Building DataLeap's AI-Ready Data Knowledge Base: From Scattered Metrics to Reliable Conversational Analytics

As large models integrate with enterprise data systems, conversational analytics — querying data, running analysis, generating reports — are moving into production. The decisive factor is not model intelligence but the availability of unified, governable data knowledge : metric definitions, business rules, terminology, and analysis templates. In practice, this knowledge lives scattered across documents, dashboards, chat groups, and individual expertise, leading to version conflicts, ambiguous definitions, and models that answer confidently but incorrectly .

Pain Points: Two Roles Blocked by Knowledge Gaps

Analysts can build agents but cannot feed them knowledge efficiently. Critical assets are siloed across platforms, forcing repetitive from-scratch work; maintenance cost grows with each agent, creating unsustainable technical debt.

Operations / Product teams need trustworthy, verifiable answers . Generic assistants lack stable frameworks, cannot enforce strict metric references, and produce unstable outputs that business users dare not rely on for weekly reviews, root-cause analysis, or anomaly detection.

The root cause is a mismatch between knowledge supply form and agent consumption needs — a "fault line" between implicit business knowledge and explicit, executable definitions. Bridging it requires a data knowledge base that weaves scattered definitions, tacit experience, and data assets into a searchable, explainable, traceable, and governable system.

Goal & Design Principles

Transform dispersed knowledge (Feishu docs, reports, SQL, tribal knowledge) into an AI-understandable, reusable, controllable, continuously improvable knowledge base that powers reliable analytical agents for BI and operations.

AI-understandable : structured, parsable, callable semantic definitions and business rules — not human-readable documents.

Reusable : build once, invoke across scenarios and applications, eliminating duplicate construction and metric drift.

Controllable : clear data lineage and ownership, permission management, standardized versioning and change process.

Continuously improvable : measurable, gap-detectable, with a closed-loop mechanism for ongoing enrichment and optimization.

Agents then receive stable, reliable knowledge supply instead of relying on chance retrieval hits.

Technical Implementation

1. Multi-Platform Semantic Integration

Reality: field descriptions in Platform A, report metrics in Platform B, metric definitions in Feishu docs, SQL templates in chat groups. Teams manually stitch information — slow, drifting, fragile; a single departure can erase a critical definition.

Solution: connect multi-platform semantics; use a centralized configuration table to hold structured table, column, and metric definitions. Business users shift from copy-paste to reference — one change propagates everywhere — achieving scalable reuse with guaranteed consistency.

2. Intelligent Semantic Completion

Enterprise data contains abundant "business jargon." AI must map each term to standard semantics, enum values, and synonym/alias mappings. Two guiding rules:

Incomplete → complete.

Non-standard → standardize.

Flow: AI one-click semantic mapping → human review → result persisted as unified, reusable knowledge asset. Four high-value categories proven in practice: semantic types, enum mappings, applicable objects, synonyms . This turns "senior analyst tacit knowledge" into explicit, reusable configuration.

3. Layered Semantic Knowledge Architecture

DataLeap's distinctive design: do not lump all data knowledge together. Split by what AI needs it for into three independently governed layers:

Layer 1 — Semantic Engine : lets AI understand what data means (metric definitions, field semantics, enums). Hard knowledge — strict alignment, controllable, no drift.

Layer 2 — Knowledge Engine : lets AI know what the enterprise has already codified (business rules, calculation logic, analysis templates). Soft knowledge — evolves, supports continuous completion and update.

Layer 3 — Analysis Patterns : tells the model how to solve complex problems (methodologies, step-by-step reasoning frameworks). Methodological knowledge — requires process-oriented expression, not a single SQL.

Separation is mandatory because governance requirements differ fundamentally across the three types.

4. Evaluatable Data Flywheel

After the first three steps, the team thought the base was launch-ready. Production quickly revealed a critical gap: generated knowledge lacked evaluation standards, preventing continuous iteration; when results went wrong, there was no fast way to pinpoint whether the issue lay in knowledge, metrics, or the model.

Added a "evaluable + sustainable completion" closed loop :

Pre-launch: introduce evaluation and regression gates to ensure key use cases meet thresholds.

Post-launch: continuously write high-frequency issues back into the knowledge base as standardized metrics, SQL, and rules.

Three stable metrics: parse accuracy, result consistency, report structure usability — quantifying effectiveness and turning reactive firefighting into controlled regression.

Failure samples continuously feed the knowledge base, driving a positive flywheel of capability iteration.

Best Practices & Verified Results

Deployed across finance, financial services, enterprise, and ride-hailing invitation scenarios. With a mature semantic and business knowledge system, recall rate and overall stability reach 90% .

Three core agent scenarios validated:

Data Query : natural-language to SQL with governed metric references.

Anomaly Diagnosis & Analysis : automated root-cause exploration using layered analysis patterns.

Analysis Report Generation : structured, verifiable reports combining retrieved metrics, applied rules, and methodological steps.

Closing Insight

One-sentence summary: whether a data-analysis agent lands stably depends not on model capability but on whether the data knowledge base is complete, metrics are consistent, and assets are reusable.