Exclusive: Data governance startup Euno emerges from stealth to manage business logic at scale

Organizations use data from many different sources, feeding data pipelines that help to power business intelligence, operations and, increasingly, AI workloads. Having consistent, clear governance for how the business logic should work for those data pipelines is a challenge that startup Euno is taking aim at.

Euno is emerging from stealth today with a product platform and $6.25 million in seed funding. The company was founded by Eyal Firstenberg, who previously founded cybersecurity startup LightCyber, which was acquired by Palo Alto Networks for $105 million; and Sarah Levy, who former CTO at Sight Diagnostics.

Through their experiences, Euon’s founders realized that there were many problems with managing data pipelines that needed to be solved. Among the most significant: Understanding how to manage business logic at scale.

Business logic is all about definitions and calculations for core business operations terms used by an organization — for example, active users, transactions and other key performance indicators (KPIs). Business logic is critical for enabling an accurate data model, which can then serve as the foundation for data driven operations.

“We’re focused on the challenge of managing and aligning a consistent data model at scale,” Levy told VentureBeat in an exclusive interview.

Why business logic matters (and why it’s often hard to get right)

Business logic is critical to enable organizations to get the most accurate results for data analytics and business intelligence (BI), but it’s often a challenge to manage properly.

Levy noted that, as organizations look at different business dashboards generated by different tools, there can often be very different numbers for identical terms and concepts.

“I think it’s a classic thing every CEO struggles with, when they get a total sales number from finance, sales and marketing and they get different numbers and no one knows why,” she said. “The data model that holds the business logic is very important if you want to trust the results data products deliver, and managing a consistent data model at scale is actually quite a challenge.”

Levy explained that the modern data stack consists of the consumption layer with data analytics and BI tools, a data layer and a data warehouse. She noted that many organizations are managing a data layer that is outside of a BI tool in an effort to make sure that data logic is not siloed and locked into a specific tool.

Euno’s solution focuses on mapping business logic across platforms, providing a catalog of definitions and automating the shift of logic from BI tools into the data layer. Euno works with a growing number of data warehouse and BI tools, including Snowflake, Databricks, Amazon and Redshift.

Governing and modeling data build tool (DBT) for business logic

Business logic is not just about having a data glossary that defines what various terms mean. The use of a data glossary is common inside of data intelligence and data catalog tools like Alation and Collibra that provide organizations with a way to organize and understand what data is available.

“What we’re introducing with Euno is a novel approach to data model governance, which has some overlap with the traditional approaches to data catalogs,” said Levy.

She pointed out that data catalogs often capture the semantic definition for data terms, which is important. For data models, it’s also possible to capture business logic as a data transformation, with the use of the data built tool (dbt), which is a widely used open-source technology. Data transformations involve the manipulation of database tables so that values can be computed through different table joins, filters or computations.

What Euno is able to do is capture any new business logic and code it automatically in dbt, said Levy. Euno will also add new logic to a central data model.

“We are mapping business logic across all your platforms,” said Levy. “So that’s in dbt, in the warehouse and in all your BI tools; we are automatically mapping business logic, introducing a business logic catalog that includes all the calculations and the descriptions, the lineage view of your business logic and usage scoring for every metric or table in your data model.”

Levy also noted that having an accurate data model is an essential component building the foundation for self-serve analytics tools to work reliably.

“We view ourselves as a tool that is designed to build the foundation for those self-serve AI driven analytics tools to actually work and be trustworthy,” she said.