Comparing the best BI tools: Select the Right Solution for Your Business

Data can be compared to hidden treasure. Interesting insights and outliers are buried just beneath the surface but not easily detectable by the human eye. Artificial intelligence (AI) and machine learning (ML) tools act as a treasure map to navigate uncharted data and reveal valuable information.

The modern day Business Intelligence (BI) tool combines a company’s data, AI, and ML in one platform. But with a cluttered BI landscape, it can be challenging for organizations to find the right tool for their needs.

In this article, we address why these tools are so important, their capabilities, and compare some of the most popular in the market: Tableau, Power BI, Looker, Sisense, AWS QuickSight, Domo, Qlik and GoodData.

1. What are BI tools?

Generally speaking, BI refers to the combination of strategies, technologies, and services necessary to transfer raw data into valuable insights to support decision-making processes.

A BI tool is a piece of software that helps companies manage BI-related processes, such as connecting data sources to analytics tools, creating insights and dashboards, and sharing them with relevant business team members (users and stakeholders). Such tools support companies' daily workflow and help teams to effectively react to different situations.

2. Why is it important to choose the right BI tool for your business?

Choosing the right BI tool and consolidating data in one accessible location is crucial for several reasons:

• Decision-making can be significantly enhanced through the core attribute of augmented analytics. It leverages machine learning and natural language queries (NLQ) to automatically generate valuable insights, weaving them into compelling data stories and providing a wide range of interactive visualization options. This empowers decision-makers to make more informed choices.
• Productivity can be increased through the streamlining of processes and workflows, supported by multiple data source connectivity and the creation of analytic models. This includes the ability to centrally manage one version of the analytics, and the capability to connect a wide range of data sources.
• Reusability can encourage the efficient reuse of data and reports. Facilitated by a unified metrics store, the capacity to auto-deliver the user interface saves time and enables effortless replication of valuable insights and dashboards. This is further supported by an “as code” architecture and seamless integration with third-party tools for extensive analytics.
• Future-proofing capabilities ensure a company’s analytics solution is prepared for future data challenges, technological advancements, and further integration with third-party tools, ensuring long-term sustainability.
• Cost-effectiveness is also important to keep expenses in check while maximizing the value of your data analysis. It is crucial to ensure that the BI tool ownership is motivated to continually innovate and improve the platform. Additionally, guaranteed security options and reliable customer support contribute to a comprehensive and valuable solution.

To successfully evaluate a BI tool, it’s important to determine the most important characteristics. For more information about features to look out for, check out our e-book How to Choose the Best BI Tool. This provides detailed explanations of key capabilities that can help streamline your business.

3. Comparing the best BI tools

Your choice of analytics platform directly impacts employee and/or customer happiness and, consequently, the future success of your organization (not only today but for many years to come).

To help with your initial evaluation, the tables below compare the key capabilities of the major BI and analytics tools on the market today. For a more detailed comparison of individual tools, take a look at our AWS QuickSight alternative, Sisense alternative, Tableau alternative or Qlik alternative.

Architecture

100% Cloud-native (Docker & Kubernetes)
Real-time analytics without feature limitations
Exposed semantic data model as a shared service
Open APIs, SDKs, and standard protocols usage
Integrating with other BI and AI/MLs, developer notebooks, and other third-party applications
All analytics definitions are declarative and they can be exported, imported, versioned, shared, and inherited.
Super-high performance, intelligent analytics cache
Integration with leading process management workflow software (or built-in feature) to drive decision intelligence
Composable & reusable metrics
Strong support for multitenancy

Dashboards

Easy-to-use self-service capabilities for non-technical users
Intuitive drag-&-drop UI

Embedding

SSO & white labeling
iFrame
Javascript library to embed and customize anything you need as React, Angular, or Vue components
Web components

Scaling and Change Management

Automated scaling to thousands of user groups — departments, teams, and clients
Streamlined change management to roll out changes to thousands of user groups without breaking their customizations
Possibility to change the underlying data warehouse without breaking your data model, metrics, or dashboards

AI/ML features

AI/ML options for all analytics user personas and skillsets
Natural Language Query (NLQ) features like search, auto-complete, and in-dashboard chat
One-click ML forecasting, clustering, and key driver analysis for non-Data Scientists
Embedded Jupyter notebook integration
Personalized insights surfaced directly from the “exploration” home page

Data Integration

Manual & Automated CSV upload
Use your own data warehouse (e.g., Redshift, Snowflake, BigQuery, PostgreSQL)
Industry-first, Analytics Lake stores analytics-ready artifacts such as metadata, semantics, and pre-aggregated metrics

Data Security and Compliance

End-to-end compliance & security — from data warehouse to visualizations
SOC 2, ISO 27001:2013, CCPA, GDPR & HIPAA

Deployment Options

Fully hosted — managed in the cloud by the provider
Self-hosted — deploy to Amazon Web Services, MS Azure, Google Cloud, or on-premises

GoodData offers access to third-party resources and provides seamless integration with external applications, including Jupyter Notebooks and other BI tools. This enables sophisticated machine learning applications and advanced predictions, thereby expanding the platform’s capabilities and effectively catering to a wide range of use cases.

Thanks to a consistent (single source of truth) metrics store and semantic layer, all the data, expressions, metrics, and KPIs remain the same when you use these other tools (developer notebooks, other BI or AI tools, etc.)

Flexible pricing model

GoodData offers a transparent and flexible pricing model tailored to meet the needs of different customers. Customers are given the following options to purchase licensing, depending on what works best for their use cases.

• Workspace pricing: Flexible workspace pricing allows for customization based on the inclusion of specific features and services. This flexibility aligns pricing with unique organizational needs to ensure value for each workspace and chosen features.
• User pricing: An affordable user pricing model, enabling teams to start small and scale up as the content becomes more valuable and usage grows.
• Cost saving: GoodData stands out from other vendors as it does not include a native cloud data warehouse (which can lead to increasing data warehousing costs) in its analytics stack. Instead, GoodData reduces costs through an advanced caching layer built on Apache Arrow.

Ready to learn more?

To further explore this topic, check out our ebook covering best practices for launching BI and analytics. To learn more about the GoodData platform’s capabilities, sign up for a trial. Alternatively, request a demo, where our team will guide you through the platform and answer your questions.