Start of Main Content

The BI market has reset.

AI, warehouse-native compute, and embedded analytics have changed what teams expect from BI, both for internal usage and customer-facing products.

As organizations modernize, one pattern is clear:

These tools are not interchangeable. They serve different architectures, different operating models, and different levels of data maturity.

This is our practical, 2026-ready view of what’s working and why.

Warehouse-Native BI for Teams Building a Real Foundation

Sigma remains one of the strongest options for organizations that want governed, scalable analytics that live directly on the warehouse.

Why teams choose Sigma:

  • True warehouse-native compute (no extracts, no copies)
  • Spreadsheet-like interface with real modeling depth
  • Best-in-class governed embedded analytics
  • Database write-back via input tables
  • AI Apps that integrate workflows directly with warehouse data
  • Solid NLP and AI features for internal teams

Sigma is a strong fit when you need:

  • Governed self-service that scales
  • Finance, planning, and executive reporting
  • Unified metrics with transparent lineage
  • Embedded dashboards with granular permissions

If you want the safest “do it right the first time” BI foundation, Sigma is a very strong choice.

The Fastest Path to AI Querying and Embedded Product Analytics

If embedded analytics or NLQ needs to feel like a real product experience, Omni is leading the field.

Why teams are choosing Omni:

  • Best-in-class NLP querying today
  • Native dbt integration with a seamless semantic layer experience
  • Multi-tenant embedded analytics
  • Developer-first APIs and workflows
  • Flexible, governed semantics
  • Rapid iteration for data and product teams

Omni excels when you need:

  • A clean, version-controlled semantic layer
  • Embedded analytics inside SaaS products
  • Multi-tenant delivery
  • AI-native exploration for business users

If embedded analytics, product intelligence, or NLQ are priorities, Omni is neck-and-neck with Sigma on innovation.

The Underrated All-In-One Platform for Lean Teams

Domo is often misunderstood as “just BI.” It’s not.

It’s a full analytics platform in a single system:

  • Ingestion
  • Transformation
  • Orchestration
  • BI
  • Automation
  • Apps
  • Governance

For teams that want fewer vendors, faster implementation, and one system to operate end-to-end analytics, Domo is often the most practical choice.

Domo shines when you need:

  • One platform from ingestion through insights
  • Operational and automation-heavy workflows
  • Consistent governance for a small data team

If speed and simplicity matter, Domo deserves serious consideration.

Ubiquitous, Capable, and the Default for Microsoft Shops

Power BI is everywhere because it is:

  • Affordable
  • Deeply embedded in the Microsoft ecosystem
  • Reasonably powerful
  • Governance-friendly
  • Easy to distribute at scale

It’s not where most BI innovation is happening, but it remains a safe, effective option.

Best fit: organizations already standardized on Azure and Microsoft 365.

Why Teams Are Migrating Away in 2026

Let’s be direct.

Tableau:

  • Weak embedded analytics
  • Not built for AI-native workflows
  • High cost and high maintenance

Looker:

  • Innovation has slowed significantly
  • Rigid architecture
  • Poor alignment with dbt-native modeling
  • Declining customer support

Most migrations we see today are away from Tableau or Looker and toward Sigma, Omni, or Domo.

  • Internal analytics + AI-Apps for integrated workflows → Sigma
  • Embedded analytics + AI querying → Omni
  • One platform to run the full stack → Domo
  • Microsoft-first organization → Power BI
  • Currently on Tableau or Looker → It’s time to modernize

Sigma and Omni are pushing the innovation curve.

Domo is the operational workhorse for lean teams.

Power BI remains the enterprise default.

Brooklyn Data stays tool-agnostic, but we call it like we see it.

Published:
  • Domo
  • Omni
  • Sigma

Take advantage of our expertise on your next project