Why Omni over ThoughtSpot?

Both Omni and ThoughtSpot are designed to provide self-service access to data and enable business users to get quick insights.

But ThoughtSpot’s rigid search-based interface only allows you to answer simple business questions, and becomes a blocker for users trying to do more complex analyses. For users who prefer spreadsheet interfaces or to write raw SQL, ThoughtSpot does not support these workflows and you have to export and switch to a separate tool.

In Omni, users can seamlessly switch between natural language queries with AI, spreadsheet UI with Excel formulas, writing raw SQL, or a point/click interface. Every user in Omni can explore and model data in the way that works best for them.

In addition, it’s difficult to ensure consistent metrics and accurate AI answers in ThoughtSpot, which can quickly lead to chaos. There is no central semantic layer, so teams will often end up with different (and conflicting) answers to the same question.

In Omni, the built-in semantic layer means everyone is using a shared metrics foundation, but users are still free to explore data and do ad-hoc analyses. If what they build is useful, they can easily promote it to the shared model so it’s accessible by the entire team.

ThoughtSpot has extensive AI features, including AI-powered insights and summaries. In theory, this is very helpful in enabling your business stakeholders to answer questions independently.

Unfortunately, ThoughtSpot’s data modeling architecture is not well suited to enable accurate or reliable AI answers which can easily create more problems than it solves. Despite the addition of their “Agentic Semantic Layer for AI”, ThoughtSpot still lacks a central shared data model. This means it’s difficult to create a central source of truth for AI context; and without the right business context, AI won’t be able to accurately interpret metrics and provide accurate insights.

On the other hand, Omni’s shared semantic layer makes it easy to curate relevant definitions, business context, and context for AI, so that data teams and their stakeholders can trust the answers. This frees up data team bandwidth to move from ad-hoc requests to more strategic, impactful products.

dbt is excellent for defining core data models that need to be versioned, tested, and reused across products. But not every use case fits cleanly into those buckets. In practice, business teams constantly need new metrics or variations to answer ad-hoc questions. When every change requires a dbt update and deployment, analytics teams become a bottleneck and iteration slows down.

dbt transforms data before it lands in downstream tools. That’s critical for performance, applications, or workflows that depend on those models. But this approach doesn’t easily support the smaller units of logic needed when exploring, filtering, drilling, or slicing the data at query time.

Omni’s semantic layer is complementary to dbt. It lets analysts define reusable pieces of logic and combine them at query time rather than waiting on a dbt deploy. This gives teams flexibility to iterate and answer business questions quickly. And when an ad-hoc metric proves valuable, it can be promoted to Omni’s shared data model or pushed to dbt — enabling flexibility without sacrificing governance.

Omni's semantic layer also creates a foundation for AI to deliver trustworthy responses. You can leverage descriptions, AI-specific context, and other metadata from dbt to inform AI answers.

Both ThoughtSpot and Omni let you explore data and create visualizations in a UI without using SQL.

However, ThoughtSpot does not give you the option to switch to SQL, even if it would be faster or easier. You’re stuck in a rigid UI with pre-defined options, which can make it difficult to get exactly the result you want.

In practice, this means if you use ThoughtSpot to enable self-service for business stakeholders, you’ll need another separate tool for your more technical users. In addition, ThoughtSpot lacks spreadsheet functionality, so many of your business users will end up exporting data into Excel, further increasing fragmentation.

With Omni, you don’t have these limitations. Everyone, from business users that have only ever used Excel to your most technical data scientists, can work in the same tool – in exactly the way they want. And they can seamlessly switch between different interfaces at any time. For example, you can start exploring and curating data via natural language chat, and then switch to Excel formulas or SQL to generate additional fields — all within the same workflow.

Omni and ThoughtSpot can both help increase self-service data access for business users, which is great for busy data teams.

In practice, however, the lack of a central data model in ThoughtSpot means that your data team has to field questions from business users who are confused by inconsistent metric definitions, conflicting reports, or unreliable AI. In addition, business logic developed in ThoughtSpot cannot easily be pushed down to dbt or your data warehouse, so it’s inaccessible for other applications. This leads to fragmentation and/or duplicative work.

Change management in ThoughtSpot is also difficult, leading to more work for your already stretched data team. For example, ThoughtSpot’s basic native version control features make it challenging to stay on top of changes or develop collaboratively. Editing the system-wide data model happens locally via file downloads, so you’ll need to stay on top of countless versions spread across multiple users’ machines. And if any underlying data changes, you’ll need to manually update every dependent asset like tables and liveboards to prevent content from breaking.

Omni, in contrast, is built to free up bandwidth for your data team. The shared data model helps ensure metrics consistency and governance across every UI and workflow — from AI and spreadsheets to point/click and SQL. This results in fewer questions about discrepancies from business users, and more time for the data team to focus on strategic work.

In addition, Omni’s architecture streamlines your everyday workflows. You can push metrics developed in Omni to dbt to make them universally accessible and improve performance, and update your schema and references in bulk with Omni’s content validator if something changes in the data sources.