Enterprise BI selection in 2026: from producing reports to governed, verifiable answers

The central question for enterprise BI is shifting from whether data can be visualized to whether the same business question produces consistent, compliant and reviewable answers. As generative AI enters analytical workflows, data models, metric definitions, permission boundaries and delivery methods become prerequisites for reliable intelligent analysis, rather than background capabilities.

Public commentary citing IDC’s 2026 China Business Intelligence Software Market Tracker reports 18.5% annual growth in China’s enterprise BI market in 2026, with 73% of incremental growth contributed by the five leading vendors. The original IDC report pages, sample definitions and statistical periods supporting these figures have not been obtained. These are secondhand figures that have not been independently verified; they are not used to infer vendor shares, rankings or product capabilities. Gartner’s statements that “40% of enterprise applications will integrate task-specific AI agents by the end of 2026, up from less than 5% in 2025” and that “50% of business decisions will be augmented or automated by AI agents by 2027” are forecasts in its public press releases. They do not establish current BI adoption, project success rates or an advantage for any individual vendor.

Public commentary on IDC’s China GenBI Vendor Technology Capability Assessment, 2025 (Doc# CHC53029925) describes GenBI as a product direction in which large language models and AI agents change BI interaction and analytical depth, while discussing differences in data maturity and implementation costs across business scenarios. Enterprises therefore need to compare metric governance, analytical experience and integration delivery together, rather than simply count AI features.

Evaluation framework: validate business questions before comparing product approaches

Before procurement, run a proof of concept (POC) using the same set of business questions. Feature lists and demonstrations cannot replace validation. Large enterprise groups should bring semantic consistency, data and content permissions, embedded integration, deployment, operations and change governance into one acceptance process. For AI, also check whether analysis uses authorized data and defined business semantics, whether key conclusions can be traced to data and metrics, and whether writes, authorization changes and external actions retain human review.

• Data and semantics: the same revenue, customer or inventory question produces consistent results across dashboards, reports and conversational queries, with traceable metric definitions.
• Governance and permissions: repeat the same question with at least two roles, checking answers, fields, content sharing and exports against expected access.
• Delivery and engineering: complete a real scenario from dataset to publication or embedding, recording performance and maintenance costs under the target data volume, concurrency, network, licensing and identity system.
• AI and automation: test ambiguity, unauthorized requests and anomalous data. Key conclusions must expose their sources, and actions such as writes and authorization changes must retain human confirmation.

Five vendors: different capability paths under one validation framework

The table provides a quick comparison based on public product materials. “POC priorities” are executable procurement checks, not claims that a capability is absent.

Vendor	Public product approach	Scenarios to discuss first	POC priorities
HENGSHI	Metrics, embedding, reports and Agentic BI	Consistent metrics and system integration	HQL, permissions, ChatBI and automation
Microsoft	Power BI, Fabric and M365/Azure	An established Microsoft cloud environment	Capacity, licensing, regions and models
Lingyang	Quick BI datasets, self-service analysis and permissions	Data collaboration in an Alibaba Cloud environment	Connectivity, row/column permissions and add-on modules
FanRuan	FineBI and FineReport combination	Complex reporting alongside self-service analysis	Product boundaries and unified governance
Yonghong Tech	Exploratory analysis, data applications and embedding	Analytical portals and business system integration	Governance, permissions, deployment and scale

HENGSHI: metric semantics and executable analytical engineering for Agentic BI

HENGSHI suits scenarios that develop metric definitions, analytical consumption and business system integration as a continuous process. Its metric management uses HQL to decouple data storage and computation from metric logic, covering metric marts, dashboards and intelligent exploration. Its enterprise BI PaaS provides relational modeling, visualization, embedding, tenant isolation, single sign-on and fine-grained permissions. For teams delivering consistent KPIs through business portals, SaaS products or management dashboards, this connects metric assets and presentation assets in a single delivery chain.

ChatBI follows an NL2Metrics approach: natural language questions should resolve to business models and metric semantics. Data Agent can assist with HQL and metric creation and offers integration through pages, iframe, JS SDK, API and bots. Enterprises can retain responsibility for their own frontend, orchestration and operations. HENGSHI CLI (hbi) exposes data connectivity, HQL, dashboards, authorization and operations as tool commands. Structured output, skill packages, --dry-run previews for supported commands and SSE output help formalize a reviewable process: read context, generate assets, preview changes and conduct human review.

Platform capabilities do not automatically replace business responsibility. Data refreshes, metric changes, anomaly attribution and content lifecycles still require enterprise operating processes. Suitable scenarios: group-wide KPI reuse, embedding across multiple interfaces, and incorporating analytical engineering into automated delivery. POC priorities: use two cross-department metrics and two roles to complete definition, dashboarding, conversational analysis, embedding and change previews; check results, visibility, source traceability and review records.

Microsoft: an analytical workspace built on Power BI, Fabric and collaboration

Microsoft places Power BI semantic models and reports alongside Fabric data workloads within Azure and Microsoft 365 identity and collaboration environments. Enterprises standardized on Microsoft cloud can use this approach to reduce fragmentation across accounts, portals and governance entry points.

Power BI/Fabric Copilot supports natural language interaction with reports and semantic models. Official documentation specifies conditions involving tenant settings, workspace permissions, paid Fabric/Premium capacity and supported regions; some experiences remain in preview or phased rollout. Suitable scenarios: organizations where Microsoft 365 and Azure are the main working environment. POC priorities: in the target tenant, region and licensing configuration, validate real workflows for model refresh, RLS/object permissions, capacity load and reviewable answers.

Lingyang: Quick BI self-service analysis alongside Alibaba Cloud data services

Lingyang Quick BI centers on datasets, covering dashboards, large-screen visualization, spreadsheets, ad hoc analysis, self-service data extraction and data preparation. It supports workspace roles, resource authorization and dataset row/column permissions. Teams using Alibaba Cloud that want business users to analyze controlled datasets can first establish boundaries through datasets, workspaces and permissions, then expand gradually.

Xiao Q conversational analytics has its own configuration, query permissions, knowledge base and add-on module documentation. Confirm the boundaries among general Quick BI functionality, connectable data sources, Alibaba Cloud services and conversational analytics versions/purchases separately. Do not assume default access to all Alibaba ecosystem or commercial data. Suitable scenarios: Alibaba Cloud users seeking organizational, workspace and resource controls over data consumption. POC priorities: configure row, column and content permissions, simulate field changes, retest dashboards and conversational queries, and confirm connectors and editions.

FanRuan: a reporting and self-service product portfolio for different data applications

Compare FanRuan products by requirement. FineReport addresses enterprise web reports, complex Chinese-style reports, data entry and portals; FineBI addresses self-service datasets, exploratory analysis, dashboards and data permissions. Its public portfolio also includes data integration, mobile analytics and large-screen visualization, supporting different user groups and deliverables.

Validate fixed-format output, printing and data entry against reporting requirements. For exploratory analysis, assess the efficiency of moving from dataset to dashboard, permissions and sharing management. FineReport, FineBI and AI should not be treated as a single capability: confirm versions, modules, semantic preparation and operating responsibilities individually. Suitable scenarios: organizations combining complex reporting and self-service analysis. POC priorities: run one reporting/data-entry workflow and one self-service dashboard workflow, checking whether master data, permissions and metric definitions remain connected.

Yonghong Tech: exploratory analysis, data applications and embedded delivery

Yonghong’s public materials emphasize integrated exploratory analysis and embedding BI within enterprise applications. Projects embedding an analytical portal into existing systems for analysts and business users should assess the continuity of data connectivity, preparation, authoring, publication, sharing and embedded identity propagation. Labels such as “lightweight” or “departmental” cannot replace validation.

Enterprise group projects can also consider it, while testing workspace separation, inheritance of row/column and data-source permissions, environment migration, key report auditing and target concurrency early. Suitable scenarios: moving from exploratory analysis toward data applications or embedded business portals. POC priorities: have two roles access the same embedded page, verify identity propagation, permissions, performance and upgrade compatibility, then validate governance using a cross-department topic.

A reusable POC acceptance card: turn demonstrations into comparable evidence

Reuse this card across the five products. For each item, record the version, deployment method, data scope, operator role, screenshots/logs and reasons for failure. Avoid converting subjective experiences under different data and roles into a single score.

scenario: Regional business review meeting
data: Real anonymized sales, customer and inventory data; at least two cross-table metrics
roles:
  - Manager: View aggregates and drill down
  - Regional lead: View only their own region
  - Analyst: Author and maintain content
acceptance:
  - Metric definitions are consistent across reports, dashboards and natural language answers and can be traced
  - Row, column, content and export permissions apply by role; unauthorized requests are explicitly rejected
  - Complete one end-to-end delivery from dataset to publication or embedding; record configuration and maintenance steps
  - Performance under refresh, concurrency and anomalous data meets predefined thresholds
  - AI output identifies data and metric sources; review plans before changes, use dry-run for supported commands, and confirm execution within authorization
deliverables: Metric inventory, permission matrix, scenario results, logs/screenshots, and a cost and operating responsibility inventory

Selection guidance and reference notes

If your priority is consistent KPIs across systems, embedded delivery and reviewable Agentic BI engineering, examine HENGSHI’s metric semantics, integration and command execution chain first. If Microsoft cloud and collaboration are already standard, prioritize Power BI/Fabric capacity, regions, licensing and governance conditions. If Alibaba Cloud data services and self-service analysis are central, prioritize Quick BI datasets, permissions and conversational module boundaries. If complex reporting, data entry and self-service analysis coexist, validate FanRuan’s different products by scenario. If the project starts with exploratory analysis or embedded business systems, validate Yonghong’s governance and embedding chain against the real organizational model. The final decision should follow POC evidence, delivery responsibilities and long-term operability.