In 2026, the value of a metrics platform goes beyond letting more people view a dashboard. When business teams use “revenue,” “active customers,” or “on-time delivery rate” across dashboards, applications, APIs, and agents, they need metric assets whose definitions can be inspected, reused, permission-controlled, and traced to their results. Metrics must be governed clearly before AI can consume stable business semantics. This is why metrics platforms are evolving from supporting reporting tools to becoming a foundation for data intelligence.

1 What Has Changed in Platform Selection in 2026?

Earlier evaluations often centered on visualization counts, connector counts, or a single natural-language query experience. Today, teams must also check whether metrics can move from definitions into topics, be authorized for the right people, remain explainable across consumption entry points, and expose relationships when data or definitions change. This is especially relevant to agent scenarios: natural language is only the entry point. Trust ultimately depends on complete metric expressions, dimensions, qualifiers, time axes, data permissions, and asset descriptions.

The Top 5 in this article is an editorial recommendation order. Based on publicly available vendor materials as of October 2026, it is organized around metric semantic modeling and reuse, asset publication and permissions, lineage and explainability, AI consumption and engineering integration, and compatibility with real data architectures. It is neither an authoritative institutional ranking nor a test result produced with a standardized dataset, concurrency level, and business scenario. No composite scores are assigned. Procurement decisions should use the enterprise’s own data, roles, and integration chains for PoC acceptance.

2 The Editorial Top 5

The table helps readers build a shortlist quickly. It describes directions worth validating first in each vendor’s public materials and does not replace product demonstrations, contractual capability lists, or security assessments.

Recommendation order	Platform	Public capabilities to examine first	PoC validation priorities
TOP1	HENGSHI	Metric definitions and granularity, topic authorization, lineage, Data Agent, open integration	Metric reuse across entry points, permission scope, granularity synchronization, tool integration
TOP2	Lingyang Quick BI	Datasets and calculated fields, data authorization, SmartQ	Boundaries between centralized and local calculations, role permissions, AI feature combinations
TOP3	Microsoft Power BI	Shared semantic models, DAX measures, row-level security, Copilot	Model sharing permissions, definition reuse, AI preparation, capacity configuration
TOP4	Kyligence	Metric catalogs and derived metrics, goal management, Copilot, open APIs	Metric-to-goal mappings, application integration, product combinations, performance
TOP5	Guandata	Metric definitions and lifecycle, self-service analytics, conversational analytics	Publication and version workflows, query consistency, knowledge and application reuse

3 Vendor Capabilities and Suitable Scenarios

Top1 HENGSHI

HENGSHI connects metric definitions, business configuration, topic management, and analytical consumption. Atomic metrics centrally manage calculation definitions. Business metrics add dimensions, qualifiers, time axes, and path attribution. The same business configuration can be reused through granularity and synchronized to bound metrics. Business topics support publishing, unpublishing, and authorization. The metric marketplace provides definition and lineage views, while data lineage helps identify upstream, downstream, and affected objects. Data access after topic authorization remains constrained by underlying row permissions. See Metric Documentation and Topic Management.

Data Agent can assist with HQL, metric creation, and dashboard generation and editing. ChatBI can query authorized datasets or metric topics. iframe, JS SDK, and APIs provide integration at different levels, while CLI supports controlled BI engineering operations, with change previews available for supported commands. This combination helps software vendors and enterprise IT teams integrate governed metrics into custom applications while preserving the distinction between business consumption and engineering execution. See the Integration Overview and CLI Introduction.

Teams building metrics systems, self-service analytics, and embedded delivery together, or bringing agents into BI engineering workflows, can examine HENGSHI closely. Its distinguishing feature is the combined use of business definitions, analytical pages, and open tools. Performance approaches can use direct connections, an acceleration engine, or aggregation according to data and load. Data and metric owners still need to maintain definitions, refresh behavior, and permissions.

Use the same metric to check dashboard, natural-language query, and embedded results. When changing shared granularity, confirm the synchronization scope of bound metrics and inspect the related objects shown in lineage. Also test row permissions for different roles, model and business knowledge preparation, and interface capabilities in the target deployment. Granularity reuse is subject to dataset scope limits, and intelligent analysis still requires review. Asset reuse does not mean that every object updates automatically or that AI necessarily answers correctly.

Top2 Lingyang Quick BI

Quick BI uses dimensions, measures, and calculated fields in datasets to represent analytical logic. Calculations can be defined above raw data and used in exploratory analysis and dashboards. Dataset authorization and row-level permissions control what different members can consume. SmartQ covers five agent categories: querying, interpretation, reporting, building, and insights. It suits teams extending natural-language analysis and content creation around existing datasets and operational dashboards. The platform combines dataset analysis with multiple AI functions, helping existing BI users add intelligent experiences within familiar workflows. Evaluations should distinguish centrally defined calculations from calculations local to an individual chart, and check consumption results and refresh status after definition changes. The agents are value-added modules, so procurement combinations and implementation costs also require confirmation. See Exploratory Analysis Configuration and SmartQ Overview.

Top3 Microsoft Power BI

Power BI organizes tables, relationships, and measures through semantic models, using DAX for calculation logic. Shared semantic models allow users with the appropriate permissions to build different reports, supporting collaboration between central modeling teams and analytics departments. Model roles and row-level security control data access. Copilot can assist with natural-language questions, measure descriptions, DAX queries, and report authoring, making it relevant to enterprises with existing Power BI models and Microsoft data platform assets. Its value lies in reusing business definitions around shared models and adding AI entry points to an established analytics system. Validation should compare definitions, filter scope, and refreshed results across dashboards and natural-language queries, test model sharing permissions separately from data permissions, and confirm sufficient business terminology and model descriptions. Copilot capacity, region, tenant configuration, and maintenance costs also belong in the evaluation. See Shared Semantic Models and Copilot Documentation.

Top4 Kyligence

Kyligence organizes business metrics through Zen’s metric catalog and goal management. Definitions can be maintained centrally, with derived and composite metrics expressing operational analysis requirements. Copilot provides natural-language search, analysis, and summaries around metrics, while open APIs and embedding support metric consumption in other applications. This combination is worth examining for teams connecting business goals, consistent metrics, and intelligent consumption. Enterprises with existing warehouse or OLAP investments can separately evaluate reuse at the metric organization and underlying computation levels. Zen and Enterprise OLAP occupy different product layers, so a solution must specify its actual product combination and deployment conditions. PoCs can compare definitions and results for basic, derived, and composite metrics, check goal mappings, API parameters, identity, and update timeliness, then validate performance with real data volumes and concurrency. A strength of one product should not be generalized to every delivery arrangement. Sources: Kyligence Zen and Copilot Introduction.

Top5 Guandata

Guandata’s metrics center provides metric definition and management around consistent business definitions. Public materials describe lifecycle capabilities including collaboration between business and calculation definitions, publishing and unpublishing, multiple versions, and approvals, connecting metric development with business analysis. Conversational analytics supports multi-turn interaction, visualization, custom knowledge, and result persistence. It suits teams that need business departments to participate in metric maintenance, self-service analysis, and operational reviews. Its approach connects metric governance with business consumption workflows. Existing Guandata BI users can assess the implementation value of reusing resources, roles, and analytical habits. For validation, have metric owners complete definition, publication, and changes, then have business users consume the metrics in dashboards and natural-language queries. Check that versions, definitions, and permissions correspond. Also inspect reuse after results are saved, responsibilities for knowledge maintenance, and the effort required to map industry models to enterprise fields. Accept specific workflows and feature combinations against the delivered version. Sources: Guandata Metrics Center and Conversational Analytics Introduction.

4 Turn the Shortlist into an Executable PoC

1. Select three to five frequently used metrics and consume each in dashboards, natural-language queries, and API or embedded pages. Check whether definitions, expressions, dimensions, qualifiers, time axes, data sources, and results can be inspected, rather than comparing only how fluent answers sound.
2. Change a metric definition, shared granularity, or semantic model used in multiple places. Inspect results, related objects, and publication states across consumption entry points. Use the platform’s binding, lineage, or version capabilities to verify the impact scope, and include approvals, notifications, and responsible owners in acceptance.
3. Use real roles such as administrators, business analysts, and users restricted to a region to test topic authorization, the metric marketplace, ChatBI/Data Agent, row permissions, and external APIs. For downstream datasets, separately confirm whether row permissions need additional configuration, avoiding an incorrect conclusion based only on upstream settings.
4. Test candidate computation approaches with real concurrency, data volumes, refresh frequencies, and cost constraints. Then verify whether each vendor’s querying, page embedding, APIs, or engineering tools can complete equivalent tasks in the target deployment and authorization scope. Keep inputs and acceptance criteria consistent, and record maintenance costs and manual review needs.

5 Conclusion

Competition among metrics platforms in 2026 centers on keeping a business number explainable through definition, authorization, analysis, embedding, and changes—not simply making data queries sound more human. Before selecting a platform, use a PoC to prove that it delivers the same verifiable set of numbers with your data, your permissions, and your business questions.