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In recent years, the data ecosystem has been undergoing rapid evolution with the emergence of new technologies on a daily basis. An increasingly popular technology within this landscape is the semantic layer. This crucial component of the modern data stack is instrumental in facilitating accessibility and comprehension of data for business users and data producers.
Every data practitioner understands the challenges that business users face in understanding the data itself, such as meaningless column names and the absence of translation of underlying metadata into business-oriented terms.
To understand the situation mentioned above, let's consider a few scenarios. First, imagine that you are a business user who needs to access data from various channels. A semantic layer can help you by providing a consistent data model that can be used across multiple sources.
Next, let's consider a 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 layer can help you by providing a simple interface for accessing the data.
Obviously, a great solution to the problems indicated is the semantic layer, which is a business abstraction that is derived from the technical implementation layer to uniformly maintain business logic. It frees us from concerns about complexity, provides a clear picture of underlying data sources, and finally offers a consistently simplified view of the data to business users and data producers.
Semantic layers, which were conventionally created within BI tools and data applications, resulted in inconsistencies across the organization and distribution channels. To ensure consistency throughout the enterprise, it is essential to have a standardized approach to forming semantic layers.
There are several key offerings of semantic layers that make them a valuable tool for businesses. These include:
By providing a semantic layer, 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 layer acts as a consistent business model that maps complex data into clear business terms. This creates a unified semantic layer where dimensions, measures, and hierarchies, etc., are synchronized to enhance business semantics over technical data and facilitates various analyses for business people.
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 layers.
Semantic layers 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.
By leveraging an underlying distributed cluster, semantic layers 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 layers.
As the process of democratizing analysis intensifies, the demand for self-service data-using manner continues to expand. In order to fulfill this need, data products must cater to business individuals and minimize any hurdles faced by producers. Therefore, there is a growing need to evolve beyond the limitations of the traditional semantic layer.
The traditional semantic layer still expresses itself in the form of tables or BI reports that show a data-driven point of view. However, businesses find metrics more engaging than tables, as metrics are the most user-friendly presentation. Metrics provide values that can be directly perceived and better drive the realization of business goals, thus exhibiting a typical metrics-driven scenario.
The metrics layer plays a crucial role as the bridge between DW and actual analytics. Semantic layers have to evolve into enabling better service for metrics-driven businesses. This approach aids businesses in comprehending their performance and making metrics-driven decisions.
Simplification is a constant and universal pursuit when managing data. Although semantic layers are potent, they can be challenging to employ and sustain due to the need for specialized technical abilities.
To solve this issue, low-code can create a significant impact by removing technical barriers and monotonous tasks like data modeling and ETL. It accomplishes this usually by offering a straightforward UI. Additionally, large-scale automation must be implemented to automatically recognize query modes, optimize existing data models, unintentionally accelerate popular metrics, and more.
Based on years of experience working with valued customers, Kyligence has developed a new approach to semantic layers that inherits the original benefits while addressing the limitations present in traditional semantic layers. As a result, Kyligence has introduced a metrics platform called Kyligence Zen.
Kyligence Zen is the go-to low-code metrics platform that defines, collects, and analyzes 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.
In order to facilitate metrics-driven decision-making processes, Kyligence Zen offers a range of features:
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.
Stop wasting precious hours crunching numbers. With Kyligence Zen's automated metrics calculation powered by a built-in industry-leading augmented OLAP engine, you can spend less time calculating and more time taking action on the data that matters most.
With open APIs, Kyligence Zen makes it easy to connect your organization's data and metrics with the BI and SaaS tools preferred by your business - no matter what they are. Align all of your businesses, data consumers, and decision-makers on one unified metrics platform.
Check out the extensive catalog of metrics templates created by professionals. Apply the metrics in clicks to your data sets. You can save 80% of your time and energy by avoiding the need to construct them from scratch.
In conclusion, the semantic layer is an essential ingredient of the modern data stack that helps bridge the gap between technical jargon and business terminology. It allows business users and data producers to understand and access complex data easily. However, traditional semantic layers have limitations and may not be suitable for metrics-driven businesses, and they may require specialized technical expertise.
Kyligence has introduced a new approach called Kyligence Zen, a low-code metrics platform, that enables companies to democratize analysis and make data-driven decisions without being impeded by technical barriers. Ultimately, the semantic layer, combined with Kyligence Zen, can help businesses develop a competitive edge in today's metrics-driven market.
Learn more about Kyligence Zen here or try out Kyligence Zen for free.
The following references were used in creating this document:
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