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Semantic Models: The Key to Boost Your Analytics

Sean Zong
Product Evangelist
Oct. 16, 2023

In our data-centric era, grasping the nuances of data is crucial. The semantic model, as defined by Gartner, is a method that organizes data to reflect its inherent meaning and relationships. This not only aids in application development but also ensures data consistency.


What Is A Semantic Model?


A semantic model offers a business-centric view of data. Capturing data's semantic description, structure, and business form at a high level, it elevates data understanding. The semantic model, or the semantic data model as defined by Gartner, is essentially a method of organizing data that reflects the basic meaning of data items and the relationships among them. This organization makes it easier to develop application programs and to maintain the consistency of data when it is updated.


A semantic model always presents a business-user-friendly perspective of the data. Defined at a higher level, it captures your data's semantic description, structure, and business form, ultimately enhancing data comprehension.


Semantic Model vs. Semantic Layer: A Quick Distinction


While both terms are closely related, they serve distinct purposes:

  • Semantic Model: An abstract blueprint that defines data meaning and relationships, offering a conceptual framework for data.
  • Semantic Layer: The practical application of the semantic model, translating technical data into user-friendly formats. In essence, the semantic model gives a theoretical understanding, while the semantic layer provides a tangible interface for data interaction.

How Can Data Analytics Benefit from a Semantic Model?


Have you ever grappled with meaningless column names or struggled to translate underlying metadata into business terms? Every data practitioner has. These challenges underscore the importance of semantic models for business users.


To understand the situation mentioned above, let's consider a few scenarios. First, imagine you are a business user needing to access data from various channels. A semantic model can help you by providing a consistent data model with a logical framework that can be used across multiple sources.


Next, let's consider another scenario where you are a non-technical data producer. You might have data stored in different systems, and you need to make this data available to others in your organization. A semantic model can help you by providing a simple interface for accessing the data in a structured manner.


Navigating cryptic column names or translating metadata into business terms is a challenge many face. Semantic models address these issues, streamlining data access and interpretation for business users. But beyond these immediate benefits, what are the core competencies that make semantic models indispensable in the world of data analytics?


Key Competencies of A Semantic Model


Semantic models offer shared business logic, quick insights for complex queries, a unified security strategy, and high-performance engine capabilities. These competencies not only enhance the user experience but also ensure that businesses can leverage data more effectively and efficiently.


Shared Business Logic

By providing a semantic model, different business departments within a company can share the same business logic. This eliminates the need to separately develop semantic information with BI tools and data applications. The semantic model acts as a consistent business model that maps complex data into clear business terms. This creates a unified semantic model where dimensions, measures, and hierarchies, etc., are synchronized to enhance business semantics over technical data and facilitates various analyses for business people.


Quick Insight for Complex Queries

Data analysts can provide business users with straightforward insights into efficient performance by defining time intelligence functions, typically like YOY (year-on-year) and YTD (year-to-date). In addition, decision makers can easily manage complex analysis scenarios involving multiple fact tables, many-to-many relationships, and semi-additive measures through semantic models.


Unified Security Strategy

Semantic models allow enterprises to reduce data security risks by centralizing user and data access management at the data asset layer of the platform. This configuration carries over to all business applications at the upper layers, thus eliminating the need for IT personnel to configure extra data access controls for downstream systems. Ultimately, this prevents data from being spread across different business systems and reduces associated security risks.


Leveraging High-Performance Engine

By leveraging an underlying distributed cluster, semantic models can greatly improve overall query speed even across many dimensions and large volumes of data. Additionally, caching frequently used data helps to reduce access time and improve the overall experience. As a result, enterprise teams can enjoy stable and superior performance while using semantic models.


Having explored the competencies of the semantic model, it's essential to remember the distinction between the model and the layer. While the semantic model provides the conceptual understanding of data, the semantic layer offers the tangible interface for users to interact with that data. With this in mind, let's delve into the typical implementations of semantic layers.


Typical Ways to Build A Semantic Model


Building an effective semantic layers requires the right placements. Gartner categorizes common semantic layers into the following three types in their report "Demystifying Semantic Layers for Self-Service Analytics":

  • Data Layer Semantics: This semantic layer is built within a data platform as an extension of data services. It is typically implemented through data marts, views (including materialized views), and Online Analytical Processing (OLAP) models.
    • Advantages: Highly centralized and governed data; maximizes the utilization of internal resources and technologies; provides a large amount of data to existing users.
    • Challenges: Highly dependent on the IT department and data engineers; lacks flexibility; difficult to handle unstructured data.
  • Stand-alone Semantic Layer: This is an independent layer located between data sources and the consumption layer. It is usually implemented through data virtualization, abstraction layers, or data lakes.
    • Advantages: Well-governed data; centralized storage of data analysis models and metrics; supports multiple data formats from various data sources.
    • Challenges: Requires significant IT development and implementation work; traditionally not designed for data science, machine learning, and integrated application use cases; introduces extra complexity to enterprise data analytics architecture.
  • A&BI Tool Semantic Layer: This is typically an embedded feature within a BI analytics tool, located within the data analysis layer.
    • Advantages: Supports flexible and agile data analysis, accelerating the process from data to insights, and promoting data democratization.
    • Challenges: Lack of data governance; data models and metrics are scattered across multiple systems; limited by the technical stack of the BI tool.

The Imperative of Adopting a Semantic Layer Now


In the rapidly evolving landscape of data analytics, the adoption of a semantic layer has become more than just a trend—it's a necessity. A recent poll conducted by Gartner, a leading research and advisory company, sheds light on this pressing need. The poll titled "Have you considered using a universal semantic layer in your analytics architecture?" garnered responses from 118 participants, revealing the following insights:

  • 12% of participants mentioned they already have a universal semantic layer in place.
  • A significant 53% are actively pursuing the implementation of such a layer.
  • 14% have considered it but are not currently pursuing it.
  • 17% have not considered it at all.
  • A mere 4% were unsure about what a universal semantic layer entails.

These numbers underscore the growing recognition of the value a semantic layer brings to analytics architecture. Modern data architectures require a semantic layer to ensure precision, reusability, reliability, and cost-effectiveness. It abstracts and augments metrics and other semantic components, allowing business users to consume and explore data without needing to be technical data pipeline experts. Moreover, a semantic layer provides a governed, curated metrics data mart, enabling business users, data analysts, and data scientists to focus more on insights and less on backend table structures.


The increasing inclination towards the adoption of a semantic layer is a testament to its critical role in modern data analytics. As businesses grapple with diverse data sources and the challenges they pose, the semantic layer emerges as a solution that bridges the gap between complex data structures and comprehensible insights.


Evolving Semantic Models

Control vs. freedom in analytics

With the rise of various tools and concepts, the approaches to implementing semantic Models in enterprises have also evolved. Over time, there has been a convergence trend between BI tools emphasizing self-service analytics and semantic layers focusing on centralized governance. According to Gartner's report, the drive toward this convergence of capabilities has several prevailing themes:

  • Organizations’ demands to balance governance and agility: Organizations that adopted self-service — whether organically or as a reaction to the inflexibility of IT- controlled semantic layers — find themselves straddled with mounting technical debt to maintain fractured views of metrics. They are looking for some enterprise level of governance to guide further development.
  • Organizations’ increased demands for integrated analytics: The growing demand for analytics across use cases, including data science and machine learning (DSML) and integrated applications, has caused many organizations to build dedicated pipelines to serve these needs. Both traditional and self-service semantic models generally have not supported these use cases.
  • Vendor developments to expand use-case support: Both vendors of stand-alone semantic layers and vendors of self-service A&BI tools are actively developing to achieve the utopian universal semantic layer platform. The emergence of the metrics store concept draws a convergence between the centralized governance of IT-led solutions and the business-user collaboration of self-service platforms.

While these three types of semantic layers each have unique benefits, they also come with a common set of challenges. In many cases, enterprises struggle with enabling business users to define and maintain metrics in a self-service fashion, while ensuring IT-centric data governance. As a result, there's an increasing need for a solution that can overcome this obstacle, which is where the concept of a Metrics Platform comes into play.


Kyligence: Metrics-driven Approach to Build A Semantic Model


Metrics are powerful. Almost any behavior can be distilled into specific, trackable metrics, making them more engaging than mere tables for businesses. Therefore, businesses often find metrics more engaging than tables. Metrics provide the most user-friendly presentation and yield values that can be directly perceived and more effectively drive the realization of business goals. This scenario exemplifies a typical metrics-driven approach.

Metrics-driven approach to build a semantic model

A metrics-driven strategy is particularly effective in constructing semantic models. By refining and consolidating business information into a uniform manner, metrics can communicate crucial data to business users or data producers more effectively. Semantic models must evolve to better serve metrics-driven businesses. This approach aids businesses in understanding their performance and making metrics-driven decisions.


Discover the Power of Kyligence Zen: Transforming Enterprise Data into Actionable Insights


Based on years of experience working with valued customers, Kyligence has incorporated a metrics-driven approach to construct a semantic model. This led to the creation of a metrics platform called Kyligence Zen.


Kyligence Zen - The Metrics Platform


Kyligence Zen is the go-to low-code metrics platform to define, collect, and analyze your business metrics. It empowers users to quickly connect their data sources, define their business metrics, uncover hidden insights in minutes, and share them across their organization.

Kyligence Zen Architecture

In order to facilitate metrics-driven decision-making processes, Kyligence Zen offers a range of features:


Metrics Catalog

Make sure everyone is on the same page with a unified metrics catalog. Align the definition of metrics across your entire organization, from business owners to data consumers and decision-makers. Get smarter insights and make better decisions in less time.



Streamline the business decision-making process with unified metrics across all businesses, data consumers, and decision-makers. With Goals, Kyligence Zen helps organizations break down business objectives into relevant metrics and targets.


Kyligence Copilot

Revolutionize your approach to data analytics with Kyligence Copilot. No more tedious data crunching - just chat, ask, and discover. Our AI-driven chatbot understands your business metrics, allowing you to communicate with your data in the most intuitive way, even without technical expertise.


Metrics Automation

Stop wasting precious hours crunching numbers. With Kyligence Zen's automated metrics calculation by the built-in augmented OLAP engine, you can spend less time calculating and more time taking action on the data that matters most.


Metrics API

With Open APIs, Kyligence Zen makes it easy to connect your organization's data and metrics with the BI and SaaS tools that make sense for your business - no matter what they are. Align all of your businesses, data consumers, and decision-makers on one unified metrics platform.




Semantic models and semantic layers play pivotal roles in our data-heavy world. To reiterate, the semantic model provides a conceptual understanding, acting as a translator between complex data structures and our comprehension of them. On the other hand, the semantic layer offers a tangible interface, simplifying our interaction with this data and making it business-relevant and user-friendly.


Using these tools effectively, as demonstrated by platforms like Kyligence Zen, can offer businesses a significant advantage. Kyligence Zen, with its metrics-driven approach to building semantic models, empowers users to connect their data sources swiftly, define their business metrics, uncover insights, and share them organization-wide. By focusing on these aspects, Kyligence Zen enables businesses to decode their data efficiently, catering to their diverse needs.



  1. Gartner. (n.d.). Semantic Data Model.
  2. O'Reilly Media, Inc. (n.d.). Semantic Modeling for Data.

Further Reading


Want to delve deeper into semantic layers? Check out these articles to learn more:

  1. Semantic Layer: The BI Trend You Don't Want to Miss
  2. Kyligence Continuously Recognized by Gartner in "Demystifying Semantic Layers for Self-Service Analytics" Report
  3. Metrics Store: A New Critical Capability for Analytics and Business Intelligence Platforms