A semantic layer is a way to represent data so that it can be easily understood by business users, making it easier for them to interpret and use it directly, without a dependence on data engineering teams. Wikipedia defines it as “a business representation of corporate data that helps end users access data autonomously using common business terms”.
The layer serves as a critical bridge between raw data—described precisely by rows, columns and field names but which can only be understood by analysts and data scientists—and actionable insights for business users. By adding meaning to data and allowing better-informed interpretations, this layer transforms the data into a more understandable and valuable form.
What Role Does a Semantic Layer Play In the BI Ecosystem?
Business users often lack the technical knowledge to access or explore data directly from data sources. A sematic layer removes this constraint and allows them to unearth insights on their own. As an example, sales data comprises of transactions stored in rows and columns in multiple tables with complex relationships. A semantic layer translates this data into business terms, with dimensions such as customer, product, supplier and location, and metrics such as revenue, cost and profit.
To summarize, a semantic layer provides a unified view of an enterprise’s data across all its systems and departments by encapsulating business definitions, logic and relationships. By enhancing data usability, it empowers decision-makers to make informed choices based on reliable data interpretations. The layer plays a pivotal role in unlocking the full potential of data, enabling organizations to uncover patterns, trends and opportunities that drive growth and competitive advantage.
Semantic Layer Implementation: The Key Challenges
Organizations inevitably develop semantic layers when interacting with data, usually within BI platforms. As different parts of the organizations work with disparate BI, analytics and data science tools, each tends to create isolated data definitions, dimensions, measures, logic and context. Semantic layers, if tightly coupled with BI tools, are then managed by separate teams. This leads to discrepancies in data interpretation, business concepts and definitions among different user groups, resulting in mistrust of reports and intelligence derived from data.
With advances in data technology and engineering, the implementation of semantic layers moved within data pipelines and warehouses. These, however, had their own challenges. When semantic layers are implemented with data pipelines, a technical team is required for connecting BI and analytics use cases to data assets. Data engineers are responsible for transforming and preparing data for analysis. Any bottlenecks or inefficiencies in pipeline design, data extraction, transformation or loading can hinder the timely availability of actionable insights to business users. These factors again cause a dependency and delay in delivering actionable insights for data-based decision-making.
In addition, most business users cannot directly work with data warehouses. Data is required to be made “business-ready” through views or data marts, which creates yet more dependency on technical resources. While centrally controlled definitions and metrics avoid discrepancies, transformation models become inflexible and unable to serve diverse workgroup needs.
Further, querying massive cloud-scale tables often leads to slow performance, compelling users to extract data into BI and analytics platforms, once again, fostering localized semantic layers. None of these solutions has proved to be the right choice.
A Universal Semantic Layer Delivers Greater Value
With unprecedented growth in data volume, the task of reporting, analyzing and extracting insights has become colossal for enterprises already grappling with challenges of managing and making sense of a data deluge. The size and complexity of data is only expected to increase exponentially as data sources and data volumes continue to grow.
As a single and dedicated intermediary/abstraction layer between an organization’s data sources and its analytical tools, a universal semantic layer addresses these challenges. The layer enables a transformative approach by providing a unified and standardized view of the data, independent of its original sources. Abstracting the complexities of various data structures, formats and schemas ensures a seamless and cohesive data experience, even when working across different systems and applications.
For instance, consider a global enterprise like an investment bank which needs a single view of data across multiple geographies, lines of business and has complex reporting requirements. Without a universal semantic layer, it is extremely challenging to analyze a large volume of financial data or to model complex KPIs. By creating a single source of truth, a universal semantic layer allows the centralization and unification of complex business logic, such as year-over-year calculations and currency conversions.
Modern universal semantic layers extend these capabilities to cloud-native ecosystems. Within this ecosystem, massive volumes of data stored in the cloud can be abstracted and made accessible to all relevant users as a unified source. Organizations can promote a data-driven culture and harness the power of all their data streams by implementing a universal semantic layer.
Advantages of a Universal Semantic Layer
Build a self-serve BI ecosystem: Business users lack the technical expertise to comprehend and utilize their data assets fully. A universal semantic layer makes it easier for them to view and interpret their data in terms of standard business terms, without having to worry about the technical complexities of data models. This allows users to simply drag and drop dimensions, metrics or hierarchies to create reports and charts that help in optimizing their business.
Establish data trust: The use of multiple BI tools, each with its semantic layer, leads to inconsistencies. For instance, if multiple departments of an organization maintain their own data and analytical silos, any analysis performed on them will produce different answers, leading to an erosion of trust in the data and impeding data-driven decisions. A universal semantic layer addresses these concerns by creating a single source of truth that enables smart analytics. This ensures that decisionmakers get the consistent and accurate answers, without the analytics tool they use becoming a factor.
Get cloud cost optimization: As data volumes explode, organizations are witnessing a spike in their cloud spending, especially for compute costs. While enterprises aim to drive a data culture and democratize data, numerous concurrent users not only impact costs but also slow down query performance. The layer solves this problem by pre-processing or pre-aggregating data and using it as a base for analytics. Once these ‘build-once-query-multiple-times’ models are built, users do not need to access cloud-based data lakes or warehouses repeatedly, thus cutting down query processing costs and times.
Centralize data security and governance: A universal semantic layer helps in establishing centralized, universal data security and governance policies. It establishes a multi-tiered security architecture which provides enhanced data access control with robust, role-based data access, encryption, authentication and data masking, where privileges may be assigned based on user roles, departments, or specific data elements.
This level of granularity provides flexibility, allowing for precise control over who can access what information, whilst ensuring data security, privacy, integrity and compliance with regulatory requirements. In addition, self-service analytics and a unified data source eliminate the possibility of circumvented security rules, undermined data integrity or corrupted data sources, such as users creating local copies of data.
Simplify data modeling: Data within organizations often resides in disparate systems across departments and locations, resulting in time-consuming data modeling tasks for data engineering teams. Universal semantic layers offer a solution by creating a unified data model across all data sources, simplifying and optimizing tasks for these teams. The layer allows them to deliver a consistent, comprehensive view of the enterprise by working seamlessly with the visualization layers of all analytical tools used by the enterprise.
Enable scalability and performance: As data and analytics needs grow, scalable solutions are necessary to handle data at any level without compromising overall BI performance. A semantic layer is designed to deliver exceptionally high performance on enterprise-wide data. Powered by smart technologies like data pre-aggregation, advanced query optimization and distributed computing, a universal semantic layer provides instant answers to user queries, even with extremely high data workloads. Minimizing latency, it makes all data landing in warehouses query-ready within minutes.
Enable collaboration and holistic decision making: Deriving coherence from data can be challenging, particularly for disparate teams seeking to access information from different business perspectives. When multiple teams across an organization have an integrated data set to work with, it becomes easier for them to collaborate and to reuse each other’s data products, yielding richer insights and eliminating duplication of effort. This collaboration delivers a comprehensive view of the organization to leadership and leads to a holistic, concerted effort to meet business goals.
To summarize, a universal semantic layer serves as a transformative element in modern BI stacks. Reducing cloud consumption costs, increasing agility, improving query performance and enabling collaboration without forgoing security are the key benefits of its successful implementation. By providing a central place to get consistent and reliable data, it ensures that all stakeholders have access to the same source of truth, thus eliminating data silos, enhancing data governance and fostering collaboration for establishing a data-driven culture throughout the organization.
Embracing a universal semantic layer within a modern data stack could be the strategic move that propels organizations towards success today, as well as makes their BI and analytics platform future ready.
About the author: Ankit Khandelwal is the senior director of engineering at Kyvos Insights, a data analytics and business intelligence company. He leads the engineering team responsible for developing and deploying the company’s enterprise-scale BI platform. Khandelwal has more than 20 years of experience in the software industry and has held several leadership roles in engineering, product management, and operations. At Kyvos, Khandelwal is responsible for leading the development of the company’s big data analytics platform and its associated services. He is also responsible for the ongoing development and improvement of the platform, managing the engineering team, and strategizing on the product roadmap. Khandelwal is a highly experienced and respected leader in the software engineering space. His passion for technology and his commitment to excellence have been instrumental in Kyvos Insights’ success in the analytics industry.
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Author: Ankit Khandelwal