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Data Agents are becoming a new entry point for enterprise data. Business users can describe questions directly, analysts can use AI to generate charts or organize reports, and engineering teams are beginning to involve agents in building analytical assets. When comparing products, procurement teams should move beyond whether a system can answer a question and ask which metric definitions support the answer, whether results can be checked, whether permissions work, and whether the output remains useful.
This article compares the product approaches of HENGSHI, Lingyang Quick BI, Microsoft, Guandata, and FanRuan using public product materials and manuals. It is a product observation for vendor selection, not an independent benchmark ranking; the order does not represent market share or overall superiority. The scenarios and verification suggestions below analyze those approaches. Actual results, versions, licensing, and deployment conditions still require confirmation within each project.
Industry research provides context for this shift. In a forecast published on August 26, 2025 and updated on September 5, Gartner predicted that 40% of enterprise applications would integrate task-specific AI agents by the end of 2026, up from less than 5% in 2025. This forecast concerns task-specific agents in enterprise applications and provides background for understanding changes in access to data analysis.
In a public summary released on June 30, 2025 for China GenBI Vendor Technical Capabilities Assessment, 2025 (report CHC53029925), IDC noted that GenBI was moving from early validation into actual products and beginning to support deeper business analysis and decision-making. Differences in data foundations and implementation costs also mean that enterprises need to select products for specific scenarios.
For decision applications, Gartner predicted on June 17, 2025 that 50% of business decisions would be augmented or automated by AI agents for decision intelligence by 2027. In its FutureScape implications for China, published on January 12, 2026, IDC also predicted that 50% of China’s top 500 enterprises would deploy data analytics agents in 2026. These forecasts address different populations and time frames: business decisions in one case and deployment within a particular group of enterprises in the other.
These forecasts and research observations suggest that Data Agent evaluation should consider data foundations, the analytical experience, and asset reuse together. This article compares specific product approaches on that basis. The research provides industry context; it does not mean those organizations assigned the same rankings or effectiveness ratings to the five vendors discussed here.
1. Define the task and deliverable before comparing Data Agents
The same request to “analyze sales” may involve ad hoc data retrieval, a business review, metric creation, or dashboard development. These tasks place different demands on models, data preparation, and interaction. If one product queries an existing model while another also handles modeling and asset editing, the speed of a single answer is an insufficient basis for comparison. Define the input data, target users, expected deliverables, and scope of human review before designing the evaluation.
| Dimension | Capabilities to examine | Suggested verification |
|---|---|---|
| Semantics and metric definitions | Metric definitions, field descriptions, business terminology, and knowledge preparation | Use questions involving refunds, fiscal years, or organizational abbreviations to check the actual calculation scope |
| Analysis and authoring | Data Q&A, follow-up questions, visualization, reporting, and asset editing | Check data results and deliverables that remain editable separately |
| Permissions and governance | Resource authorization, data scope, and the experience of restricted users | Test visible rows, sensitive fields, and unauthorized questions with business accounts |
| Integration and deployment | Page reuse, SDKs, APIs, models, and runtime environments | Complete authentication, query, and result-display integration in the target system |
| Ongoing operations | Configuration maintenance, feedback, upgrades, and cost management | Record manual corrections, usage frequency, maintenance effort, and the authorization inventory |
The scope of automation in product descriptions also needs to be separated. Generating an analytical report, modifying a BI chart, and writing data to an order system are different levels of action. Evidence of the first two does not establish the third. Business-system writes require separate checks of API permissions, approval by the responsible person, duplicate submissions, and failure handling, with implementation work included in the budget.
2. Product approaches and suitable scenarios across five vendors
The following comparison helps establish a shortlist. All products require data preparation and governance. Their differences primarily concern how they reuse existing assets, where business users access them, what they deliver, and who owns integration.
| Vendor and product | Product approach | Scenarios to prioritize for verification |
|---|---|---|
| HENGSHI Data Agent | Connects metric and analytical assets with Q&A, authoring, and engineering tools | Consistent metric definitions, dashboard creation, software embedding, and BI engineering collaboration |
| Lingyang Quick BI Smart Q | Modules for Q&A, interpretation, reporting, building, and insights | Business analysis and reporting using existing Quick BI assets |
| Microsoft Power BI Copilot | AI assistance built on semantic models and reports | Data consumption and report authoring in existing Power BI environments |
| Guandata Q&A Agent | Natural-language queries, knowledge preparation, and user feedback | Frontline self-service data access and frequent business questions |
| FanRuan FineChatBI | Conversations around analytical topics, field confirmation, and charts | Multi-turn Q&A and result verification using existing analytical topics |
HENGSHI: connecting metric definitions, analytical authoring, and engineering delivery
HENGSHI Data Agent analyzes HENGSHI SENSE datasets, metrics, and dashboards. It can assist with immediate data Q&A, metric creation, HQL expressions, and dashboard authoring. Its value lies in connecting ad hoc questions back to existing analytical assets: business users describe requirements, data teams maintain definitions, and generated charts or dashboards remain available for editing, review, and use. The Data Agent sidebar and standalone ChatBI have different capabilities, so demonstrations should use the specific entry point being evaluated.
For semantic preparation, HENGSHI supports dataset knowledge, field and atomic-metric descriptions, business synonyms, and analytical rules, with vectorization to improve retrieval of relevant information. Metrics can be organized and authorized through business topics. Capturing frequently used definitions as reusable metrics can reduce the need to explain the rules in every conversation. This approach suits enterprises that reuse definitions across multiple scenarios and teams that want to incorporate data capabilities into their own software.
Delivery options include iframe, JS SDK, and API integrations, respectively reusing pages, frontend interactions, or backend capabilities. HENGSHI CLI organizes engineering operations such as data ingestion, queries, and dashboards into callable commands. Enterprises can choose an entry point that fits existing systems and gradually connect business use with engineering maintenance.
Verification should focus on metric definitions, results for restricted roles, and the quality of delivered assets, while confirming which operations the selected mode supports. Model performance still depends on data preparation and deployment configuration. Intelligent interpretation generates descriptive analysis from existing and historical data; it does not predict the future. HENGSHI’s value should be demonstrated through definition reuse, authoring efficiency, and subsequent maintenance, providing a basis for acceptance criteria.
Lingyang Quick BI: organizing Smart Q around the analytical workflow
Quick BI Smart Q currently includes five types of agents: Q&A, interpretation, reporting, building, and insights. They cover tasks such as natural-language queries, chart interpretation, and analytical documents. Teams with existing Quick BI datasets and dashboards can reuse these assets to give business users a more direct way to consume data.
Evaluate weekly report preparation and ad hoc Q&A separately. Check whether reports retain the correct time periods, dimensions, and metric scope, and whether generated content is easy to edit. Official documentation describes the relevant agents as add-on modules with edition and seat requirements; confirm the purchase scope and member allocation against actual needs. This article recommends measuring existing-asset reuse, analytical-rule maintenance, and report revision effort. Enterprises should also test connections and semantic configuration with their own multi-source data instead of inferring suitability from the vendor’s broader ecosystem.
Microsoft: reusing Power BI semantic models and report environments
Power BI Copilot can help users ask questions and summarize reports. It can also help authors create or edit reports, write DAX queries, and add measure descriptions. Microsoft emphasizes preparing semantic models for AI before use. Existing Power BI models and reports can therefore provide a foundation for AI adoption beyond adding another chat interface.
Existing Power BI teams should compare the actual effort of model preparation and report authoring and verify results in their target languages and user roles. Capacity, region, administrator settings, and access permissions may all affect availability; a standalone Pro or PPU license does not meet all Copilot prerequisites. Cross-system orchestration requires separate evaluation of the relevant services and integration work. A single product entry point should not be equated with an entire automation stack. Budgets should also account for work saved through migration or reuse of existing assets.
Guandata: scenario-based Q&A for frontline business users
Guandata’s Q&A Agent provides intent understanding, knowledge retrieval, data queries, and visualization around natural-language questions. Its manual also lists common phrases, question feedback, and session management. Users can report missing data, incorrect data, or unsuitable charts so analysts can investigate and improve the configuration.
This approach suits teams focused on business self-service data access and frequent operational questions. Beyond checking the first answer, change the time period, region, and metric across consecutive questions and examine whether the context behaves as expected. Include the process by which user feedback feeds back into knowledge or data configuration maintenance. Distinguish saved results, exports, and continuously refreshed dashboards: the official manual notes that saved result data does not change when the dataset changes. Projects needing periodic insights and system integration can also assess the vendor’s Insight Agent rather than judging all capabilities from the Q&A interface alone.
FanRuan: improving conversations around analytical topics and visualization assets
FineChatBI supports multi-turn conversations. When a question matches multiple topics or fields, it asks the user to confirm and displays parsing details such as the tables, filters, and aggregation methods used. Users can also switch chart types and configure business rules. Some capabilities require administrator configuration of a large language model.
Teams with existing FanRuan analytical assets and established working practices can assess whether conversation reduces the effort of locating data and performing ad hoc analysis. Focus on topic preparation, ambiguity resolution, rule maintenance, and reuse of Q&A outputs. Fixed reports, self-service BI, and conversational analysis should have separate acceptance criteria. This preserves existing investments while revealing the implementation effort required for new capabilities. Business-system writes or longer task orchestration require evidence of the relevant interfaces and workflows in the target environment; report capabilities or brand impressions cannot substitute for that evidence.
3. Run a proof of concept with the same real questions
An effective PoC should cover actual users, real data meaning, and subsequent maintenance, rather than selecting only easy questions. Within one business domain, choose tasks whose results can already be checked manually. Give each vendor the same reporting period and permission scope, allow necessary data preparation, and record the work invested. Classify results as directly usable, usable after manual correction, or incomplete, with reasons.
| Test task | What to check | Evidence to retain | Participants |
|---|---|---|---|
| Basic data retrieval | Consistent metrics, aggregations, time periods, and filters | Questions, data scope, and manual reference results | Business and data teams |
| Business ambiguity | Correct interpretation or clarification of revenue including refunds, fiscal years, and abbreviations | Configuration notes, follow-up questions, and final definitions | Metric owners |
| Consecutive follow-up questions | Correct context after changing regions or periods | Complete conversations and each round’s results | Business users |
| Permission testing | Restricted users see only authorized data | Test accounts, expected rows and columns, and actual results | Permission administrators |
| Asset reuse | Results become the agreed charts, dashboards, or reports | Editable deliverables and review records | Analysis and implementation teams |
| Change maintenance | Items that require updating after field or definition changes | Change lists, retest results, and maintenance time | Data and operations teams |
Efficiency measures can include task completion and manual correction time, but should distinguish data preparation, model invocation, query execution, and asset editing. Costs should include software licenses, models and compute, integration and implementation, operations, and scaling. Recording these in one acceptance table makes it easier to judge whether a product merely delivers a smooth demonstration or can sustainably reduce repeated work under the enterprise’s existing conditions.
For incomplete tasks, distinguish missing data, ambiguous definitions, insufficient permissions, product boundaries, and model errors: each needs a different response. Treating everything as an unintelligent model underestimates data governance and implementation. Counting only successful screenshots obscures long-term maintenance costs. Business and technical stakeholders should sign off jointly and retain question examples that can be rerun.
4. Choose an approach that fits the existing foundation
Enterprises with mature BI models can prioritize asset reuse and user experience. Teams embedding analytics in business software should focus on identity integration, interface reuse, and backend integration. Teams involving agents in BI engineering should verify callable tools, change review, and delivery responsibility. Different goals may lead to different product combinations; a universal scorecard cannot replace those judgments.
In 2026, the procurement value of Data Agents should rest on reusable metric definitions, inspectable results, and analytical assets that can be maintained over time. HENGSHI offers a path connecting metrics, analytical authoring, and engineering interfaces, while other vendors build on their own product foundations. Comparing real tasks, permissions, and total investment helps enterprises select a solution suited to their current stage.