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Kyligence Continuously Recognized by Gartner in Demystifying Semantic Layers for Self-Service Analytics Report

Kaige Liu
Head of North America
Jun. 08, 2023

Recently, Gartner released a research report titled April 2023 Demystifying Semantic Layers for Self-Service Analytics. Based on Gartner's latest report, this article will explore semantic layers and how they serve self-service analytics in enterprises.




As businesses strive towards data-driven objectives, the demand for data analysis is becoming increasingly complex. The semantic layer, as a part of the enterprise data architecture, is typically developed and maintained by the IT department to support various data analysis needs of business departments. To better meet those needs, many enterprises have started deploying self-service BI analytics tools to fulfill their requirements for agility and flexibility. Consequently, data teams often spend a significant amount of time on tasks like report development, which are tedious and repetitive.


Over time, the BI analytics tools/systems procured by business departments have gradually formed data silos, further affecting broader company objectives such as data consistency and collaborative sharing of key performance indicators. Therefore, the IT department has begun leading and promoting secure and trusted data management goals, seeking a balance between self-service mode and centralized mode.


Common Implementations of Semantic Layers


The semantic layer is typically an abstraction of business logic built on top of a data platform. It unifies and maintains business logic to provide unified and trusted data for users throughout the enterprise, enabling business personnel to access and understand the data easily. Gartner categorizes common semantic layers into the following three types in their report:

  • 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.

Evolving Semantic Layers


With the rise of various tools and concepts, the approaches to implementing semantic layers 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.


What is a Metrics Store?


Users can create business metrics in a metrics store through the user interface or IT could define metrics through code. It manages metrics from data warehouses and serves downstream data analytics, data science, and business applications.The main purpose of a metrics platform is to manage metric definitions and serve various data analysis tasks centrally. Ideally, a metrics platform enables business users to contribute to and maintain metric definitions while being centrally governed by the IT department.Compared to standalone semantic layers, a metrics store further achieves:

  • Business users is able to contribute and manage metric definitions.
  • Exposing metrics to BI visualizations, SaaS integrations, and an API, opening up tons of new use cases that were not previously possible with BI reporting.
Compare traditional BI reporting with metrics store

According to Gartner's report, the commercialization of metrics stores is still in its infancy. Therefore, it is yet unknown whether metric stores will become their own layer in the analytics stack or be absorbed as a capability of the semantic layer. The implementation of a metrics store offers a compelling capability to define and manage (often disparate) analytics definitions.


Gartner lists Kyligence as a representative vendor of the stand-alone semantic layer. Gartner has previously included Kyligence in multiple reports, such as Gartner's June 2022 Hype Cycle for Data Management, September 2022 Innovation Insight: Metrics Store, and September 2022 Cool Vendors in Data Management, China.


Kyligence Zen: An All-in-One Metrics Platform


As one of the earliest vendors to attempt the implementation of a metrics store, Kyligence has already helped enterprise customers in the finance, retail, and manufacturing industries build Metrics Platforms. Based on rich industry practices and technological accumulation, Kyligence has officially announced the general availability of its all-in-one Metrics Platform, Kyligence Zen, this year.


With Kyligence Zen, users can build a common data language across an entire organization for trusted and consistent key metrics. It is a comprehensive all-in-one solution that simplifies the management of metrics and lowers costs by using a single platform.

Kyligence Zen Architecture
  • Enable everyone to agilely utilize metrics for their work and quickly respond to business changes.
  • Ensure consistent metric standards for all business users, data consumers, and decision-makers.
  • Simplify the process of defining metrics and data calculations, change the way businesses use data, and reduce development costs.
  • Strengthen data collaboration and sharing by integrating with BI tools and SaaS products through open API interfaces.

As we have seen, the demand for a solution that balances centralized governance and self-service agility in data analysis is rapidly growing. The emergence of metrics platforms like Kyligence Zen promises to address this need by creating a common data language across enterprises. The advantages of implementing such a platform are substantial, including simplified metric management, consistent key metrics, and enhanced data collaboration.


Now that you've gained a deeper understanding of the potential of Kyligence Zen, it's time to experience it firsthand. We invite you to explore the platform and see how it can revolutionize your business's data analytics.


Try out Kyligence Zen for free today, or learn more about Kyligence Zen here. Discover how Kyligence Zen can transform your organization's data management and help you confidently enter a data-driven future.


Learn more about Kyligence Zen here or try out Kyligence Zen for free.

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  1. Gartner. (2023, April). Demystifying Semantic Layers for Self-Service Analytics. Gartner Research.
  2. Gartner. (2022, June). Hype Cycle for Data Management. Gartner Research.
  3. Gartner. (2022, September). Innovation Insight: Metrics Store. Gartner Research.
  4. Gartner. (2022, September). Cool Vendors in Data Management, China. Gartner Research.