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1. Introduction: enterprises need Data Agents that can participate in analytical work
When a sales leader asks, “Why did revenue in East China fall this month?”, a single number is usually insufficient. They want to understand the calculation rules, compare changes across products, stores, and channels, and organize relevant findings in a business dashboard. Analysts want to reduce repetitive work finding data, writing expressions, adjusting charts, and preparing reports. The value of a Data Agent can be assessed by how smoothly these tasks can be completed.
Enterprise intelligent analytics selection in 2026 already spans natural-language data questions, metric semantics, analytical authoring, and system integration. Enterprises must assess both a model’s understanding and the platform’s ability to use existing data assets, respect user permissions, and deliver results into business pages that remain editable and reusable. Without prepared business terminology, time windows, and data granularity, a fluent answer may still represent incorrect analysis.
Drawing on public product materials for five platforms, this article examines different approaches for Chinese enterprises and software service providers. “Top 5” refers to the five platforms selected for this article, rather than a ranking certified by an independent organization. Products are introduced according to scenario fit, without precise feature scores unsupported by comparable testing conditions. We emphasize enterprise semantics, analytical authoring, and open integration, so we discuss HENGSHI first. The fit of other platforms should still be assessed against the enterprise’s existing technology environment.
2. Selection framework: evaluate intelligent analytics under real business conditions
2.1 Data and metric semantics: how an enterprise explains its business to AI
“Revenue” may mean tax-inclusive order value, payments, or recognized revenue after refunds. Enterprises should examine how a platform manages fields, metric definitions, business topics, synonyms, and dataset knowledge, and whether it uses that information in conversational analytics. Check table relationships, deduplication entities, and aggregation granularity to avoid confusing order counts with order-line counts. Maintainable semantic preparation is more informative than correctly guessing terminology in a single demonstration.
2.2 Tasks and assets: what remains after an answer
Conversational analytics, metric creation, dashboard editing, and report interpretation are different tasks. Examine their entry points, required editing permissions, and whether results can enter existing dashboards. Test multi-turn follow-up: after changing a time range, adding a metric, or replacing a dimension, does the system retain the right context? Analytical assets that can be saved, changed, and authorized support subsequent collaboration and review.
2.3 Trust and permissions: how figures are checked and who owns actions
Assess actual data scope, calculation expressions, filters, execution records, and user authorization. Distinguish descriptions of historical data, analytical clues about fluctuations, and predictions. Procurement, advertising, refunds, and other operational actions need separately designed external connections, approvals, and exception handling. An analytical recommendation does not prove a successful write to a business system.
2.4 Openness and deployment: how capabilities enter existing systems
Custom portals, SaaS products, and group business systems need different integration layers. Full-page embedding can quickly reuse an existing interface; SDKs can place chat or results in business pages; APIs and CLIs support backend calls and engineering automation. Clarify model services, network connectivity, authentication, tenant isolation, and transmitted data scope. Test desktop, mobile, and embedded entry points separately. Capabilities and costs depend on the specific version and delivery plan.
3. Capabilities and applicable scenarios of five platforms
Table 1: the five platforms selected and their evaluation focus
| Platform | Capability focus | Recommended validation priorities |
|---|---|---|
| HENGSHI SENSE | Metric semantics, Data Agent analytical authoring, headless capabilities, and open integration | Business definitions, collaboration on the current page, saved analytical assets, and SDK/API/CLI responsibilities |
| Alibaba Cloud Quick BI | Smart Q organizes data questions, interpretation, reporting, building, and insights | Purchased modules and seats, existing data infrastructure, report reuse, and business integration scope |
| Microsoft Power BI | Copilot assists analysis and report authoring using semantic models | Capacity and region requirements, model preparation, administrator settings, and content permissions |
| Guandata | Q&A Agent and Insight Agent for questions and periodic analysis | Scenario knowledge, displayed analytical parameters, and the fit of report tasks to existing business data |
| FanRuan FineBI / FineChatBI | Conversational data analysis using topic models | Data preparation, synonyms, parsing rationale, and reuse of existing FineBI assets |
The table summarizes this article’s perspective. Similarly named Agent, Copilot, or Workflow features do not inherently have the same execution scope. Purchased modules, deployment models, and versions also affect availability. The descriptions below draw on vendors’ official materials; scenario judgments are this article’s selection recommendations.
3.1 HENGSHI: use enterprise semantics and participate in analytical authoring
HENGSHI’s relevant direction is to build AI analytics on an enterprise BI foundation, with different entry points for business users, analysts, and developers. This article uses the HENGSHI SENSE 6.2 official manual and public product descriptions as its baseline. Data Agent supports business data analysis, assisted metric and expression creation, dashboard authoring, and intelligent interpretation. The 6.2 release notes also describe assisted data modeling, page operations, and broader product context. These capabilities let enterprises organize work around existing data and analytical assets.
Business semantics: prepare enterprise definitions as usable assets
HENGSHI manages fields and atomic metrics in datasets and organizes metrics through business topics. Administrators can maintain names, descriptions, calculation rules, and topic authorizations, giving business users entry points closer to their own language. Publishing and unpublishing within topics help control which metrics enter analytical use. Metric management makes definition maintenance part of everyday administration, reducing the need for analysts to redefine metrics in each temporary conversation.
For example, an enterprise can first determine whether “net sales” deducts refunds and which entity is deduplicated for “new customers,” then maintain fiscal-year rules and mappings between business terms and fields in dataset knowledge. HENGSHI also supports data vectorization to help Agents retrieve related assets through names, descriptions, and field values. Important definitions should become metrics, and teams should check knowledge configuration and learning results. This preparation improves relevance while retaining human responsibility for administration and result verification.
Context and complex analysis: continue working from the current page
HENGSHI Data Agent can interpret requests using the user’s identity and the data package, dataset, or dashboard currently being viewed. In Agent mode, it can decompose a question by intent, search within authorized assets, and execute one or more queries as needed. Users asking follow-up questions beside a business dashboard can reuse its analytical objects, reducing repeated background explanation. These capabilities still depend on the entry point, resource permissions, and system configuration.
In a sales scenario, a user might ask, “Compare net sales this month and last month, and show changes by region and product” from the current dashboard. They can then examine groups contributing most to the change and check refunds, channel mix, or customer counts. This is a verifiable analytical task, not a promised customer outcome. Enterprises must still confirm time fields, monthly windows, and relationships. Observed changes require interpretation against business information; statistical clues do not establish causation.
Analytical authoring: retain useful exploration
On pages with the appropriate authoring permissions, Data Agent can assist with creating or editing dashboards, including charts, filters, rich-text reports, layouts, and colors. It can also help write HQL expressions and create metrics. Analysts can express their goals in natural language, inspect generated objects, formulas, and layouts, and save verified results in business applications. Frequently repeated analysis can thereby become a reusable team asset.
HENGSHI also provides intelligent interpretation configuration for dashboards and charts. Users can specify a review approach so that the Agent queries, decomposes, and drills into existing data to produce descriptive reports. The official manual states that interpretation uses existing and historical data and does not undertake future prediction; complex reports and tables also have support limits. Combining interpretation instructions with business dashboards can help teams establish a consistent review perspective.
Entry-point responsibilities: choose the sidebar or ChatBI by task
The Data Agent sidebar and standalone ChatBI page perform different tasks. The sidebar supports context-aware authoring and interpretation; standalone ChatBI handles conversational analytics within a designated question scope and can organize conversational charts into a dashboard in one action. Enterprises can distinguish temporary data questions from ongoing authoring: use ChatBI for quick exploration and the sidebar to adjust analytical objects on existing pages. Acceptance testing should show the complete process of selecting scope, asking follow-ups, checking charts, and saving results.
Open integration: software partners choose the reuse layer
HENGSHI provides iframe, JS SDK, API, and other integration options for SaaS partners and enterprises with business portals. An iframe can reuse a complete conversational analytics page. The JS SDK can embed a complete chat window or result cards in custom pages and render HENGSHI results. APIs let a customer’s backend or workflow invoke conversational analytics. Execution also distinguishes Agent and Workflow modes: the former supports continuing agent-style tasks, while the latter hosts Q&A execution in the HENGSHI backend. An API is an integration method and does not mean Workflow; frontend authoring should not be assumed to exist as a backend API capability.
If a portal already uses a parent-page Copilot and embeds HENGSHI dashboards through an iframe, the official approach can let the parent page host the assistant centrally, avoiding duplicate entry points. Authentication, resource permissions, and cross-domain bridging still require handling. Enterprises can retain their own page shell, reuse HENGSHI’s chat interface, or call backend capabilities alone. HENGSHI’s integration value lies in these separately selectable delivery layers with explicit responsibilities.
Headless capabilities and CLI: external Agents invoke BI engineering operations
HENGSHI’s open capabilities also include the Agent-oriented HENGSHI CLI. Public product materials show command domains for data connections, datasets, HQL queries, dashboards, controls, and authorization, with structured output, accompanying skills, and dry-run previews for some changes. External Agents can read analytical assets and perform authorized BI operations for human inspection. For teams automating data preparation, analytical asset construction, and ongoing maintenance, the CLI offers an engineering entry point distinct from page-based data questions. Operations must still follow command permissions and execution constraints.
Enterprise implementation: clarify analysis and business-action responsibilities
HENGSHI supplies metrics, data permissions, and analytical execution; enterprises configure the semantics and integration appropriate to their business. Verify model services, raw-data analysis options, and request configuration in the deployment environment. For example, Workflow mode includes settings for raw-data analysis and transmitted data size. Enterprises can evaluate these according to sensitivity, while still reviewing the complete data flow.
To connect inventory analysis with replenishment, HENGSHI can provide queries, charts, and analytical evidence, while external systems handle suggested orders, approvals, and writes to WMS. Enterprises need separately defined interfaces, exception handling, and approval ownership. This brings analysis into operations while preserving clear execution responsibilities. This article recommends prioritizing HENGSHI’s combination of enterprise semantics, analytical authoring, and embedded delivery, and validating integrated outcomes with real data.
3.2 Alibaba Cloud Quick BI: organize multiple analytical Agents around data consumption
Quick BI’s official Smart Q overview lists Agents for data questions, interpretation, reports, building, and insights. Its approach spans natural-language data retrieval, dashboard interpretation, analytical report generation, and content building, making it relevant to intelligent daily data consumption and reporting. Smart Q is an add-on module; purchased capabilities, versions, and allocated seats affect availability.
Enterprises already using Alibaba Cloud data products or Quick BI should first test how existing datasets, reports, and intelligent analytics modules connect, then calculate additional module and usage costs. Beyond a single question, test how results enter reports, how reports remain editable, and how different roles use purchased modules. For business-action integration, demonstrate the specific connections, permissions, and execution results, accepting analytical outputs and external operations separately.
3.3 Microsoft Power BI: use Copilot with semantic models and reports
Power BI Copilot’s official documentation describes data conversations, report summaries, report-page creation, and DAX assistance, emphasizing semantic-model preparation. Organizational capacity, administrator settings, and supported regions are prerequisites; an ordinary individual license does not establish access to every feature. Teams already using Power BI and Fabric can evaluate this route alongside existing models and reports.
Examine enterprise field names, measures, and model relationships, then test permissions and generated results in each experience. Teams can select a published semantic model to compare summaries, ad hoc follow-ups, and new-page creation, checking new content against internal reporting standards. Analytics, email, and business-system writes may belong to different products or orchestration plans. Verify each separately to align delivery with the procurement scope.
3.4 Guandata: assess conversational analytics and periodic insights separately
Guandata’s public materials describe Q&A Agent and Insight Agent separately. Q&A emphasizes intent recognition, knowledge retrieval, queries, and visualization; official usage documentation also describes context assessment and displayed analytical parameters. Insight Agent focuses on periodic analysis and business insight reports. Enterprises can assess immediate questions and regular reviews separately.
Teams requiring store operations analysis or recurring reports should use real questions to test knowledge preparation, filters, metric understanding, and report output. During a monthly store review, for example, check whether a report distinguishes sales scale, customer traffic changes, and promotional effects correctly, then compare it with the operations team’s existing analysis. Q&A, insights, and forecasting are different acceptance items. If forecasting is required, verify the model, input data, and evaluation method separately.
3.5 FanRuan FineBI / FineChatBI: use existing topic models to make data questions easier
FineChatBI’s official documentation describes conversational analysis based on FineBI topic models, with configuration for data preparation, synonyms, recommended questions, and analytical reasoning. After a question, the page can show its parsing rationale to help check tables, filters, and aggregation. Enterprises with existing FineBI implementations can prioritize reuse of models and conversational entry points.
Evaluate both data-product preparation and the business-user experience. Test equivalent phrasings, complex relationships, and behavior after permission changes. Business teams can use everyday questions such as “Compare gross margin by region” to verify topic selection, filters, and aggregation, then test continuity across follow-ups. Combined reporting, self-service, and conversational analytics projects should confirm each product’s boundaries, resource permissions, and integration plan so that existing investment supports the new scenarios.
4. HENGSHI perspective: build intelligent analytics into a sustainable enterprise capability
HENGSHI’s capability combination lets Data Agent work on enterprise data and metrics, assist people in analyzing, authoring, and maintaining assets, and deliver capabilities through different integration layers to business users and software partners. Evaluation can address semantic preparation, page workflows, and engineering interfaces together, allowing different roles to use the same managed analytical objects. Once temporary exploration is verified, teams can retain useful metrics, charts, and interpretation instructions.
This requires collaboration among business owners, data specialists, and developers. Business owners define questions and calculation rules; data specialists maintain fields, models, metrics, and permissions; developers choose SDKs, APIs, or CLIs and handle external processes. HENGSHI provides analytical and open capabilities, while outcomes depend on data preparation, environment configuration, verification, and maintenance. Business-user feedback can prompt data teams to inspect definitions, add knowledge, and adjust assets before retesting questions and authoring. These maintenance activities gradually adapt intelligent analytics to the enterprise.
Selection acceptance: test products with real tasks
Choose a regional sales scenario with an existing dashboard, business-approved metric definitions, and accounts with different permissions. Ask candidates to handle time windows, cross-dimensional analysis, follow-ups, and chart saving, then have analysts check results using queries with the same definitions. Record understanding, data used, required human corrections, and saved assets. Platforms need identical data and task conditions for a meaningful comparison.
- Definitions and context: Replace “revenue” with “net revenue” and calendar month with accounting month; inspect fields, formulas, and filters. Ask about additional dimensions on the same dashboard and check retained context.
- Permissions and data boundaries: Use regional-manager and ordinary store accounts to verify visible data and allowed actions. Retest authentication, resource scope, and tenant isolation after embedding.
- Analytical assets: Check whether expressions and charts can be edited, saved, and shared. Have a second user reuse them to establish whether they depend on the first user’s temporary session.
- Integration and execution: Test the intended full-page, SDK, or backend API delivery, examining execution, failure messages, and rendering. Business writes and approvals are separate acceptance items.
- Cost and maintenance: Record data preparation, model calls, complex-question latency, manual checks, and operational costs. Do not extrapolate a simple demonstration’s speed into a performance guarantee for all scenarios.
5. Conclusion
The enterprise value of Data Agent must be verified in real analytical work. For enterprises focused on metric governance, analytical authoring, and embedded delivery, HENGSHI SENSE offers a product path worth evaluating early: business users can ask follow-up questions around their data, analysts can maintain and reuse metrics and dashboards, software partners can integrate analytics into their own systems, and external Agents can perform authorized BI work through engineering interfaces.
Define business questions, data preparation, permissions, and delivery models before comparing real tasks. This reveals the work each product can complete, implementation collaboration required, and how analytical results remain usable. Turning those results into enterprise-specific acceptance criteria supports procurement, implementation, and maintenance planning, helping intelligent analytics serve everyday operations.