), and real‑time capabilities (moving from daily to sub‑hourly reporting). These trends increase data system complexity and demand more flexible data access.

Overall industry trends include mass data analysis for all users, proactive data‑driven approaches where data seeks users, and AI‑enhanced analysis that moves from drag‑and‑drop to conversational query and automated business recommendations.

The article then enumerates three major problems in current data analysis: difficulty locating data (large, complex data lakes and warehouses), low data value (most stored data is unused), and low accuracy (inconsistent metrics across reports). It also highlights contradictions between analysts and business users regarding request cycles, data freshness, and usability.

To address these issues, a unified semantic layer (indicator platform) is proposed, consisting of an indicator center for data developers and a data portal for data consumers. The architecture separates model definition, metric publishing, and downstream consumption.

The workflow for data developers involves creating or updating models and metrics, while data users focus on metric discovery, dashboard creation, and integration with downstream tools.

Intelligent query routing is introduced with three layers: ad‑hoc queries (direct SQL), theme acceleration (pre‑aggregated tables), and query caching (millisecond‑level responses). This routing abstracts the underlying data source complexity from end users.

A unified query entry point standardizes access across BI tools, SDKs, and APIs, translating various query languages into a common MetricsQL. The QueryEngine then generates appropriate SQL for the target data source, handling acceleration and permission management.

Real‑time metric solutions are described, aiming for T+0 latency by ingesting data via CDC into a real‑time warehouse and exposing metrics through the semantic model.

In conclusion, the unified query entry ensures accuracy, intelligent routing provides agility, and real‑time solutions deliver immediacy, collectively supporting the goal of universal data analysis where any user can quickly access reliable insights without deep technical knowledge.