1) Tableau
Pros
Very sophisticated visual analytics & interactive dashboards.
Large community, many training opportunities.
Well suited for storytelling and management reporting.
Cons
License costs in enterprise rollout are higher than with Power BI.
Modeling/governance is not always consistently solved — requires experienced BI professionals.
Creating dashboards can become complex when many stakeholders are involved.
Conclusion – suitable for:
Companies that need professional visualizations and management dashboards and are willing to build or purchase dedicated BI expertise.
2) Scavenger AI (Text-to-SQL + Semantic Layer + NL Analysis)
Pros
Natural language → precise, understandable analyses in seconds.
Understands business logic (customers, products, margins), rather than just tables.
Specifically built for mid-sized & European companies (including GDPR & EU hosting).
Minimal setup time — no BI tool training required for users.
Cons
Younger than established BI suites — ecosystem & templates are just growing.
For very complex dashboard layouts, may need to be supplemented with traditional BI tools.
Conclusion – suitable for:
Companies that want to make informed business decisions quickly without BI expertise — especially finance, sales & production in the mid-sized sector.
3) Qlik Sense
Pros
Associative Engine: Very good for exploratory analyses, quick pattern recognition.
Strong self-service analysis for departments.
Good scalability and governance options.
Cons
Unusual analysis paradigm: One has to relearn.
Platform setup & governance require data/BI roles.
Licenses & app sizes need to be well planned.
Conclusion – suitable for:
Teams that work a lot exploratively (e.g., Sales, Operations) and frequently ask questions like: “What affects what?” instead of just looking at static reports.
4) Looker (Google Cloud)
Pros
Central Semantic Layer: clear, reusable KPI definitions.
Ideal for Governed BI in larger organizational structures.
Very good embedded analytics capabilities.
Cons
Introducing LookML requires modern data modeling skills.
Generally makes sense only in warehouse/MODERN-DATA-STACK architectures.
Strategic commitment to the Google ecosystem.
Conclusion – suitable for:
Companies that need scale, uniform KPI definitions, and BI as a central product — e.g., SaaS companies or data-driven mid-sized firms/enterprises.
5) Sigma Computing
Pros
Table interface like Excel, but directly live on the data warehouse.
Very good for departments without SQL skills that still work in a structured way.
Particularly strong in combination with Snowflake / BigQuery / Redshift.
Cons
Works best only then when a modern cloud warehouse is available.
Governance/modeling quality still depends on data team skills.
Visualization options are okay, but often less "glamorous" than Tableau.
Conclusion – suitable for:
Companies that come from Excel but want to access cloud data — without having to chase every request through the data team.