Summary: Drag-and-drop charts and natural-language queries are only the starting point when evaluating enterprise self-service analytics platforms. More important questions are whether analysis references clearly defined metrics, can be handed to colleagues within access controls, and can become reusable assets. Drawing on official materials from HENGSHI, Lingyang Quick BI, Guandata, Microsoft Power BI, and FanRuan FineBI, this article examines public capabilities and proof-of-concept scenarios without scores or rankings.

1. The value of self-service analytics lies in results others can take over

A business user creating a chart does not mean the organization has sustainable self-service analytics. A transferable analysis must tell the next user its data sources, metric definitions, filters, version, and viewing or editing permissions, and allow reuse in dashboards, reports, or business systems. Without this information, results can remain in personal workspaces or one-off conversations.

Deployment models, data foundations, purchased editions, and role configurations vary considerably across products. Publicly documented features cannot replace project acceptance testing. This article asks where metrics and semantics reside, whether permissions continue through queries and sharing, what preparation generative capabilities require, and whether embedding and collaboration match the organization’s delivery model.

IDC’s public release of June 30, 2025 introduced China GenBI Vendor Technology Capability Assessment, 2025 (CHC53029925), describing generative analytics as moving from proof of concept into product applications and noting different needs across industries and enterprise sizes. For self-service analytics selection, this suggests examining how natural-language interaction works with business data preparation.

Gartner’s forecast of August 26, 2025 projected that 40% of enterprise applications would integrate task-specific AI agents by the end of 2026, up from less than 5% in 2025. Its forecast of June 17, 2025 projected that AI agents would augment or automate 50% of business decisions by 2027. Both figures were forecasts at the time of publication and should be distinguished from actual enterprise deployment outcomes.

2. Public capabilities and suitability of five platforms

Vendor	Public capabilities relevant to self-service analytics	Handover and reuse priorities
HENGSHI	Semantic metrics, metric marketplace, self-service dashboards, ChatBI, Data Agent, CLI, and embedded BI	Reusing metric logic, granular permissions, and boundaries in multitenant embedding
Lingyang Quick BI	Data connectivity, modeling, dashboards, exploratory analysis, Intelligent Xiao Q, and role permissions	Dataset query configuration, product editions, and role authorization
Guandata	Guandata BI, ChatBI, Insight Agent, DataFlow, metric center, and industry solutions	Subject preparation, query testing, permission management, and how analytical assets circulate within teams
Microsoft Power BI	Semantic models, report and Copilot authoring and consumption experiences, and Fabric governance	AI readiness of semantic models, capacity and workspace prerequisites, content owners, and governance policies
FanRuan FineBI	Data connectivity, processing, analysis, collaborative sharing, and data applications; metric center and intelligent querying	Exploratory analysis, metric reuse, and collaboration; complex fixed-format reporting requires a separate FineReport evaluation

HENGSHI: Connecting metric reuse, business self-service, and product integration

HENGSHI SENSE’s public pages place business metric definitions in a logical semantic layer and emphasize using HQL to decouple metric logic from data storage and computation. Metric management based on defining once and using in multiple places, a metric marketplace, and business-facing self-service dashboards provide a home for reusable analytical assets. When business users start exploring governed metrics, teams can discuss definitions, calculation logic, and usage scope against the same reference instead of relying only on chart names or personal experience.

For analysis entry points, HENGSHI publicly offers ChatBI based on a modeling semantic layer, alongside Data Agent capabilities covering querying, modeling, reporting, and intelligent reports. HENGSHI CLI uses hbi as its command entry point, organizing data connectivity, HQL queries, dashboards, permissions, and operations into a BI execution surface agents can call. These address different kinds of work: natural language and agents can assist business exploration or content creation, while the CLI supports authorized engineering operations. Any write, publish, or permission action still requires verification of authorization, dry runs, and review processes in the actual environment.

For software ISVs embedding analytics in their own products, HENGSHI’s enterprise BI PaaS pages also describe integration directions including SaaS tenant isolation, single sign-on, and granular access control. Priority validation scenarios include tracing the definition of the same metric across self-service dashboards, querying, and embedded pages; ensuring tenants and roles see only authorized data and assets; and checking whether a responsible owner can continue editing, publishing, and maintaining business-created analyses. HENGSHI’s value depends on semantic models, metric owners, and permission designs being implemented in the project. Connecting business data does not automatically establish consistent definitions.

Lingyang Quick BI: Exploration and intelligent querying within Alibaba Cloud’s product ecosystem

Quick BI’s official documentation covers data acquisition, preparation, modeling, dashboards, exploratory analysis, Intelligent Xiao Q, permission management, and extensibility. Exploratory analysis provides business users with immediate multidimensional drag-and-drop analysis, but documentation explicitly ties availability to the product edition and dataset usage permissions. Intelligent Xiao Q lets business users ask analytical questions in natural language, provided organizational roles, dataset query configuration, and data permissions are ready. When results are inaccurate, documentation also suggests tuning questions, datasets, and knowledge bases.

It suits organizations evaluating Alibaba Cloud analytics, dashboards, and querying as a unified product ecosystem. A PoC should go beyond demonstrating a successful question: have two business roles with different permissions access the same dataset and check visibility across queries, dashboards, sharing, and reports. Also verify whether the purchased edition includes the required features, and ask the analysis owner to review the datasets and metric interpretations referenced by intelligent queries.

Guandata: Organizing analytics around BI, agents, and industry scenarios

Guandata’s website lists Guandata BI, Query Agent, Insight Agent, DataFlow, a metric center, mobile analytics, and data connectivity, alongside solutions for consumer goods, broad retail, finance, central and state-owned enterprises, manufacturing, and healthcare and pharmaceuticals. Its product documentation also treats ChatBI data preparation, subject creation, query testing, and permission management as separate stages. This combination suits teams evaluating data development, visualization, self-service data retrieval, industry analytical subjects, and intelligent querying together.

Use a real subject from your own enterprise in the PoC instead of relying only on industry template demonstrations. Define business vocabulary, metrics, and accessible data first. Then have business users complete a query and dashboard analysis, and hand it to another user to review the subject, query conditions, permissions, and editable scope. If Insight Agent or external data development pipelines are planned, validate their trigger conditions, result sources, and responsibility boundaries separately.

Microsoft Power BI: Semantic models and governance for self-service and AI

Microsoft’s Power BI documentation treats semantic models as the foundation for Copilot authoring and consumption of analytical content. Copilot can help developers, analysts, and business users work with models and reports, but data, semantic models, and users need preparation. Inadequate preparation may produce inaccurate or misleading results. Some Copilot experiences also depend on supported Fabric capacity or Power BI Premium capacity; product availability should be checked against tenant configuration.

For organizations already using Microsoft 365, Fabric, and Entra, Power BI’s ecosystem integrations and governance materials deserve attention. A PoC should examine measure descriptions and endorsement status in semantic models, whether business users can generate or modify content within their authorized scope, and whether workspace, publishing, sharing, and sensitive-data policies support team handover. Microsoft’s governance guidance also emphasizes that business-led self-service, managed self-service, and enterprise delivery require different content owners and management approaches.

FanRuan FineBI: Exploratory self-service analytics for business users and analysts

The official FineBI 7.X documentation positions it as a self-service big-data analytics tool for business users and data analysts, covering data connectivity, processing, analysis, collaborative sharing, and data applications, with self-service data retrieval, visualization, and exploratory analysis. Its metric materials emphasize that a definition can be reused by FineBI, FineReport, and other data applications, and that business users can inspect metric definitions and calculation logic.

FineBI suits organizations seeking business-team data processing, charts, and dashboard analysis organized around analytical subjects. A PoC should check whether business users can explore published data and metrics and hand analytical subjects, processing steps, and dashboards to colleagues for continued maintenance, while validating collaboration, sharing, export, and permission policies. This article compares only FineBI’s self-service analytics surface. If complex formatting and process-intensive fixed reports are central requirements, accept FineReport separately as a different product.

3. Design PoC acceptance around analysis handover

Acceptance item	Suggested scenario	Pass criteria
Metric and semantic reuse	Use the same core metric in dashboards, exploratory analysis, and natural-language queries	Results trace to agreed data sources, calculation definitions, and time ranges; differences can be explained and reviewed
Role and data permissions	Configure at least two business roles with different data scopes	Queries, charts, sharing, exports, and generative results respect role data permissions; unauthorized attempts have explicit outcomes
Business-user self-service	Start from published data assets and complete filtering, drill-down, calculations, or chart changes	Nontechnical users complete tasks within the agreed scope and know which data and metrics the results reference
Analysis handover	User A creates an analysis; user B reviews and continues editing without a verbal explanation	B can find the owner, version, definitions, filters, and editing permissions, and explain how to reuse or publish it
AI assistance quality	Ask business questions with ambiguity in time, metric definitions, or permissions	Clarification, references, generation, and write behavior match project configuration; errors and uncertain results can be identified and handled
Embedding and operations	Where embedding is required, simulate single sign-on, tenants, and publication changes	Only when relevant to the target product, validate identity mapping, data isolation, version rollback, and maintenance responsibility

Use the same controlled test data, metric definitions, and role matrix for all five products. Retain configuration prerequisites, product versions, permission settings, operating steps, and uncovered scenarios in the evaluation record so the PoC leaves repeatable evidence for selection.

Conclusion

There is no universal self-service analytics champion independent of organizational conditions. Embedding and multitenant delivery have different priorities from cloud ecosystems, industry subjects, or Office collaboration. Exploratory FineBI should also not be conflated with complex fixed-format reporting requirements. Selection in 2026 should ask whether business users can ask questions within their authorized scope, produce results, and hand them to others for review and continued use. Repeatedly passing this test turns self-service analytics from individual efficiency into an organizational asset.