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Natural language is becoming a new entry point for enterprise data use. But effective AI-assisted analytics is not a matter of passing a question directly to a model. The question must be grounded in well-prepared data, clear business definitions, and the correct access scope. Through a conversational interface, HENGSHI Data Agent helps users analyze business data, create metrics, and generate dashboards. This article explains how organizations can turn natural questions into reliable answers by addressing capability boundaries, data and metric preparation, access management, and implementation steps.
1 Why Data Questions Need a New Interaction Model
Traditional analysis often begins with reports, filters, or ad hoc data requests. Business users need time to understand datasets, fields, and chart configuration, while repeated questions consume analytical capacity that could otherwise be spent on metric definitions and deeper analysis.
Natural-language interaction lowers the barrier to asking questions. Users can begin with business questions such as “How did sales revenue change across regions this week?” or “Which stores saw a significant decline in average order value?” They can then refine the time range, dimensions, filters, or comparison method through follow-up questions. This does not replace dashboards or professional analysis; it provides an entry point closer to everyday language.
2 What HENGSHI Data Agent Can Do
In HENGSHI SENSE, Data Agent uses large-model capabilities to provide a conversational data analysis experience. It can support tasks such as on-demand analysis of business data, metric creation, and visualization or dashboard generation. The available scope depends on the product version, AI service configuration, data preparation, and the permissions granted to the user.
2.1 From a Question to an Analytical Result
When a user asks an analytical question, Data Agent interprets the intent using the data assets, fields, metrics, and related descriptions that the user can access, then generates a corresponding result. For common needs such as sales trends or regional comparisons, the user can continue a multi-turn conversation about time, dimensions, filters, and presentation. The result can serve as the starting point for further analysis rather than a one-time conclusion.
2.2 Establishing Complex Definitions as Reusable Metrics
Complex calculations should not depend primarily on temporary wording in a conversation. For important definitions such as repeat-purchase rate, gross margin, or ROI, a more reliable approach is to define and validate reusable metrics in the data layer, together with their calculation scope, time basis, and business description. When Data Agent gives priority to validated metrics in natural-language queries, it can reduce interpretive differences caused by freely combining fields and help business and data teams align on the same definition.
3 High-Quality Answers Begin with Data and Knowledge Preparation
The quality of Data Agent’s answers is directly related to data preparation. The clearer the dataset names, field types, field and metric descriptions, synonyms, and analytical rules are, the easier it is for the model to understand an organization’s internal terminology and business context. Fields that do not participate in question answering can be hidden or organized as appropriate to reduce interference.
Organizations can also add industry terms, business rules, and analytical constraints through Data Agent prompts and dataset knowledge management. For example, they can specify the start and end dates of a fiscal year or clarify whether GMV includes refunds, discounts, and shipping. In scenarios with many questions and data assets, product capabilities such as data vectorization can improve retrieval and recall of relevant information. These configurations must still be validated against the business.
Practical recommendation: Begin by validating a small number of frequent questions with stable definitions. Organize business terminology, validated metrics, and representative question patterns together, then expand the data scope gradually. This is more likely to produce reliable results than attempting to cover every data asset from the outset.
4 Access Management Is a Prerequisite for Natural-Language Analytics
Natural language does not change an organization’s existing data access boundaries. Before launch, teams should confirm the user’s existing authorization at the connection, dataset, and application layers, then validate query results under real user identities. HENGSHI’s connection permissions and application data permission system can configure data access scopes for different roles. The applicability of row- and column-level permissions depends on the authorization model, deployment configuration, and product version.
Access design should therefore return to verifiable scenarios: which datasets each user can access, which fields are visible to which roles, and what the same application should present to different users. When sensitive data is involved, business, data, and security stakeholders should jointly define authorization rules, test cases, and launch acceptance. Data Agent should use data only within existing permission boundaries; it is not a mechanism for bypassing authorization or replacing enterprise access management.
5 Moving from a Pilot to Stable Use
- Select a scenario: Prioritize business scenarios with relatively clear metric definitions, frequent questions, and results that people can verify.
- Prepare the data: Organize datasets, time fields, dimensions, and metrics; add descriptions and synonyms; and establish complex logic as validated metrics.
- Configure the boundaries: Complete the AI service and Data Agent configuration, check user permissions, and define the data scope available for questions.
- Design test questions: Use real business questions to test common phrasing, ambiguous wording, and multi-turn follow-ups, then cross-check key figures against existing reports or manual calculations.
- Expand gradually: Collect user feedback and continue improving metrics, dataset descriptions, and prompt configuration. Retain human review and publication processes for important conclusions.
If natural-language analytics needs to be embedded in a business system or collaboration environment, teams can choose an integration method supported by their deployment version, such as a native entry point, iframe, JS SDK, ChatBot, or selected enterprise collaboration integrations. Before integration, they should validate identity mapping, permission inheritance, data scope, and interaction experience rather than assume that every environment can reuse the capability directly.
6 Usage Boundaries: AI Assists Analysis but Does Not Replace Judgment
AI output is nondeterministic: the same question may not produce exactly the same expression or result every time. Answers may also diverge from expectations when data is missing, definitions are unclear, permissions are insufficient, or a question is ambiguous. In important management, financial, or risk-control scenarios, teams should use validated metrics and source data as the basis for reviewing key figures, filters, and analytical conclusions.
Data Agent is designed to help users understand and use data more efficiently. It is not intended to modify business systems automatically, execute management decisions automatically, or replace professional judgment. Organizations should incorporate it into existing data management, metric management, and access management processes, then develop an appropriate operating model through limited pilots, continuous improvement, and human validation.
7 Conclusion
The value of conversational analytics is not that AI makes decisions for people, but that more business users can reach trustworthy data more quickly through natural language. HENGSHI Data Agent connects questions, analysis, metrics, and visualization. Data preparation, metric definitions, access boundaries, and result validation determine whether that connection can serve the business reliably over time.