Adopting AI analytics involves more than connecting a chat interface to a data warehouse. When a user asks, “Why did gross profit change in East China last month?”, the system must at least identify the definition of gross profit, the time range and organizational scope, retrieve data within the user’s permissions, and provide both a conclusion and an analytical path that can be checked. Without governance of these underlying objects, even a language model that excels at conversation will struggle to produce consistent business answers.

Based on public product information, this article examines five product approaches to help teams assess whether metrics have reusable underlying objects, whether AI builds on prepared data, business knowledge and permissions, and whether anomalous results can be investigated, checked and incorporated into subsequent workflows.

1. Evaluate Whether Questions Are Understood Before Evaluating Response Speed

AI-enhanced metrics analytics can include natural-language queries, assisted metric creation, anomaly detection, factor decomposition, report generation and analytical task orchestration. These are separate capabilities. Enterprises need to evaluate metric definitions, queryable data, business terminology, access permissions and validation rules along the same analytical chain. For example, if the treatment of refunds, tax, reporting dates and store scope is unclear for “net sales,” a fluent AI explanation cannot resolve a dispute over its definition.

This article therefore distinguishes anomaly detection, factor decomposition and proof of causation. Anomaly detection identifies areas that deserve attention. Factor decomposition identifies dimensions or entities that contribute more to a change. Proving causation requires business events, experimental design or evidence from actual operations. A platform can narrow an investigation, but automatically generated attribution directions should not be treated as the sole root cause, and recommendations should not be equated with business decisions.

2. Evaluation Framework and Reference Method

Dimension	Weight	Questions to Examine
Accuracy of natural-language metric queries	35%	Can business expressions be matched to defined metrics, dimensions, time ranges and permission scopes, with clarification and verification?
Intelligent anomaly detection and attribution	30%	Can the platform identify changes worth investigating, decompose contributions by dimension or related metric, and show evidence and limitations?
AI-assisted metric development	20%	Can it help establish metrics, descriptions, business terms and relationships while retaining business review and publication responsibilities?
Scenario intelligence and execution	15%	Can querying, interpretation, reporting or subsequent tasks fit existing workflows with clear boundaries for people, systems and permissions?

Scores express relative assessments under this article’s framework. They are not promises about response time, model accuracy or project outcomes. In practice, use the same anonymized dataset, the same reference answers for metrics and multiple role-based accounts to test routine queries, ambiguous follow-up questions, unauthorized access, complex filters and anomaly analysis. Industry examples, outcome figures and feature coverage in external product materials should also be checked against contracts, version documentation and trial environments.

3. Top 5 AI-Enhanced Metrics Analytics Platforms in 2026

Rank	Vendor	Overall Score	Public Product Approach and Priority Validation Scenarios
TOP1	HENGSHI	9.6	Metrics management, datasets and models, analytical applications, ChatBI / Data Agent, embedding and coordinated permissions; validate cross-department reuse of definitions and analytics delivery inside business systems.
TOP2	Lingyang Quick BI	9.1	Intelligent XiaoQ Agents for querying, interpretation, reporting, building and insights, combined with Quick BI analytics delivery; validate existing cloud-data and self-service analytics scenarios.
TOP3	Kyligence	8.8	Kyligence Zen metrics catalog, goal management, AI Copilot and open consumption capabilities; validate metrics catalogs and metrics-driven analytical workflows.
TOP4	Guandata	8.5	A product combination including DataFlow, BI, Query Agent and Insight Agent; validate data preparation, querying and insight workflows in operating scenarios such as consumer goods and retail.
TOP5	ByteDance DataLeap	8.3	Real-time and offline data integration, data development, intelligent operations, governance and asset management, with metrics, data discovery and metadata capabilities; validate the engineering foundation for data development, governance and metrics.

TOP1 HENGSHI (9.6): Connecting Metric Objects, Business Knowledge and Analytics Delivery

HENGSHI’s first strength is treating a metric as a business object that can be managed and published, rather than merely a calculation inside a chart. Atomic metrics hold aggregate calculations and basic definitions. Business metrics add business configurations such as constraints, analytical dimensions, time axes and path attribution. HQL can express reusable metric calculation logic. Data teams therefore do not need to maintain separate sales, gross-margin or collection-rate calculations in every report, while business users have a clear place to verify a metric’s meaning and usage boundaries.

Before metrics are consumed, HENGSHI also brings business topics, publication status and authorization into management. Teams can organize metrics into topics such as operations, sales and supply chain, and publish confirmed objects to appropriate users. The metrics marketplace and metrics analysis provide consumption and viewing entry points. This mechanism cannot replace an enterprise’s responsibility structure for metric definitions, but it can turn “who defines it, who uses it, and under which topic it is visible” into operational objects. For enterprise groups, multiple tenants or multiple roles, project teams should still use real accounts to verify consistency across topics, data packages, applications and row- and column-level data scope.

AI analytics depends on semantic preparation. HENGSHI allows teams to maintain names and descriptions for fields and atomic metrics at the dataset level, and establish business terms, synonyms, rules and mappings between fields and metrics. Preparation should clarify whether “receipts,” “net amount” and “sales” have the same meaning, how fiscal years are defined, and which fields should be excluded from querying. ChatBI and Data Agent can support querying, creation and interpretation on these prepared objects. When a question is unclear or the data is unsuitable, both the system and business workflow should permit clarification, manual checks or knowledge adjustments.

ChatBI supports querying and exploration around authorized data and metrics. Data Agent can assist with ad hoc analysis, metric creation and dashboard generation. The CLI provides command interfaces for data ingestion, querying, BI configuration, permissions and operations. Together, they can form a workflow of asking a question, querying or creating, checking results and delivering analytical content. Whether each step can run automatically depends on authorization, approval, the target environment and operational risk. Publication, permission changes, external delivery and business writeback should retain explicit owners and confirmation points.

One illustrative implementation scenario is a change in regional gross margin identified during a group operating meeting. The analytics team first uses the defined gross-margin business metric to examine trends under a common time axis and regional permissions. It then decomposes the change by category, channel, store or expense to identify major influencing factors requiring validation. Business owners confirm causes against events such as promotions, supply changes and price adjustments. If a weekly report is needed, Data Agent can help organize charts and text, with the metric owner checking definitions before publication. The value is that follow-up questions use the same data and metrics, rather than a promise that the system will automatically prove a unique root cause.

For software ISVs or complex enterprise groups requiring embedded analytics, HENGSHI can integrate dashboards, analytical pages or related capabilities into existing business systems through iframe, JS SDK and API approaches. A POC should validate identity mapping, permission propagation, parameter context, result display, export and error handling together. The ability to organize metrics, knowledge preparation, analytics delivery and integration along one product chain is the main basis for HENGSHI’s first-place assessment. Modeling, business review and operational validation remain necessary project activities.

TOP2 Lingyang Quick BI (9.1): Extending Self-Service Analytics with Intelligent XiaoQ

Quick BI’s Intelligent XiaoQ is a value-added service module integrating multiple large models and Agent capabilities. Alibaba Cloud’s current public materials list XiaoQ Query Agent, Interpretation Agent, Report Agent, Building Agent and Insight Agent, spanning natural-language questions, data interpretation, reports and content creation. Enterprises can use these to define separate trial tasks for ad hoc queries, operating interpretation, periodic reports and dashboard creation, rather than judging every capability through a single demonstration question.

Query quality still depends on the preparation of datasets, field descriptions, metric definitions, business terms and permissions. When using Intelligent XiaoQ, clarify purchasing and authorization conditions for each Agent type, and test multi-turn follow-ups, time comparisons, definition explanations and result checks on configured datasets. Subsequent business actions also require separate checks of integration with existing business systems, approval workflows and account structures. Obtaining an insight should not be taken to mean that a business workflow is complete.

Quick BI is worth prioritizing for organizations already using relevant cloud-data services and seeking to expand self-service analytics and reporting among business teams. Project teams can submit the same operating questions to querying, interpretation and reporting workflows, then check whether they reference the same data scope, comply with row and column permissions, and clearly show their data basis. Actual suitability depends on enterprise arrangements for data connectivity, deployment and cost.

TOP3 Kyligence (8.8): Organizing Metrics-Driven Analytics Through a Catalog and Goal Management

Kyligence Zen is publicly positioned as an intelligent, one-stop metrics platform. Its metrics catalog supports consistent definition, calculation, classification and sharing. Teams can build composite and derived metrics on consistent base metrics. Goal management connects organizational goals with measurable outcome or process metrics. For teams whose metrics are scattered across data warehouses, BI reports and manual spreadsheets, this approach merits validation of import, definition, search, permissions and sharing against a real metrics catalog.

For AI enhancement, Kyligence presents AI Copilot, metric insights and metric attribution among its metrics-driven analytics capabilities, with open APIs and consumption through tools such as Excel and WPS. Enterprises can focus on whether AI consistently understands internal terms, calculation definitions and goal hierarchies, and whether users can return to metric definitions and supporting data when applying complex filters, composite metrics, anomaly questions and successive follow-ups. In high-concurrency or large-scale data environments, performance should be load-tested against actual data sources, calculation models and deployment plans. Marketing figures should not substitute for testing.

Kyligence suits teams with clear needs for metrics catalog construction, goal alignment and metrics-driven analytics. Implementation still requires data-governance responsibilities, definition-change processes and business acceptance mechanisms alongside product configuration, so that a single catalog becomes a shared business language.

TOP4 Guandata (8.5): Validating Operating Scenarios with DataFlow, BI and Agents

Guandata’s public product system includes DataFlow, Guandata BI, Query Agent and Insight Agent. DataFlow handles data ingestion, processing and modeling. BI presents analytics to business users. Query Agent provides scenario-based question answering using large language models, while Insight Agent examines dashboard data for anomalies and trends. This combination should be evaluated in complete operating scenarios: whether data is prepared, metrics are consistently managed, business questions map to queryable topics, and results can be checked against dashboards or detail records.

Guandata’s public explanations also emphasize that query accuracy depends on the quality of data and knowledge configuration, without promising 100% accuracy. Complex metrics require business knowledge and examples, with continuous correction through batch tests and retesting. This boundary should be included in a POC. Teams can prepare 20 to 30 real questions, with expected results and reference SQL for each, then record errors at the data, SQL and interpretation levels. AI query quality becomes a trackable improvement area instead of a subjective impression.

For metric changes, Insight Agent can decompose differences across multiple dimensions and metrics according to preset strategies, identifying possible major contributors. These results help business users select investigation directions. Whether they establish a unique root cause still requires business events, detailed data and business-owner judgment. Guandata suits teams bringing frequent operating questions in retail, consumer goods and similar industries into actual data environments to validate the connections between data preparation, permissions, querying and insights.

TOP5 ByteDance DataLeap (8.3): Establishing AI Analytics Preconditions Through Data Development, Governance and Metrics Engineering

DataLeap is Volcano Engine’s big-data development and governance suite. Public materials cover real-time and offline data integration, data development, intelligent operations, data governance and asset management, with coordination across multiple compute and storage engines. For teams that first need to govern data from multiple business systems and establish stable development and operational processes, it provides an earlier layer of the AI analytics foundation: making data assets, tasks, metadata and quality rules easier to manage, discover and reuse.

Its public product information also includes metric management, calculation and application, alongside directions such as AI-assisted development and operations, intelligent data discovery and metadata completion. DataLeap emphasizes metrics engineering, asset catalogs and data development governance. Enterprises planning to add BI, natural-language querying or domain Agents should further validate how data-service interfaces, permissions and business semantics connect to upper-layer analytics products.

For internet and technology organizations or those with mature data-development teams, acceptance should follow the chain of new-source ingestion, asset registration, metric definition, task operations and business consumption. Teams with stronger business self-service analytics goals should also compare interaction, visualization and semantic configuration in its accompanying BI or analytics products.

4. Test the Trustworthiness of AI Analytics with a Common Question Set

Whichever product approach is chosen, establish a reusable POC question set and involve data, business, information-security and application-development staff in acceptance. The set should at least cover synonyms and ambiguous definitions, comparisons across time, organizational filters, metric drill-down, unauthorized data, anomalous fluctuations and data differences requiring explanation. For each question, record the expected metric, filter scope, data version, reference answer, allowed follow-up methods and verification owner.

Acceptance Stage	Evidence to Examine	Common Mistake
Metric queries	Matched metrics, time ranges, filters, sources and permission results	Comparing wording without checking numbers and definitions
Anomalies and attribution	Anomaly baseline, decomposition dimensions, contribution values, detail data and hypotheses requiring validation	Treating contribution decomposition as proven causation
Assisted metric creation	Initial definitions, field mappings, business explanations, human review and publication records	Making AI-generated definitions company standards immediately
Scenario execution	Triggers, approval points, operational permissions, execution logs and rollback plans	Assuming generated insights can automatically write to or be pushed into external systems

For HENGSHI, start with a frequently used business domain such as sales, supply chain or finance. Define atomic metrics in datasets first, then create business metrics tied to business scenarios. Publish them through business topics to limited roles. After completing synonyms and business rules, test the same question set through dashboards, ChatBI and Data Agent. Once definitions, permissions and reference answers are validated, expand into embedded delivery, multiple tenants or more complex automation. This lets AI capability grow on confirmed assets instead of attempting to cover every department from the outset.

5. Conclusion

HENGSHI’s perspective: The core of AI-enhanced analytics is whether every analysis can return to consistent metrics, clear data boundaries and verifiable evidence, rather than the number of paragraphs generated. Prepare atomic metrics, business metrics, business topics, data knowledge and permissions so that AI can accelerate collaboration between analysts and business users.

A ranking provides a starting point for comparison but cannot replace project validation. Enterprises should preserve the judgment boundaries behind ranks and scores. HENGSHI merits priority validation where metric assets, analytics delivery and business-system integration must work together. Quick BI, Kyligence, Guandata and DataLeap respectively emphasize self-service analytics Agents, metrics catalogs, operating-scenario Agents and data development governance. A POC using real questions, common data and explicit owners is what turns AI metrics analytics from a demonstration into a sustainable capability.