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Natural language lowers the barrier to data use for business users, but it does not automatically eliminate ambiguous metrics, incorrect data scopes, or permission risks. HENGSHI Data Agent brings on-demand data analysis, metric creation, and dashboard generation into day-to-day business work. For its answers to withstand real use, organizations must first prepare their business semantics, data objects, and access boundaries.
Natural Language Is Only the Entry Point
When a business user asks, “Why did sales decline this month?”, the question may contain a sales definition, time range, organizational level, and comparison basis all at once. A model can understand the surface intent, but it cannot decide for the organization whether “sales” includes refunds, which stores can be compared, or who may see transaction details.
HENGSHI’s position is clear: trustworthy data questions do not come from making a question sound more human. They come from making data understandable, computable, and properly authorized. Business semantics, metric definitions, permission boundaries, and human review form the shared foundation for natural-language analytics.
What Data Agent Can Do for Analysis
Data Agent supports on-demand analysis of business data and can also assist with creating metrics and generating dashboards. Business users can start from an analytical question and gradually produce a result suitable for presentation, while data teams can establish frequently used definitions and analytical rules during data preparation.
These capabilities are suited to tasks such as identifying trends, inspecting composition, comparing dimensions, and creating dashboards. They do not make a generated answer equivalent to a factual conclusion. For management decisions, anomaly assessments, or complex calculations across tables, users should return to the metric definition, filters, time range, and actual data scope.
Complete Five Areas of Data Preparation First
Before using Data Agent, organizations should place analytical context alongside the data objects instead of leaving it only in people’s experience.
| Preparation area | What to define | Value |
|---|---|---|
| System prompt | Business role, analytical principles, and answer boundaries | Keeps questions and answers centered on the organization’s analytical tasks |
| Dataset knowledge and analytical rules | Business relationships among tables, available definitions, and decision rules | Reduces ambiguity from identically named fields and business concepts |
| Field and metric descriptions | Meaning, unit, calculation method, and applicable scope | Points users and the system to the same business language |
| Object governance | Hide irrelevant data objects and retain task-oriented objects | Narrows invalid choices and keeps analysis focused |
| Data vectorization | Complete vectorization preparation as required by the product capability | Provides a foundation for dataset knowledge and question interpretation |
The point of this checklist is not to “configure a few more parameters.” An organization must first document reusable business definitions, then let Data Agent work within them. The more complete the data preparation, the closer later conversations can come to what business users actually intend to ask.
Separate the Entry Point, Integration Method, and Responsibility Boundaries
The Data Agent sidebar and ChatBI have different capability boundaries, so teams should choose an entry point for the actual use case. Data Agent capabilities can support analytical collaboration within the product. For data-question scenarios or established ChatBI usage patterns, teams should follow ChatBI’s capabilities and configuration.
An integration should also distinguish among Agent, Workflow, and API modes. Organizations can choose a mode according to where a system is integrated, how workflows need to be orchestrated, and where operational responsibility belongs; the modes should not be treated as a single default path. A data-question bot for enterprise communication tools can be associated with ChatBI data to provide business users with a controlled question-and-answer entry point.
Permissions and Review Determine Whether an Answer Can Be Used
The data-question flow must inherit existing data access controls. HENGSHI can configure access scope at the application or dataset-package layer and combine that scope with permissions at the connection, catalog, table, row, and column levels. Organizations should define who can enter each application, which data objects they can read, and which rows and columns they can see before opening the conversational entry point.
Generative output is nondeterministic. After receiving a result, users should review the metric definition, filters, time basis, and data scope. Important conclusions should also be verified against a dashboard or detailed records. Natural language can accelerate data retrieval and exploration, but it should not replace the business owner’s judgment about the result.
HENGSHI’s View: Prepare First, Then Ask
HENGSHI does not use a model as a substitute for data preparation. The model interprets and organizes the analytical process; the organization still has to define data semantics, set permission boundaries, maintain metric definitions, and review critical conclusions. With that foundation, Data Agent can develop from a convenient conversational entry point into a trustworthy capability for business analysis.