Business User Workflows on Elementary

Data needs to be ready for use when the business needs it. When it isn’t, engineers become the default support layer for every question about finding the data, understanding it, and knowing whether it can be relied on.

Most data quality and observability tools were built for engineers. Business users can see when something looks off, but validating logic, understanding impact, or answering simple questions still depends on SQL, dbt, and engineering time. To compensate, analysts run their own checks, creating duplicate work and misalignment around what “good data” means.

In this webinar, we’ll show how Elementary enables business users to trust, understand, and work with data independently, without having to rely on engineering for everything.

You’ll see how business users can:

• Trust the data, with shared visibility and health scores
• Safely enrich data with business validations, no SQL or dbt skills required
• Find the right data and understand it, using natural language
• Investigate issues with AI, and get clear answers about what’s happening
• Apply governance consistently, aligned with engineering-defined rules

Under the hood, this is powered by a single control plane, a shared context engine, and AI agents that bring observability, quality, governance, and discovery together. The control and guardrails remain in the engineers’ hands, while enabling observability, quality governance, and discovery to scale across the entire organization.

‍