It’s taken as read that the data created by your organization is incredibly valuable. Collecting and analyzing your data can lead to improved customer insight, increased market intelligence, forward-thinking innovation, improved business operations, and too many other business benefits to count.

But as ubiquitous as your data is, it’s worthless if your teams don’t understand it.

Most professionals aren’t technical experts in data engineering or data analysis. But they shouldn’t have to be in order to draw insights from your business’ data. In order to make knowledge gleaned from your raw data accessible to your organization at large, it’s crucial to provide a business-friendly view that allows anyone in your organization to quickly and easily find answers to their business questions.

Enter the Semantic Layer, aka “the Knowledge Layer”

In and of itself, data has little value. It’s the knowledge that can be gleaned from your data that’s worth its weight in gold. And that’s why it’s crucial to build a layer of knowledge — the semantic layer which consists of the key business concepts and the relationships between them about your organization. The semantic layer should live in your data catalog because that is where it’s defined, explained, and mapped to the source data.

According to Domo’s CDO, Mohammed Aaser and’s Principal Scientist, Juan Sequeda, 97 percent of workers at most organizations are non-technical and unable to work with raw data. But these workers still need to be able to access key data to rapidly answer business questions. Within a data catalog, a semantic layer uses common business language to empower these non-technical workers to find the data they need, understand what data they’re looking at, and how it relates to all facets of your organization. 

Saving Time and Resources As You Scale

The semantic layer enables consistent, org-wide understanding and definitions of your data, ensuring everyone’s using the same internal language to describe the same thing. Not only does this make your data easier to find and understand across your business, it also prevents time-sucking duplicate work; Businesses often start data projects from scratch, even when much of the analysis has previously been done, simply because different teams within your organization use different data definitions; For example, teams may use differing terms like “Active customer” or “customer,” without understanding their internal definitions actually refer to the same thing. And often, Team A might have already done the analysis Team B is hunting for… but because they’ve defined the data differently, they don’t realize the work has already been done.

This constant reengineering and rework adds up, particularly in organizations attempting to scale up their data use and data-driven decision making. But according to Aaser, if teams simply arrived at common definitions and understanding of their data, organizations could avoid redundant work and reduce the time they spend on data projects by 70 to 80 percent. Just imagine the knowledge your data teams could unveil with all that time…

Cue the semantic layer, which dramatically simplifies queries by creating a logical view of your data using business-friendly terms. The semantic layer skirts the complexity of your vast amounts of data, empowers everyone in your organization to find exactly what they’re looking for, and ensures you’re all speaking the same data language.

A semantic layer as represented by a knowledge graph
A semantic layer as represented by a knowledge graph.

Building a Semantic Layer over Your Data

Building a semantic layer can be a complicated process. But at, we’re firm believers in making it as easy as possible by representing the semantic layer visually. This visual representation is called a knowledge graph, and — among other benefits — it provides a birds-eye view of your data’s semantic layer and how it’s all connected.