Expert Reveals Semantic Layer Advantages in Modern Data Stack

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise tech. In this feature, Kyvos Insights‘ Pratik Jain reveals semantic layer advantages in the modern data stack.

A significant shift is happening in how data is seen by businesses today. There is an increasing recognition of its formidable potential for business transformation. Data is increasingly perceived as a potent resource capable of driving innovations, efficiency, product enhancements, customer engagement and overall strategic decision-making.

This shift is leading a notable change in who drives data requirements. Earlier, the impetus for data needs predominantly came from analysts or IT teams, but now business users are taking a lead role. They want to ensure that the insights that they need from data are readily available, and tailored to their specific needs.

Business users, however, lack the technical expertise to work with complex data schemas, SQL queries and intricate data structures. What they need is a user-friendly interface and an intuitive language to interact with data, and this is where the concept of a semantic layer comes into play. It bridges the gap between the complexity of underlying data and the accessibility that business users require, enabling them to extract meaningful insights without being data experts themselves.

Quoting from Wikipedia, a semantic layer is a “business representation of corporate data that helps end users access data autonomously using common business terms”. The semantic layer’s core purpose is to enhance data’s utility for the business. Acting as an abstraction layer, it maps the source data into familiar business views using dimensions, measures and hierarchies that business users are familiar with, like products, sales, territories, time period, customer ID etc.

Also called the metric layer, a semantic layer is sandwiched between the presentation layer (BI, analytics tools, data science tools) and the data warehouse or data lake. It has been called the “foundational plank” in a modern data stack that makes it functional, practical, efficient and scalable. The advantages of a semantic layer are manifold.

Self Service

A semantic layer allows business users to independently explore and derive granular insights from the data with minimal or no reliance on IT and data experts. They can create custom reports, perform ad-hoc analyses and gain valuable insights on their own. Organizations can significantly reduce bottlenecks in the data/ insight delivery process by eliminating the need for IT or data experts to generate reports or answer data-related queries.

With self-service analytics through a semantic layer, business users can swiftly access and explore the data leading to faster insights. They can ask questions, refine queries and investigate data anomalies in real time, enabling quicker responses to changing market conditions or emerging opportunities.

Single Source of Truth

Various departments in an organization tend use different BI tools – each having their own metric definitions. For example, a week may be defined as starting Monday or Sunday; time as local or GMT; leading to different versions of reports from same source data.

A semantic layer provides a single unified set of metrics within a data stack across the organization that are shared across various BI tools. By harmonizing and standardizing the metric definitions across the company, it fosters a single source of truth.

Departments can continue to use their preferred BI tool to access and interpret data. This promotes a common and consistent understanding of key business performance indicators across the organization and raises the trust in data-driven decisions.

Improved Performance

An organization’s data and analytics requirements continues to evolve and expand over time. Modern data stacks should be designed to be highly scalable so that they stay current. A semantic layer in the stack empowers exceptional performance for enterprise-wide analytics, with minimal degradation as the scale increases.

It pre-aggregates data to optimize performance across numerous dimensions and measures, without the need for data movement. Even as your data workloads grow exponentially, users can obtain instant insights and answers to their queries. Besides a unified view, scalability and speed are critical in a data-driven business as agility and responsiveness are key to maintaining a competitive edge.

Unified and Efficient Data Modeling

A Semantic layer creates a unified data model across all data sources that may reside in disparate systems across departments, on-prem or cloud, as well as geo locations. Irrespective of BI tools or storage platforms, it presents a consistent view of enterprise data.

Data modeling tasks are simplified with a Semantic layer with these added benefits:

• It functions on top of diverse data sources providing virtualization and federation capabilities, enabling business users to access data from multiple sources as if it were centralized.
• Users can define intricate calculations and express complex business logic facilitating the extraction of deeper insights from the data.
• The layer supports the addition of new data sources without disrupting the existing business data view, ensuring scalability and adaptability.
• It optimizes data access, eliminating redundancy and latency, resulting in seamless and efficient data retrieval.

Data Security and Governance: By acting as a gatekeeper between the enterprise data storage system and BI tools, a semantic layer incorporates a formidable mechanism for enforcing security measures. It allows for precise and granular control over who can access what data, implementing row and column-level security measures, at both group and user levels.

Sensitive data remains protected and is accessible only to authorized individuals. This meticulous control contributes to the organization’s overall data governance strategy, enhancing data quality and integrity while preserving data privacy and compliance.

In summary, the implementation of a semantic layer not only simplifies data analytics but also enhances data-driven decision-making by providing a unified, efficient and scalable approach and accessibility of data insights across the enterprise.