Mode Analytics Partners with dbt Labs to Co-create the Future of Modern Business Intelligence

Mode Analytics, the modern Business Intelligence (BI) platform for scaling businesses, announced its launch partnership with dbt Labs for the newly unveiled dbt Semantic Layer, announced this morning in public preview in a keynote session at Coalesce, dbt Labs’ annual conference. Mode’s deep integration with the dbt Semantic Layer makes consistent, governed metrics instantly available for exploration via a no-code interface, enabling organizations to centrally define and ensure consistency of key business metrics.

“The dbt Semantic Layer gives customers a central source of truth for their business-critical metrics, and the ability to explore them from tools like Mode,” said dbt Labs Chief Product Officer Margaret Francis. “Through this partnership between dbt Labs and Mode, organizations will be able to enable collaboration between their data and business teams, accelerating time to value for their investment in dbt.”

Recommended AI: Microsoft 365 Security Features Protect Business Data from Evolving Threats

By defining key business metrics once, in dbt, and making queries in the Semantic Layer, data teams are able to uplevel their ability to collaborate with business teams on highly-leveraged work. As a platform already designed for bringing data teams and business teams closer together, Mode is the natural home for metric analysis, visualization, and exploration.

“This is a huge step forward for the data ecosystem, because it means that analytics engineers can define metrics in one place, and surface those metrics consistently to business stakeholders in a matter of minutes,” said Benn Stancil, Co-founder and Chief Analytics Officer, Mode Analytics. “It’s a relief for data leaders looking to reduce time spent answering questions about why data doesn’t match, and invest more time collaborating with business teams on analytical work that will generate understanding of the key drivers behind the metrics.”

Mode’s integration extends metrics centrally defined in the dbt Semantic Layer into BI, making them instantly available to the rest of the business for code-free reporting. Based on the configuration defined by analytics engineers, business teams can explore the metrics by choosing filters, dimensions, and time grains in a familiar drag-and-drop interface. The differentiation of Mode’s integration comes from the platform architecture, which turns its proprietary computational engine into a highly-performant interface to the Semantic Layer. By passing the data through the Mode engine and relying only on the logic in dbt, Mode delivers trusted metrics, reporting, and insights throughout an organization, while preventing potential mistakes such as aggregating aggregates, or summing distinct values.

Recommended AI: Consider Your DOOH Buying Methods Wisely: Direct Sales vs. Programmatic Buying

Mode creates the guardrails analytics engineers need to confidently enable their business teams to answer their own questions, and ensures that the metric is reported correctly, every time. If a metric exploration uncovers something unexpected, and analysts are called on to collaborate on ad hoc analysis, they can immediately access the SQL query generated by the dbt Semantic Layer logic, and start expanding from there, turning the dbt metrics into a starting point, rather than a dead end.

“Creating and maintaining accurate metric definitions is challenging because our business logic lives in so many places like queries and one-off docs,” said Trish Pham, Head of Analytics, at PayJoy. “Now, we can define those metrics in one place, in the dbt Semantic Layer, and have that data easily and consistently accessible to our business teams in Mode.”

Recommended AI: Understanding the Role of AI in Gaming

[To share your insights with us, please write to sghosh@martechseries.com]