Unifying Metric Definitions: HENGSHI's Approach to Metric Management

When the same “sales revenue” metric produces different numbers in different reports, the underlying data is not necessarily wrong. More often, the statistical scope, filters, time basis, or calculation method has not been clearly stated, reused, and verified. HENGSHI’s metric management capabilities help teams organize and use metrics more clearly around prepared datasets, fields, and business definitions. Across dashboards, reports, and AI-assisted queries, they help users discuss results using the same business definitions wherever possible. This is not a universal semantic layer detached from the business. It is an analytics capability that must be implemented together with data preparation, access controls, and continuous validation.

1 Why Metric Definitions Fall Out of Control

Common disagreements about business metrics often stem from differences that seem small but materially affect the result: whether tax is included, whether refunds are excluded, whether the order date or payment date is used, or whether new users are included in a denominator. When these rules are scattered across SQL, Excel, and separate reports, business teams struggle to determine where a number came from, while data teams cannot easily decide whether to reuse, revise, or create a metric.

A more reliable approach is to clarify each metric’s name, business meaning, calculation scope, applicable dimensions, and usage boundaries before reusing it deliberately in analytics. Metric management does not promise that every page will automatically display the same number. Its purpose is to reduce repeated definitions and give changes, validation, and communication a clear basis.

2 How HENGSHI Organizes Metrics

2.1 From Atomic Metrics to Business Metrics

In HENGSHI, teams can begin with atomic metrics such as sales revenue, order count, and visitor count, then combine them into business metrics for analysis. A business metric can be configured with dimensions, constraints, time axes, and other information. Some versions also support analytical settings such as path attribution. The exact options depend on the product version, data model, and licensed capabilities in use.

This layered structure makes foundational calculations easier to identify and reuse, while explicit metric definitions carry more complex business logic. For calculations such as repeat-purchase rate, retention rate, and conversion rate, teams should first establish a business-approved metric and then use it in reports or natural-language queries. This avoids rewriting the same logic temporarily in every analytical entry point.

2.2 Building Searchable Shared Understanding with the Metric Marketplace

HENGSHI’s Metric Marketplace can organize metrics by topic, display metric definitions and related information, and support filtering and favorites. Teams can maintain metric catalogs by management topic, business domain, or use case so that people can more easily find and discuss which metrics exist, what they mean, and where they apply.

The Metric Marketplace addresses organization and reuse; it does not replace approval by business owners. Before adding or changing a metric, teams should still identify its owner, applicable scope, calculation prerequisites, and acceptance samples. When business rules change, they should assess the reports, applications, and analytical tasks that use the metric and arrange the necessary validation and communication.

3 Defining Usage Boundaries with Access Controls

Consistent metric definitions do not mean that everyone should see data at the same level of detail. HENGSHI supports permission configuration at the application and connection layers. Depending on the authorization model in use, access can be controlled by user, role, or organizational boundary, with fine-grained row- and column-level data permissions also available. The exact capabilities depend on the deployment version, data connection method, and permission model.

Implementation should begin by specifying who can see which data and in which scenarios. For example, an operations leader might view aggregated management metrics, regional staff might see only their authorized regions, and sensitive fields might have restricted visibility. Once permissions are configured, teams must still test them using different roles rather than infer access outcomes from the mere presence of configuration.

4 Working with Data Agent: Reliable Inputs Before Natural-Language Queries

Data Agent can use prepared datasets, field information, and metric knowledge to assist users with natural-language queries, analytical content, and dashboard creation. Metric management provides clearer business vocabulary and a reusable calculation foundation, but it does not mean that AI will automatically understand every implicit rule.

A more reliable approach is to prepare the data first, add dataset and field descriptions, and establish key metrics. Teams can then analyze questions with business context and verify the returned results. Because generative AI output is nondeterministic, business and data specialists should review results that involve management decisions, sensitive data, or complex calculations rather than treating generated output as a final conclusion.

5 A Practical Implementation Sequence

1. Inventory frequently used metrics, prioritizing definitions that cause substantial cross-functional disagreement and offer high reuse value.
2. Specify each metric’s business definition, data source, filtering scope, time rules, and owner.
3. Configure the foundational definitions in datasets and metrics, then compare them against sample reports or historical results.
4. Organize and describe metrics by business topic in the Metric Marketplace to avoid repeatedly creating synonymous metrics.
5. Configure usage boundaries through application and connection permissions, then test row and column visibility with different roles.
6. Reuse the metrics gradually in reports, dashboards, and Data Agent scenarios. When a definition changes, assess its impact, communicate the change, and validate the results.

6 Boundaries That Must Be Acknowledged

Metric management is not a one-time configuration that remains correct forever. Changes to data sources, business rules, permission models, or product capabilities across versions can all affect how metrics behave. Requirements involving performance, cross-source analysis, or embedded delivery must also be validated against the actual data scale, deployment environment, and business scenario.

HENGSHI is therefore best understood as providing metric management and analytics platform capabilities within an enterprise analytics system. It helps teams organize common definitions more clearly and use them more consistently, while business, data, and IT teams remain responsible for approving definitions, designing permissions, and reviewing results.

Conclusion

Good metric management does not hide complex questions behind technical terminology. It makes business meaning, data scope, and usage boundaries understandable, reusable, and verifiable. By combining atomic and business metrics with the Metric Marketplace, access management, and Data Agent, teams can gradually build a more stable analytical language and bring data discussions back to the underlying business questions.