20 top business intelligence tools to know about in 2025

1. Alibaba Cloud Quick BI

The largest public cloud provider in the Asia-Pacific region, Alibaba Cloud offers a wide range of data management and analytics technologies, from SQL databases to advanced AI tools. Quick BI is its native business intelligence service, with a particular focus on data visualization and real-time analytics capabilities. A workbooks feature is also built into the software. It lets users filter and aggregate data for analysis in a familiar spreadsheet environment rather than having to learn a query language.

In addition, business intelligence dashboards built with Quick BI can be brought together into a single portal using a drag-and-drop interface. This enables organizations to create centralized and collaborative analytics systems from dashboards built by users in different departments.

Quick BI is integrated with the rest of the Alibaba Cloud ecosystem, and the service's pricing is competitive with the cost of other BI tools. Alibaba has a limited presence outside Asia-Pacific, but Quick BI and its related cloud services are often chosen by companies based there or subsidiaries of other organizations that operate in the region.

2. Amazon QuickSight

Like Quick BI, Amazon QuickSight is a business intelligence tool native to its vendor's own cloud ecosystem -- in this case, the AWS platform. QuickSight is built on a serverless architecture, enabling high scalability with minimum effort for an IT department. It also offers integration with other AWS technologies to support advanced capabilities, including machine learning and natural language processing features.

Although limited to AWS cloud deployments, QuickSight is popular with businesses that already process and store their data on the platform. Similarly, built-in embedded analytics capabilities are popular with SaaS vendors that run their software on AWS. It's easy for them to embed reports, dashboards and data analysis features into their operational applications.

QuickSight is cost-effective for relatively simple BI use cases. However, the more advanced features do have dependencies on other AWS services, such as Amazon SageMaker for creating machine learning and AI models. This can push usage costs up. For a long time, the data visualizations included with QuickSight and the user experience as a whole felt dated. But recent releases have improved the visualizations and the look-and-feel of the UI.

3. Domo

In the past, Domo's marketing strategy for its namesake BI platform focused on senior executives. The usability of the cloud-based software's analytics features reflects that. The tool can be used to create interactive and real-time dashboards as well as mobile apps to deliver data and generated insights to business users. For example, Domo is also now popular with sales and marketing teams for apps that can be used on the road or to support marketing campaigns.

The underlying infrastructure that makes this possible includes a cloud-native data warehouse; a large library of prebuilt data connectors to databases and applications; and sophisticated tools for running extract, transform and load (ETL) processes to integrate and prepare data sets.

While the extensive connectivity makes some of the initial stages of BI application design and development efficient, configuring the full Domo ecosystem can be complex. However, Domo has simplified this somewhat by opening up its platform to technology partnerships. As a result, its software can be integrated with existing data warehouses and data catalogs more easily now.

4. GoodData

GoodData has built its reputation as a BI vendor that specializes in embedded analytics for building dashboards and reports into other applications. As a result, it emphasizes features to be used in this way, such as a metrics store for sharing KPIs and a semantic layer that enables annotated data models to be reused by different teams of users and in multiple applications.

In addition, GoodData has implemented analytics as code functionality. This doesn't mean BI analysts and other users of its platform need to become software developers. Instead, analytics objects, such as measures, dimensions and hierarchies, are saved as code. They can then be version-controlled and collaborated on in much the same way as developers do with other software projects.

To support this model, GoodData deploys an "analytics lake" architecture that stores data but is also a repository for code-based analytics objects. This can make applications portable and also helps with compliance and governance issues because the entire analytics environment is managed in one place.

Because it focuses on embedded analytics, GoodData has a smaller market presence in traditional BI deployments. It also supports fewer of the capabilities that such users might need, such as data exploration, data profiling and integration with data catalogs.

5. IBM Cognos Analytics

One of the first true business intelligence tools, IBM Cognos Analytics has been around for more than 25 years and has been owned by IBM since 2008. In keeping with that background, the software is strong in traditional enterprise reporting and related features, including the ability to distribute reports in PDF files, emails and numerous other formats. This makes it attractive for more conservative businesses or organizations that have specific reporting requirements it meets.

Cognos Analytics is a component of IBM's broader analytics and AI portfolio as well as integrates with IBM Decision Optimization, a set of prescriptive analytics tools. Indeed, new adopters of Cognos Analytics often select the software for its decision intelligence capabilities, which are designed to help improve data-driven decisions. These features enable users to create budgets and strategic plans, set up KPIs to monitor them and regularly report on their status.

IBM Cognos Analytics is popular in industries such as finance, government and healthcare, but it lags behind rival BI tools in contemporary features for business analysts and other users. It also has limited adoption in the cloud.

6. Incorta

The Incorta platform emphasizes rapid data ingestion and analysis for near-real-time insights on raw operational data. Built on top of a data lakehouse, Incorta provides self-service analytics tools that can be used to track KPIs, do ad hoc analysis and create data visualizations.

Prebuilt applications are a notable differentiator for Incorta. They include dashboard templates, sample reports and data schemas for widely used business applications to give users a jumpstart in common analytics scenarios. These prebuilt offerings come from outside providers as well as Incorta directly.

Incorta also includes data mapping capabilities aimed at eliminating the need for ETL and data transformation processes. There are some disadvantages to its mapping of data sources, though. Many data sets aren't well formed and have variable data quality. Fixing this requires more data transformation than the tool supports.

Nevertheless, Incorta is most often chosen for its high-performance connectivity to ERP systems, such as SAP or Oracle E-Business Suite, while offering a lower price-point than the analytics tools sold by those vendors. Companies can start with a simplified version on a freemium license, enabling users to try the product at a low cost before deciding whether to adopt it widely.

7. Looker

As with BI tools offered by other cloud platform providers, Google's Looker software is tightly integrated into its own Google Cloud ecosystem. The Looker offering consists of two separate products: the original Looker, a standalone BI tool that Google acquired in 2020, and Looker Studio, which was developed internally and named Google Data Studio until it was renamed in 2022.

The Looker tool is primarily focused on embedded analytics. It has its own modeling language, LookML, and both an API and a SQL interface for developers. Looker is an attractive option for developers already building applications on Google Cloud. As Google adds various AI features, such as its Gemini large language model, to Looker, the platform is also becoming attractive for more advanced BI use cases.

Looker Studio is a free web-based data visualization and reporting tool for creating relatively simple dashboards and reports. It has an effective drag-and-drop interface and integrates with numerous other Google products, such as Google Analytics for analyzing website traffic.

However, support for data integration and data preparation is limited in both the Looker and Looker Studio tools. To handle those tasks, organizations commonly have to use additional Google Cloud technologies, such as the BigQuery data platform and Cloud Data Fusion integration service.

8. Microsoft Power BI

By far the current market leader in number of users and deployments, Microsoft Power BI is a highly capable analytics tool for business users. It's integrated with both the Microsoft Azure cloud ecosystem and the Microsoft 365 suite of productivity applications. A big reason for Power BI's success is that it's often bundled into broader Microsoft enterprise licensing deals, making it almost the default BI software choice for organizations adopting Azure or Microsoft 365.

Power BI now also includes augmented analytics features provided by Copilot, Microsoft's generative AI (GenAI) chatbot. For example, Copilot for Power BI can summarize reports and data insights; help users create reports; and write queries in Microsoft's Data Analysis Expressions, or DAX, language.

One difficulty users face with Power BI is the complexity of the Azure data environment. The Microsoft Fabric analytics platform aims to unify different data modeling options that reflect the history of Microsoft's BI tool set, which initially was built on top of the SQL Server relational database and SQL Server Analysis Services, an online analytical processing engine. Now, Power BI also links to a data warehouse and numerous data engineering and data science technology options in Microsoft Fabric.

Nevertheless, many users get started with Power BI on simple data sources, including Excel spreadsheets, and find its analytics and data visualization tools to be easy to use.

9. MicroStrategy

Like IBM Cognos Analytics, MicroStrategy was one of the earliest business intelligence platforms. However, MicroStrategy remains an independent BI vendor. With its long history, the company's software is a comprehensive platform for interactive dashboards, automated reporting, predictive analytics and data visualization targeting medium-sized organizations to large enterprises. It supports both cloud and on-premises deployments for flexible access, and one of the tool's strongest features is its support for mobile BI applications.

MicroStrategy includes semantic modeling features, enabling it to tackle complex analytics workflows. It also provides thorough security features. As a result, it's often used in large-scale enterprise deployments with a need for stringent governance -- for example, in the financial services and healthcare industries. The software also has a strong user base in retail, where its mobile capabilities support deployments of BI applications on mobile devices for store and supply chain managers.

Although MicroStrategy has a reputation for being a somewhat pricey platform compared with other BI tools, the MicroStrategy Cloud environment has considerably reduced entry costs for new users.

10. Oracle Analytics Cloud

Also like other BI tools that are tied to a cloud platform provider's own ecosystem, Oracle Analytics Cloud runs on Oracle's cloud infrastructure and is most suited for use with its Fusion suite of business applications. In Oracle's case, BI and analytics capabilities are integrated into the workflow of the applications, enabling decision support and actionable insights at key steps in business processes.

Oracle's business software ecosystem is comprehensive but also rather complex. However, Oracle provides prebuilt semantic models, including AI ones, plus connectors and preconfigured data pipelines to simplify the integration of different data sources for BI use cases. Specialized analytics applications for functional areas, such as HR and supply chain management, offer predefined metrics and dashboards powered by Oracle Analytics Cloud.

In addition to underpinning these heavyweight enterprise applications, Oracle Analytics Cloud includes a range of mobile analytics apps, data storytelling features, geospatial analytics capabilities and other functionality. A companion product, Oracle Analytics Server, offers similar features for on-premises BI systems and private cloud deployments.

11. Pyramid Decision Intelligence Platform

Pyramid Analytics gears its software platform to decision intelligence, not just conventional BI. For example, a virtual spreadsheet named Tabulate enables non-technical users to mash up data and perform basic analyses using familiar formula syntax. Pyramid takes this further with a Solve plug-in that offers optimization and decision modeling capabilities in the spreadsheet for simulations and what-if analysis.

Discoveries can be embedded in dashboards and data stories to share and collaborate on with other users. However, some users say the visual style of Pyramid's dashboards and visualizations look a little out of date.

Pyramid Decision Intelligence Platform includes a powerful analytics engine that drives a single data model from diverse sources and provides advanced features, such as machine learning. The engine is front-ended by an intuitive no-code UI that can be used to directly query data sources.

The Pyramid software can be deployed on-premises, in the cloud or in a hybrid architecture. This approach has been successful with customers that need a scalable, high-performance engine with a single point of management. Although not low-cost, Pyramid can be a good value compared to enterprise BI platforms from larger vendors. It has proven to be particularly appealing to SAP users.

12. Qlik Sense

Qlik was one of the first BI vendors to offer in-memory data analysis with QlikView, its initial product. This enabled high-performance analysis for business users without requiring much support from IT. Qlik Sense, its second-generation BI tool, is part of a more comprehensive data management and analytics ecosystem, which also includes a data catalog, enterprise-class data integration software, and machine learning and AI technologies.

Qlik has taken a unique approach to analytics with its Associative Engine, which lets users explore complex views of data without having to learn an advanced query language. This capability has helped make Qlik Sense popular in fields such as finance and pharmaceutical research, where business users often need to analyze detailed scenarios that would require multiple applications on other platforms.

In addition, Qlik is a cloud-agnostic vendor that supports Qlik Sense on various cloud platforms. In recent years, Qlik has grown its broader data platform by acquiring other companies. As a result, it's now primarily selling new software into existing customers rather than winning new ones in the highly competitive BI and analytics market.

13. SAP Analytics Cloud

SAP is one of the top software vendors overall, led by its ERP and finance applications. Over the years, it has taken several different approaches to analytics, including development of basic enterprise reporting software and the 2008 acquisition of early BI vendor Business Objects. Today, SAP offers a full data management and analytics ecosystem that includes SAP Analytics Cloud and SAP Datasphere, a business-focused data fabric technology that's integrated with the BI software.

SAP Analytics Cloud is also tightly integrated with SAP's S/4HANA ERP system and business applications. This enables users to explore data with detailed business context, such as how specific decision points integrate into the overall business workflow. To support such capabilities, a knowledge graph maps data from across the entire SAP ecosystem for use in analytics, simulation, budgeting and planning.

Naturally, SAP Analytics Cloud is adopted only by SAP customers. Until recently, SAP's technology was something of a walled garden, affording only limited integration with other applications. But the company has started to expand its technical partnerships for SAP Datasphere and SAP Analytics Cloud with data catalog providers as well as data engineering and data science vendors, such as Databricks and DataRobot.

14. SAS Visual Analytics

For many years, SAS has had a strong presence in industries that require advanced statistical analysis. This remains a core focus and where SAS is strongest as a vendor, and its SAS Viya analytics platform is built on a cloud-enabled engine that's well suited for such scenarios. SAS Visual Analytics, its self-service BI and analytics software, is built on top of Viya and offers users data exploration, analysis, visualization and reporting features.

With the company's background in advanced analytics, the data visualization capabilities in SAS Visual Analytics include scientific and mathematical charting that's exceptional for a BI tool. Other advanced capabilities available in the software include location analytics for geospatial data and text analytics for use cases such as sentiment analysis on social media content. Also, open source languages and libraries are highly popular in statistics and quantitative analysis. SAS has embraced this trend by creating an open platform for developers.

While SAS Viya's analytics engine is cloud-enabled, it isn't cloud-native. This can pose some challenges for users on scalability and integration with other cloud services in deployments of SAS Visual Analytics.

15. Sisense

Sisense is another BI vendor that specializes in embedded analytics, supporting a hybrid cloud deployment model. A key component of the Sisense platform is composable analytics, enabled by the company's Compose SDK. The composable analytics capability is a code-first approach targeted at developers embedding Sisense in their own applications. It enables integration of the analytics code with common development environments for continuous integration/continuous delivery processes.

For developers, this means that one person can be working on data queries from the source system, while someone else develops data visualizations and the UI. Such decoupling makes it easier to develop new user experiences on the front end, such as integrating GenAI for natural language querying (NLQ), without affecting the back-end data operations. Similarly, back-end developers can fine-tune or even replace data sources and data pipelines without affecting front-end development.

Sisense also provides data modeling features and more than 400 connectors to data sources as well as live connections for direct access to cloud data warehouses. However, the platform doesn't include its own data integration and data transformation infrastructure.

16. Spotfire

The history of Spotfire is reflected in the software's strengths and weaknesses. Originally a standalone analytics tool with excellent data science and machine learning integration, Spotfire was acquired by Tibco Software in 2007 and integrated into its enterprise software offering focused on specialized industries, such as pharmaceuticals, life sciences and high-tech manufacturing. Now, Spotfire is a separate BI vendor again within Cloud Software Group, the combination of Tibco and Citrix Systems formed in 2022, but its user base is still highly concentrated in those verticals.

Spotfire is an advanced tool and not really suitable for non-technical users. Some understanding of machine learning or statistical analysis is required to get the best from its extensive features and advanced data visualizations. It's also relatively expensive compared to other BI platforms.

However, Spotfire remains a powerful and effective platform for users in its core markets that have advanced analytics requirements. The tool can be deployed on-premises, in the cloud or in a hybrid environment. It also has connectivity to a wide range of data sources, including support for streaming real-time data.

17. Tableau

After emerging from a research project at Stanford University in 2003, Tableau made data visualization a mainstream practice for BI analysts. Rather than charts and other graphics being additional objects in a dashboard, creating expressive visualizations was a core focus of Tableau users. As the company grew to be a BI market leader, it added features for data integration, collaboration and governance of both data and analytics content.

Tableau's influence in the market remains strong. But because it's now part of Salesforce, which bought Tableau in 2019, integration with the Salesforce platform and ecosystem has become a priority. That includes the addition of CRM Analytics, a separate Salesforce tool, to the Tableau product line as a strategic technology.

Nevertheless, Tableau is still adding innovative features to its core BI platform, including NLQ support. AI capabilities built into the software include Pulse, a personalized feed of data insights expressed both as visualizations and natural language that can be integrated into a user's daily workflow.

Tableau's enthusiastic user base has long been a key strength, helping the vendor to grow and consolidate its market position. To this end, Tableau Public is an online service that enables users to share data visualizations and best practices with the Tableau community.

18. Tellius

Automated insights for BI and decision intelligence are a competitive differentiator for Tellius. Under the hood, the platform runs a wide range of machine learning algorithms to discover patterns and trends in data, which can be surfaced to users through data visualizations and dashboards.

That includes Vizpads, which are built-in dashboards composed of multiple visualizations driven from diverse data sources and displayed on a single page. Vizpads enable non-technical users to set global filters that apply across all their business data, so they don't need to know how to query and integrate different data sources. This is done for them automatically in the UI.

Tellius offers natural language search and query capabilities as part of Kaiya, a conversational AI tool driven by GenAI. Kaiya can also help prepare data, summarize visualizations and create data stories. Overall, the UX focuses on three key analytics questions users commonly want to ask about data: what, why and how.

The software supports a combination of in-memory, live data and data lake processing modes in analytics applications. Users sometimes find the automated insights generated by Tellius to be unexpected, though not necessarily inaccurate. When this happens, it can be difficult to troubleshoot or audit the insights.

19. ThoughtSpot

After it was founded in 2012, ThoughtSpot offered a new approach to analytics: a search-based software platform developed by former Google engineers. Its advanced natural language search experience provided unique capabilities compared to anything else available in the BI market at the time. Today, most BI vendors have some natural language features thanks to the GenAI. As a result, ThoughtSpot is no longer so differentiated.

However, the company has continued to innovate on search functionality with Spotter, an agentic AI tool that includes a conversational interface. Spotter isn't just an NLQ system. It enables a user to give feedback to the AI software, also using natural language. The aim is to address some of the concerns about accuracy and hallucinations in GenAI tools by integrating human-in-the-loop feedback to improve results.

ThoughtSpot is popular with executive users who don't want to learn the details of a specialized tool set. But ease of use for the business user is enabled by extensive work for IT and BI teams, who often find that deployments require a lot of work to make integrated data available to the platform. This has improved in recent years as ThoughtSpot has added more technical partners, but it's still a potential concern.

20. Yellowfin

Part of Idera Inc.'s portfolio of data management, development and security software following a 2022 acquisition, Yellowfin offers a comprehensive analytics environment that's distinguished by two key focus areas: data storytelling and action-based dashboards.

Other BI vendors offer data storytelling features, but few have developed them as core components of their software. By comparison, Yellowfin has combined data storytelling, presentation and collaboration features, enabling narratives and data analysis to be merged. Users can drill down into the details behind data stories to access embedded dynamic data. Data stories can also include reports, images, visualizations and even video in a format that feels newsy and interactive.

Yellowfin's dashboard software enables customers to embed interactive "action buttons" that fire off specified operational functions outside the analytics platform. Business users can immediately act on data insights by clicking to launch a workflow, such as triggering an alert or an ordering process. Yellowfin offers some prebuilt actions, but the real value is in integrating custom workflows with external applications.

The ability to serve large numbers of users with effective reporting and dashboards at a reasonable cost was a historical advantage for Yellowfin. However, that has been diminished by the widespread adoption of cloud-based BI.

Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups.