What is a Semantic Layer?

For an enterprise to fully harness the power of data to inform strategies and decision-making, that data needs to be accessible to as many potential users as possible--without the need for advanced data literacy. Standard metadata is easy to understand if you’re an analyst, but not if you’re the typical business user. 

A semantic layer bridges the gap by mapping complex data to common business terms so employees across a company can access data and gain critical insights without having to play data engineer or rely on IT to create reports. A key component of business intelligence, a semantic layer translates sophisticated data language into familiar terms like “product” or “revenue” to simplify querying and give users a consistent, consolidated view of data across various systems.

How an enterprise can use a semantic layer

A semantic layer makes data simpler to search for and understand, helping to streamline operations by moving away from siloed data and giving all stakeholders the ability to access, interact with, and act on data with a consistent language and representation throughout the organization. 

Because a semantic layer makes data more accessible to the typical business user, it also makes it easier for teams to collaborate on data-driven strategies in their work. With easier access to data, enterprises could use semantic layers to:

• Consolidate customer data to drive strategies for marketing campaigns.
• Predict customers who are likely to churn and deploy resources to them.
• Create price forecasting tools to boost sales.

The downsides of a semantic layer

There are some downsides to creating a semantic layer, as the process can be labor-intensive:

• It needs to be built, and once it’s built it requires regular maintenance and management--particularly in terms of keeping it synced with databases as those databases update and change.
• Bringing together data housed in separate silos throughout a business isn’t an easy process. In order to connect disparate systems, semantic layers need to be implemented into each of them. 

Despite these drawbacks, the financial benefits of making an enterprise’s data unified and simple to interact with make it the smart choice for most businesses. 

A semantic layer is a key piece of business intelligence

By translating data into language that actually means something to the average business user, a semantic layer empowers all stakeholders in an enterprise to actually use the data that’s being collected. In effect, a semantic layer becomes a force multiplier--by increasing the number of team members who are looking at data, the number of insights garnered from that data also increases. Semantic layers are a critical piece of successful data-driven enterprises, and a continuous intelligence solution like Scuba Analytics can make maintaining them simpler because we can:

• Lessen the complexity of managing multiple databases and refreshing semantic layer UIs.
• Combine multiple disparate data sources in one place.
• Lessen ETL processes--data can be processed raw/as-is.
• Speed up time to insight.

Learn more about Scuba Analytics here.