Deliver reliable, personalized embedded analytics with the dbt Semantic Layer

Too often, valuable data is stuck in internal dashboards or buried in databases, out of reach for customers, partners, and decision-makers. While it powers internal insights, it rarely delivers value externally.

Today’s data consumers want more. They want personalized dashboards that show their specific purchasing trends, reports with individualized performance metrics, and they want these insights wherever they consume data. These experiences build trust and drive engagement, but they’re challenging for data teams to deliver.

Personalization at this level often requires significant time, effort, and resources. Custom dashboards, for example, require aligning metrics, designing APIs, and coding to ensure reliable backend data for front-end visualizations. It’s a complex process, and mistakes easily slip through the cracks and impair customer experience and trust.

The challenges of traditional BI for personalized analytics

Traditional BI workflows for delivering personalized analytics often add complexity, relying on fragmented processes that slow development and increase the risk of inconsistencies. Data preparation is handled in one tool, where datasets are selected and organized to meet visualization requirements. Custom APIs are then created in another tool to retrieve the necessary data. Finally, the data is manually assembled, parsed, and bound to visualization libraries. This disjointed workflow not only slows progress but also introduces errors at every step.

This disjointed approach introduces inefficiencies and risks errors at every stage. For example, changing a report from monthly to yearly aggregates requires updates to data preparation, APIs, and the front-end—delaying projects and increasing maintenance costs.

Then there’s the challenge of metric consistency. Traditional BI tools often store metric definitions in silos, leading to discrepancies and confusion about which metrics are accurate. These inconsistencies erode trust in data, frustrate users, and complicate decision-making across the organization.

Centralize your metrics and deliver them anywhere

To unlock the full potential of your data, businesses need a centralized, governed source of truth for metrics—one that ensures consistency and can be easily embedded across multiple applications.

The dbt Semantic Layer makes this possible. It simplifies and accelerates embedded analytics development by enabling data teams to define and manage complex metrics centrally. From there, teams can deliver reliable, version-controlled, and personalized analytics to downstream tools quickly and cost-effectively.

"The dbt Semantic Layer gives our data teams a scalable way to provide accurate, governed data that can be accessed in a variety of ways—an API call, a low-code query builder in a spreadsheet, or automatically embedded in a personalized in-app experience. Centralizing our metrics in dbt gives our data teams a ton of control and flexibility to define and disseminate data, and our business users and customers are happy to have the data they need, when and where they need it."
- Hans Nelsen, Chief Data Officer at Brightside Health

How the dbt Semantic Layer powers embedded analytics

Here’s how the dbt Semantic Layer for embedded analytics helps you overcome common challenges and deliver reliable, personalized analytics without the hassle of repeatedly reconfiguring your backend.

1. Centralize metric definitions

Define your metrics and logic once, including complex metrics such as advanced calculations, aggregations, or time-based logic. Reuse them across your tools or web apps. This ensures consistent and accurate data, eliminates conflicting metrics, and provides a governed foundation for reliable reports and visualizations personalized for any user.

2. Speed up embedded visualization development

Building custom endpoints for each visualization can be a bottleneck. With the dbt Semantic Layer, standardized data models, SQL generation, and developer-friendly APIs eliminate the need for bespoke backend infrastructure. This streamlines development, enabling teams to focus on delivering engaging user experiences that delight end users.

3. Streamline queries in downstream tools

With our developer-friendly APIs and SDKs, you can dynamically generate filtered views and user-specific dashboards without creating separate endpoints for each use case. For example —using our beloved Jaffle Shop workflow— let's say you're the owner of the Jaffle Shop Pennsylvania region. Using the dbt Semantic Layer, you can effortlessly display all purchases in Pennsylvania or provide a customer with their order history over time. This approach simplifies development, reduces complexity, and ensures cost-efficiency as your data needs grow. For organizations managing complex data workflows or serving diverse user bases, this is a game-changer—it simplifies maintenance and enables seamless updates to metrics or logic without disrupting downstream applications.

4. Improve analytics performance

The dbt Semantic Layer uses several layers of query caching to increase performance of commonly run queries. This means your system can deliver responses faster without reprocessing the same data repeatedly. By reducing the strain on your database, query caching not only speeds up response times but also ensures scalability as user demand increases. This keeps your analytics fast and reliable.

Accelerate the development of personalized analytics with the dbt Semantic Layer

By adopting the dbt Semantic Layer for your embedded analytics, you can bypass cumbersome workflows and fragmented development cycles. Instead of managing changes across multiple data models, APIs, and front-end components, you gain a centralized, streamlined system that simplifies data management and accelerates development.

Discover how customers are using the dbt Semantic Layer to transform their analytics. Watch our Coalesce session with Bilt Rewards ‘Making data rewarding at Bilt’ to see how they deliver efficient, trusted analytics in their customer-facing apps. Or, check out our on-demand webinar ‘Scaling embedded analytics with Brightside Health’ to learn how they provide personalized analytics to both patients and providers, empowering better medical care.

If you're looking to simplify your analytics workflow, deliver consistent metrics, and deliver personalized embedded analytics fast, try the dbt Semantic Layer for your embedded analytics strategy today.

To get started building out your dbt Semantic Layer configurations, check out our documentation, and reach out in Community Slack (#dbt-cloud-semantic-layer) if you have any questions.