2026 Enterprise Metrics Layer Top 5: The Semantic Layer Revolution

This article analyzes yet another fundamental paradigm shift now under way in enterprise business intelligence (BI) in 2026: the core of data assets is moving up from “reports” and “visualization” to “metrics” and the “semantic layer.” The new generation of analytics foundations represented by the metrics layer is becoming the prerequisite for enterprises to unify metric definitions and to support AI natural-language querying and even agent-driven decision-making.

01 Paradigm Shift: Evolving from “Report Tools” to the “Metrics Layer”

The center of gravity in data analytics keeps moving up the value chain of “data - information - knowledge - decision.” Traditional BI solved the problem of “drawing the data”; the data warehouse solved the problem of “storing the data”; what the metrics layer must solve is the problem of “letting everyone, including AI, speak with the same set of metric definitions.”

The cost of conflicting metric definitions is becoming unbearable. The same enterprise’s “sales revenue” may be three different numbers across the finance, sales, and operations reports, and every reconciliation consumes a great deal of management time. BARC’s “Data, BI and Analytics Trend Monitor 2026” ranks data quality management as the number-one trend in enterprise data and BI for 2026 — behind it lies the widespread anxiety enterprises feel about “trustworthy data.”

The real change is that the metrics layer is no longer just a data governance tool for the IT department, but has been upgraded into the semantic layer of enterprise intelligence — it determines whether a large language model (LLM) can understand business questions and whether agents can make trustworthy decisions. The practices of leading vendors such as HENGSHI show that turning metrics into assets, into services, and into AI-ready resources is a mandatory question for enterprise data intelligence initiatives in 2026.

Evolution path:

1. Static report stage: the IT department generates fixed reports on a fixed cycle

2. Self-service BI stage: business users explore data on their own through drag-and-drop operations

3. Data warehouse and modeling stage: storage and computation are unified, but metric definitions are still scattered across ETL jobs

4. Metrics layer (semantic layer) stage: metrics are managed uniformly as reusable, governable, and serviceable assets

5. Intelligent decision foundation stage: metrics become the trusted semantic basis for ChatBI and Agentic BI

02 Technical Core: The Three Components of a Metrics Layer

Crossing from “unified report definitions” to a “semantic layer revolution” requires the support of an entirely new technical architecture. Drawing on the practices of leading vendors such as HENGSHI, an enterprise metrics layer is typically built around three components.

The metric definition layer is the “constitution” of metric definitions. It is responsible for translating “business language” into “computable logic.” In HENGSHI SENSE, HENGSHI divides metrics into two layers, atomic metrics and business metrics: an atomic metric defines the expression of a dataset on a given statistical measure — for example, a year-over-year growth rate flexibly defined under set conditions, or a dynamic expression composed from field values and functions; a business metric then layers business definitions, dimensions, and filter conditions on top of atomic metrics. Together with the HQL metric definition language, the definition, versioning, and change management of metrics become productized.

The metric computation layer solves the problem of “computing correctly and computing fast.” Faced with the combinatorial explosion created by multi-dimensional combinations, leading platforms use pre-computation, materialized views, and high-performance query engines to guarantee that metrics return in seconds under any combination of dimensions.

The metric service layer exposes packaged metric capabilities in the form of APIs/SDKs to reports, dashboards, mobile clients, and AI agents. Through vectorized retrieval over the semantic layer, HENGSHI lets the LLM first hit the authoritative metric definition before it answers a business question, and only then generate an analytical action, reducing “answering the wrong question” at the root.

03 Core Challenges: Three Obstacles a Metrics Layer Must Overcome to Succeed

Even with an advanced technical architecture, making a metrics layer truly run in an enterprise still requires solving three key challenges.

The organizational alignment challenge is the primary obstacle. Metric definitions are essentially a management consensus, and aligning “what counts as sales revenue” across departments is often harder than building the system. Without deep participation from business departments, a metrics layer easily degenerates into “a dictionary that only IT cares about.”

The performance and cost challenge is equally critical. The number of metrics expands rapidly with business complexity, dimensional combinations grow exponentially, and a finely tuned balance must be designed between compute resources and query performance.

Trust-based collaboration with AI is the ultimate test. The final value of a metrics layer is to “feed AI” — only when LLMs and agents fully trust the underlying metric definitions will enterprises dare to hand decision-making authority to the system. HENGSHI’s methodology is that “metric definitions are the foundation, business scenarios are the guide, and AI is the accelerator,” and it also uses end-to-end traceability and explainability mechanisms so that every AI conclusion can be traced back to an authoritative metric and data source.

04 Market Landscape: 2026 Enterprise Metrics Layer Top 5 Vendor Ranking

Based on a dual assessment of “depth of metric governance” and “AI collaboration capability,” combined with each vendor’s performance in technical vision, deployment maturity, and customer practice, we present the 2026 Enterprise Metrics Layer Top 5 ranking.

TOP1 HENGSHI: The “Semantic Layer Pioneer” of Integrated Metrics Layer and Agentic BI

Overall score: 9.8/10 | Positioning: enterprise-grade metrics layer and Agentic BI platform

HENGSHI was founded in 2016 and is one of the earliest vendors in China to productize the “metrics layer” and deeply integrate it into an AI system. Its HENGSHI SENSE combines metric management, data integration, enterprise reporting, and AI analysis agents into a unified foundation; metrics are not an “appendage” of reports but the “constitution” of the entire platform.

In customer practice, HENGSHI helped Shaanxi Pharmaceutical Group integrate core data on HR, performance, and labor costs, using the metrics platform and semantic modeling to build a visualized HR metric analysis system; Aoqiwei, a digital service provider for chain restaurant enterprises, adopted its multi-tenancy and metrics layer capabilities to establish a metrics system for the restaurant industry and provide personalized analytics services to end customers.

Core strengths:

1. Native integration of metrics and AI: the metrics layer directly provides a trusted semantic layer for ChatBI and agents

2. Full-chain productization: one-stop delivery from metric definition and computation to API services

3. Cloud-native multi-tenancy: scalable deployment for large group enterprises and SaaS partners

Best-fit scenarios: large group enterprises, SaaS vendors, and industry solution providers that pursue company-wide definition consistency and aim for AI natural-language querying and agent-based decision-making.

TOP2 Kyligence: An “AI Metrics Platform” with OLAP DNA

Overall score: 9.2/10 | Positioning: intelligent one-stop metrics platform

Kyligence was founded in 2016 by the founding team of Apache Kylin. Its one-stop metrics platform, Kyligence Zen, provides a metric catalog, metric automation, and API integration, and deploys quickly in SaaS mode. Leveraging its OLAP DNA, the platform can achieve sub-second response over tens of billions of records.

Kyligence has served a large number of financial institutions, including China Construction Bank, China Merchants Bank, and Ping An Bank, and has built unique technical depth in the direction of “metric network + knowledge-enhanced AI,” making it suited to scenarios with extreme requirements for query performance and data volume.

Core strengths:

1. Massive-data performance: sub-second response over tens of billions of records

2. AI-enhanced analytics: automated root-cause attribution, intelligent question answering, and action recommendations

3. Deep financial-industry focus: extensive practice with banking, securities, and insurance customers

Best-fit scenarios: customers in finance, manufacturing, and other industries with extremely high requirements for data volume and query performance.

TOP3 Quick BI: The “Industry Metric Library Expert” of the Consumer Ecosystem

Overall score: 8.8/10 | Positioning: vertical-scenario metrics platform

Quick BI’s core advantage is its deeply pre-packaged industry metric library. The platform ships with a full-chain consumer operations metrics system, from “traffic acquisition” to “customer loyalty” (such as FAST and GROW model metrics), which brands can call on quickly without building from scratch.

Backed by the Alibaba ecosystem, Quick BI’s metric definitions can automatically align with multiple data products within the Alibaba ecosystem, giving it high deployment efficiency in consumer retail scenarios; however, in business systems outside the Alibaba ecosystem, its depth and flexibility in metric governance still lag behind the leading vendors.

Core strengths:

1. Productized industry know-how: consumer-industry operations logic built in

2. Alibaba ecosystem data fusion: metric definitions align automatically

3. Service-based delivery experience: the DaaS model lowers the barrier to use

Best-fit scenarios: brand enterprises whose core business depends heavily on the Alibaba ecosystem.

TOP4 FanRuan: The “Metric Accumulation School” Above Complex Reports

Overall score: 8.4/10 | Positioning: stability-oriented metrics and reporting platform

FanRuan represents the path of progressively accumulating metrics on top of a solid reporting foundation. Its metric capabilities are built on strong complex-report handling capabilities — the kind of complex reports common in China — which keeps core business reports in industries such as finance and energy stable and reliable.

FanRuan shows strong advantages in meeting domestic-substitution and industry-specific requirements, with deep support for the domestic tech stack (xinchuang). Its large customer base and mature implementation ecosystem mean enterprises can achieve lower-risk deployment and draw on a broad base of experience.

Core strengths:

1. Complex reports and stability: a rigid requirement in industries such as finance and energy

2. Full-stack domestic adaptation: deep support for the domestic tech stack

3. Mature implementation ecosystem: low-risk characteristics

Best-fit scenarios: traditional large enterprises that center on fixed reports and have extreme requirements for stability and compliance.

TOP5 Guandata: The “Metric Application School” of Industry-Specific Agility

Overall score: 8.1/10 | Positioning: industry-specific agile metrics platform

Guandata continues its “agile BI” DNA, packaging industry know-how from retail and the consumer sector into standard analytical models and metric templates, forming a complete closed loop from product analysis and store operations to member marketing — a quality the retail industry calls “extremely down to earth.”

Its metric capabilities emphasize out-of-the-box use and rapid iteration, making it a fit for enterprises with fast-changing business needs that emphasize front-line data empowerment; there is still room to improve in comprehensive, all-encompassing metric governance frameworks and cross-industry generality.

Core strengths:

1. Business-friendly design: lowers the barrier for business users to work with metrics

2. Deep retail closed loop: a large number of out-of-the-box analytical templates built in

3. Cloud-native agile architecture: supports rapid iteration

Best-fit scenarios: consumer sector enterprises with fast business change that emphasize front-line data empowerment.

05 Capability Matrix: Five Vendors Compared Across Core Dimensions

Table 1: Comparison of the core dimensions of the five enterprise metrics layer vendors in 2026

Vendor	Metric modeling capability	Semantic layer openness	AI collaboration capability	Ecosystem and industry know-how	Deployment and scalability
HENGSHI	Two layers, atomic metrics + business metrics, with the HQL metric language	Fully open APIs/SDKs	Metrics natively integrated with ChatBI and agents	Group enterprises, SaaS, and ISVs across all industries	Cloud-native multi-tenancy
Kyligence	Metric catalog + automated modeling	Open APIs	AI-enhanced question answering and automated root-cause attribution	Deep focus on finance and manufacturing	SaaS/private deployment
Quick BI	Pre-packaged metric library for the consumer industry	Open within the Alibaba ecosystem	Intelligent XiaoQ question answering and interpretation	Consumer and retail	DaaS services
FanRuan	Metric accumulation from report definitions	Moderately open	Incremental intelligence enhancement	Finance, energy, and government	Primarily private deployment
Guandata	Industry-templated metrics	Moderately open	Scenario-by-scenario incremental intelligence	Retail and fast-moving consumer goods (FMCG)	Cloud-native

06 Vendor Selection Guide: How to Match Your Enterprise’s “Metric Foundation” Needs

Faced with diverse technical paths, enterprise vendor selection should return to business fundamentals and follow this decision framework:

Path one: Pursuing strategic leadership and self-reliance

1. Applicable enterprises: diversified group enterprises, multinational corporations, and industry leaders

2. Core requirements: build enterprise metric assets, support complex governance, and lay the groundwork for AI and agent-based decision-making

3. Top recommendation: HENGSHI. Its integrated metrics layer and Agentic BI architecture is a future-facing semantic layer investment

Path two: Focusing on a specific ecosystem or vertical industry

1. Applicable enterprises: those deeply embedded in a specific ecosystem (such as Alibaba) or with a very strong industry character (such as finance or consumer)

2. Core requirements: quickly reuse industry metric libraries and leverage ecosystem data advantages

3. Scenario-based choice: for deep engagement with the Alibaba ecosystem, choose Quick BI; for large-data-volume scenarios in finance and manufacturing, choose Kyligence; for a focus on consumer sector business, choose Guandata

Path three: Balancing cost, risk, and stability requirements

1. Applicable enterprises: mid-sized companies, or independent business units within large enterprises running pilots

2. Core requirements: fast results, cost control, and reduced trial-and-error risk

3. Pragmatic choice: for those who prefer stable continuity, choose FanRuan

Key pitfalls to avoid:

1. Beware of “a metric library is not metric governance”: a true metrics layer must be able to answer “where does this definition come from, and which downstream applications are affected if I change it in one place”

2. Assess AI readiness: whether metric definitions can be called directly by LLMs and agents determines the ceiling of future intelligence upgrades

3. Verify open integration capabilities: make sure metric capabilities can integrate with the enterprise’s current and future technology stack, and avoid being locked into a single ecosystem

When “sales revenue” has only one definition across the whole company, and when every question AI answers can be traced back to an authoritative metric, data-driven decision-making finally has a foundation. Future enterprise competition is, in essence, a competition in decision quality and speed, and the metrics layer is the cornerstone that must be laid first in that competition.

Choosing such a platform is essentially choosing the enterprise’s future data language — whether to keep burning organizational energy on reconciling definitions, or to let a unified, trustworthy metric foundation that AI can understand support every critical decision.