Hearing buzz about the semantic layer everywhere, but not sure what it really is or why it's important? This article has got you covered.
If you work in or around data, you’ve probably heard the term “semantic layer” everywhere lately. In this blog article, we aim to define it, explain its importance and benefits, and provide a few thought-starters for how to implement it at your organization.
The semantic layer is an abstraction layer that sits between the raw data and the end data consumers, offering a unified and universal way for everyone in the company to understand the data. It essentially provides a simplified, business-friendly “translation” of the data that allows users to interact with it using common business terms, rather than the technical terms used in the underlying data sources.
Simply put, think of the semantic layer as a translation layer that enables your organization to put in place a “company-wide data language” in which all data definitions are consistent and easy to understand. The “translation” happens between the data sources and any data presentation layer (BI platform, visualization tool). The semantic layer is created after the data modeling step in the data process.
Needless to say, it’s a stretch to ask business users to try to understand technical metadata such as table names, columns, and other data terminology — it’s meaningless to them, and frustrating to have to deal with such a learning curve when they simply need data to inform a business decision. That’s part of where the friction between data teams (who work on the data and data process) and the business teams (who need the data for their decision-making) comes from.
The semantic layer is designed to simplify data access, analysis, and reporting for end-users. It removes the complexities of the underlying data sources, providing a unified view of the data that can be easily understood by business users. The semantic layer typically includes business rules, calculations, and metadata that define how the data is organized, structured, and analyzed. With a semantic layer in place, different data definitions from different data sources can be quickly mapped for a consistent and single view of data for analytics.
For example, you might notice that everyone in your company has different labels and definitions for the word “lead.” Some people call them “prospects,” or perhaps “contacts.” There may also be confusion around “active” users or “paying” customers. The semantic layer allows you to define metrics and prevent the confusion around these varied terms once and for all, company-wide. Giving proper, universal “labels” to certain words that could cause confusion earlier in the data process (in the semantic layer), avoids having contrasting numbers in different tools later on.
The semantic layer is a powerful way for domain experts and data practitioners to set a common understanding of business metrics, and to get everyone on the same page. Rather than re-creating siloed metrics and dimensions in each system and app that contains data, the semantic layer allows us to define them once — in a version-controlled way — that is used for analysis in the BI platform.
With this common “layer” of understanding, efficiency and accuracy of data analysis can be significantly improved. This simplified, business-friendly view of the data in the semantic layer helps users make more informed decisions and extract insights from data more quickly and easily.
For these reasons, a semantic layer is an extremely valuable, and in our view, a necessity for organizations that want to empower business users who don’t have data expertise or background to self-serve the analytics they need, whenever they need it. It gives the confident ability for business users to answer their own data-related questions and run queries based on a common user-friendly language.
Despite this being a buzzword that’s everywhere in the data world at the moment, and easy to write off, it’s something you absolutely need from day 1 when you’re starting to work with data. In our point of view, it’s best to start with a very simple one if possible to ensure collaboration and enable self-service down the line, but you don’t need to invest a bunch of resources from the get-go.
It’s something that will become more important eventually, later on, when you’re starting to onboard more and more users onto data.
There are many types of tools in which you can create the semantic layer, such as in transformation tools like dbt or Keboola, as well as BI platforms. We recommend going for a BI platform that includes and supports the creation of the semantic layer already, like Whaly. This way, you don’t have to manage a separate tool. Plus, having the semantic layer already integrated into the data consumption (BI) platform means that once you load your data from your data warehouse, you can perform modeling and create the semantic layer in one place — ensuring that it’s always properly reflected for your end data consumers and closer to the “last mile” of data analysis.
By ensuring that your BI platform already has semantic layer creation capabilities, you can ensure that your business teams will always get the same answer when they run a query around the data, every single time.
When you're at the point where you're onboarding more and more people from your organization onto data, across a variety of diverse roles, you'll also realize that it's a good idea to go for a no-code or low-code semantic layer option. If you have a semantic layer in Looker that requires LookML / code, it will be more difficult to scale and get more people to adopt and work with data. The fact that non-technical people can contribute and work from the semantic layer will do you wonders as you scale and grow your data organization and culture.
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