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From Question-First to Analyst-First: Domain-Expert Skills and Verified Knowledge Compilation for Proactive Enterprise Analytics
Market Intelligence Analysis
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Why This Matters
A research paper proposes a new 'analyst-first' architecture for enterprise analytics that inverts traditional question-first conversational systems by embedding domain-expert 'skills' and verified knowledge compilation. The system aims to proactively generate reports and questions before user input, reducing reliance on pre-formed queries or historical logs. The contribution is architectural and theoretical, with no user-study or benchmark claims.
Market Context
The article may indicate growing enterprise demand for AI-driven analytics automation, which could benefit companies providing enterprise software, data platforms, or AI infrastructure. However, the direct market impact is unclear due to the lack of commercialization, user adoption, or performance benchmarks.
Sentiment
Neutral
AI Confidence
60%
Time Horizon
Long Term
Article Context
Note: This is a brief excerpt for context. Click below to read the full article on the original source.
arXiv:2608.28594v1 Announce Type: new Abstract: Conversational analytics systems assume the user already has a well-formed question, leaving a non-expert facing a blank query box on an unfamiliar enterprise schema. Commercial 'proactive' tools narrow this gap only by detecting statistical anomalies over analyst-curated metric layers, and academic next-question recommenders depend on query logs that a fresh dataset lacks. We describe a production analytics system that inverts the interaction model from question-first to analyst-first through two coupled architectural ideas. First, a pluggable domain-expert 'skill' abstraction: a folder-based, database-free subject-matter pack (a manifest, per-stage prompt facets, keyword-routed references, report templates, and optional compute) auto-selected per (client, dataset) by deterministic schema matching and spliced as a cross-cutting concern into every stage of an agentic pipeline, the schema explorer, and the report engines, degrading to a strict no-op when absent. Because a skill is a self-contained folder resolved deterministically, the catalogue is open-ended: an extensible marketplace of domain experts. Second, an offline knowledge-compilation loop: an agent probes the dataset's parquet via DuckDB (zero load on production), runs critic-gated per-table convergence with self-healing retries, and data-validates joins by value overlap, producing durable schema knowledge that drives standing expert reports whose every published metric is re-verified by re-executing its evidence SQL, plus suggested questions that mirror the report agenda. These close a proactive loop: reports surface numbers, the numbers seed questions, and a click launches a verified deep dive, all before the query box is used. We give a formal model and report illustrative single-tenant evidence. We make no user-study or benchmark claims; the contribution is the architecture and its defensibility.
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AI Breakdown
Summary
A research paper proposes a new 'analyst-first' architecture for enterprise analytics that inverts traditional question-first conversational systems by embedding domain-expert 'skills' and verified knowledge compilation. The system aims to proactively generate reports and questions before user input, reducing reliance on pre-formed queries or historical logs. The contribution is architectural and theoretical, with no user-study or benchmark claims.
Market Context
The article may indicate growing enterprise demand for AI-driven analytics automation, which could benefit companies providing enterprise software, data platforms, or AI infrastructure. However, the direct market impact is unclear due to the lack of commercialization, user adoption, or performance benchmarks.
Key Drivers
- Proposed shift from question-first to analyst-first enterprise analytics architectures
- Introduction of domain-expert 'skills' as a self-contained, extensible marketplace concept
- Offline knowledge compilation loop enabling verified, re-executable report metrics
Risks
- No empirical validation (user studies, benchmarks, or multi-tenant deployment evidence)
- Architecture is theoretical and lacks commercialization or scalability evidence
- Uncertainty about adoption potential or integration with existing enterprise systems
Time Horizon
Long Term
Original article published by
arXiv
on September 1, 2026.
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Analysis and insights provided by AnalystMarkets AI.