AtScale White Paper
In business intelligence (BI) and analytics, the key abstraction used in the majority of implementations is called the “semantic layer.” Here’s how Wikipedia defines it:
Semantic, in the context of data and data warehouses, means ‘from the user’s perspective’; which sounds like a nice clean solution to a nasty unbounded complexity problem. Add the adjective “Universal” to the definition and you can see how a Universal Semantic Layer (USL) should be a critical element of the modern analytics stack.
The semantic layer concept was originally patented in 1991 by Business Objects and was successfully challenged by MicroStrategy in 2003. The data warehousing space has changed drastically since then. Cloud data lakes and cloud data warehouses have become well-accepted data platform architectures. According to the 2020 Big Data & Analytics Maturity Survey, 61% of respondents currently operate cloud data platforms, and 48% plan on deploying them in the near future. In the meantime, Hadoop didn’t become the end all data solution but, rather, just one solution for managing data.
This means that data architects are increasingly becoming more comfortable with data living in different locations and in different platform architectures. The challenge of managing data access and quality across multiple silos is a big issue for IT. This is why a Universal Semantic Layer is becoming an even more critical piece of everyone’s data platform strategy.
With more and more enterprises moving to the cloud, their analytics stack becomes more complicated because their on-premise data stacks live on throughout the transition. In fact, many of these on-premises data platforms may never go away. For IT teams who need to bridge their business users across these old and new worlds, a Universal Semantic Layer is a great equalizer. It hides the physical complexity from users by presenting them with understandable business terms and business-friendly data instead of database schemas and raw SQL.
The architecture diagram below shows that with a proper Universal Semantic Layer, BI users get a centralized repository of business terms and data virtualization that makes data access ubiquitous.
Data today is more dispersed. While the needs of a data scientist and a BI user may seem quite different, they both need simple and secure access to clean, understandable data. With today’s self-service architectures, the analytics consumers are forced to become data wranglers and data engineers. In fact, the average data scientist spends more than 80% of their time preparing data rather than modeling it. Besides being a colossal waste of time, by asking business users and data scientists to program their own metrics and business terms, we’ve created a recipe for chaos and inconsistency. The Universal Semantic Layer is an excellent solution to this problem. By defining business metrics, data access and transformations in one place, analytics consumers are almost guaranteed to speak the same language, regardless of their use case or toolsets.
By creating a single point for data access, the Universal Semantic Layer also serves as a central governance gateway across the enterprise. IT can secure the data and control its access once and for all. As you can see from the chart below, 79% of enterprises rank cloud security and governance critical to their success in the cloud.
When a Universal Semantic Layer is added to the architecture, the stack is simplified, not complicated. You can retire multiple, proprietary and conflicting semantic layers that are tough to maintain and impossible to keep in sync. The key is that a semantic layer is useless and counterproductive unless it’s universal.
By investing in a stand-alone Universal Semantic Layer, you can free yourself from vendors’ proprietary chains and create the flexibility you’ll need as new data platforms and tools inevitably continue to proliferate. Best of all, with a Universal Semantic Layer, everyone will be speaking the same language and playing by the same rules of data access.
The Global 2000 relies on AtScale – the intelligent data virtualization company – to provide a single, secured and governed workspace for distributed data. The combination of the company’s Autonomous Data Engineering™ and Universal Semantic Layer™ powers business intelligence and machine learning resulting in faster, more accurate business decisions at scale. For more information, visit www.atscale.com.