Pros and Cons of BI tools on the market

A common question we get asked is…..which BI tool should my company use? And the answer is, it depends. To help you get a feel for the pros of cons of some of the more popular BI solutions on the market we put together this list. We’re happy to rundown additional options and walk through your specific situation in a consultation.

Power BI

Pros:: Power BI has quickly become a dominant player in BI and analytics. Its integration with Office 365, flexible back-end deployment options, and affordable Power BI Pro licensing make it a common choice for many companies. The tool can be deployed in various ways, including Import, DirectQuery, LiveConnection, or Dataflows. Microsoft Fabric, introduced this year, further strengthens Power BI's position within Microsoft's Data Analytics platform.

Cons: Power BI is best suited for companies already invested in the Microsoft stack. While hybrid approaches like Snowflake on Azure to Power BI are possible, integrating Power BI into cloud architectures like AWS or GCP can be challenging due to limited native integration.

QuickSight

Pros: Amazon QuickSight offers several advantages, particularly for organizations already using AWS services. Its seamless integration with AWS tools like Redshift, S3, and Athena makes it easy to connect and manage data. The serverless architecture provides excellent scalability, and its pay-per-session pricing model offers flexibility for managing costs. QuickSight also includes built-in machine learning features, such as anomaly detection and forecasting, allowing users to perform advanced analytics without needing deep technical expertise. The tool's user-friendly interface makes it accessible to both technical and non-technical users.

Cons: It offers fewer customization options compared to other BI tools like Tableau or Qlik, which may not suit users needing highly tailored dashboards and visualizations. Additionally, its data connectors are mostly limited to AWS services, making it less ideal for organizations with diverse data sources outside of AWS. Performance can also be an issue with complex queries, and advanced features may require a steep learning curve. Lastly, QuickSight lacks the sophisticated reporting capabilities found in other tools, which may be a drawback for businesses with advanced reporting needs.

Qlik

Pros: Qlik excels in time-to-value when building an application from scratch to production. Its associative data model is one of the most powerful features in analytics tools. Qlik has been significantly investing in its SaaS platform and advanced analytics, particularly its search-based capabilities.

Cons: Unlike AWS (QuickSight), Azure (Power BI), or GCP (Looker), Qlik doesn’t have a native back-end cloud platform. It’s still unclear how this will affect Qlik’s long-term direction, given the industry's drive toward convergence. However, Qlik is trying to fill this gap with Qlik Replicate and the acquisition of Talend to form a comprehensive end-to-end data platform.

Sigma

Pros: Sigma simplifies data analysis for business users with a familiar spreadsheet interface. It’s particularly effective for direct discovery use cases and offers excellent scalability on cloud data platforms. Features like input tables, text generation, and easy embedding make it a user-friendly option for organizations.

Cons: Sigma’s performance depends heavily on the performance of the cloud data platform. It requires solid due diligence at the warehouse or lakehouse layer to optimize and build dimensional models. While Sigma integrates well with Snowflake, Databricks, BigQuery, Redshift, or PostgresSQL, it doesn’t align well with the Microsoft ecosystem.

Looker

Pros: Looker sets itself apart as a modern data platform, ideal for self-service, guided, and embedded analytics. Its semantic layer (LookML) allows for SQL generation, promoting true data governance and version control. Changes made once propagate throughout, making collaboration easier, especially for developers.

Cons: Looker depends on a database or data warehouse, so it’s not ideal if you rely on spreadsheets as your primary source. Performance is tied to your data warehouse, meaning query optimization within that layer becomes critical. Since Looker's acquisition by Google, the product direction has become somewhat unclear, especially with the merging of Looker and Google Data Studio.

Tableau

Pros: Tableau is widely considered the leader in data visualizations and front-end aesthetics. It has a long history and a robust user base. Tableau Prep has improved the tool’s ETL and data preparation capabilities, previously handled by Alteryx.

Cons: Tableau struggles with embedded analytics and needs a strong data warehouse component or intermediary layer to scale effectively. Although alternatives like Tableau Prep, Knime, or Alteryx can help, this means data logic remains contained within the tool rather than a centralized, governed data warehouse. Tableau has also seen innovation slow down following Salesforce's acquisition.

Selecting the right BI tool goes beyond a checklist of features. Understanding your company's data environment, scalability needs, and long-term goals is crucial. A proof-of-concept can reveal how well a tool aligns with your business needs. Also, take into account the growth trajectory of the tool and how easily your team will adopt it. We’re around to help wade through all the options.