Top Analytics & BI Tools in 2024
Key Insights from the latest Gartner Magic Quadrant
If your work involves Analytics and BI, it is important to regularly track which tools are gaining popularity or making new advancements.
Gartner has recently released the 2024 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms. Before we discuss the key takeaways, let’s understand what a Magic Quadrant is.
The Magic Quadrant
The Gartner Magic Quadrant research methodology offers a visual competitive positioning of four types of technology providers in rapidly growing markets: Leaders, Visionaries, Niche Players, and Challengers.
The graph has two axes: Completeness of Vision and ability to execute. It is divided into four quadrants:
- Leaders (upper right): Well-positioned with a clear market vision, established presence, and consistent execution.
- Visionaries (lower right): Understand market direction but may lack execution.
- Niche Players (lower left): Specialize in a market segment but lack a broad vision.
- Challengers (upper left): Execute well currently but may not anticipate future demands.
Gartner analyzes companies based on criteria like product capabilities, market presence, and service diversification. The findings are updated every one or two years, allowing for changes in company status based on improvements.
Changes compared to the previous Quadrant
Let’s look at the previous quadrant first which was published last year in 2023.
Now, let’s look at the latest Magic Quadrant:
Key Changes
While Microsoft (Power BI) and Salesforce (Tableau), the Market Leaders, have retained their spots, no one is close to Microsoft which stands at the top of being both a leader and visionary.
Oracle and ThoughtSport have moved from visionaries to Leaders and SAP is almost at the boundary of Visionaries and Leaders and might move into Leaders in 2025.
Google is the only challenger that has now become a Leader.
Pyramid Analytics has beaten everyone to stand apart as a visionary.
1. Microsoft’s Power BI — the King
Microsoft has remained a Leader for the 17th consecutive year.
It has maintained its spot due to the various innovations in Power BI, some of which are:
- Microsoft Fabric: An integrated analytics platform combining Power BI with Azure Synapse Analytics and Azure Data Factory, enabling seamless collaboration among data professionals.
- Generative AI: Features like Copilot in Fabric help users create reports, understand semantic models, and analyse data through conversational AI.
- OneLake and Direct Lake Mode: A unified data lake that enhances data management and performance without data movement or duplication.
- New Tools for Data Analysts: Improved tools for report creation, including model explorer, DAX query views, Git integration, and enhanced web authoring.
Microsoft’s strengths include a strong combination of pricing, functionality, and leveraging generative AI to simplify report creation and drive adoption. But what sets it apart is the Microsoft ecosystem, tools like Excel, Teams and linked Sharepoint are some of the applications that most of us use daily.
However, challenges remain, including governance concerns over content creation and interoperability issues with non-Azure platforms.
2. Salesforce‘s Tableau — The Close 2nd
Tableau, a Salesforce company, is also a leader in Gartner’s Quadrant for the 12th consecutive year and is just a little bit behind Power BI.
Known for its visual-based data exploration tools, Tableau introduced several innovations in 2023, including:
- Tableau Pulse: An augmented analytics experience enhancing data governance and trust.
- Embedded Analytics Enhancements: An embedded playground generating code snippets and a new usage-based pricing model.
- Composability: A re-engineered architecture with VizQL data service, a new Tableau Pulse metrics layer, and consumption-based pricing for Embedded Analytics.
- AI Integration: Einstein Copilot, an AI assistant for data exploration, and over 140 product innovations focused on leveraging AI.
Tableau operates with its dedicated leadership team and reported a robust 16% growth rate in FY23. Its mission is to democratize data, making data-driven decisions accessible to all through continuous innovation and user feedback.
Tableau uses role-based pricing and introduces complexity with consumption-based pricing for Embedded Analytics. Its expanded product suite, including Tableau Pulse under Tableau AI, shows innovation but adds confusion for clients. To compete well, Tableau must clarify its position within Salesforce Data Cloud amid changing competitive dynamics in enterprise analytics.
3. Google’s Looker — From Challenger to a Leader
Google has moved from being a Challenger to a Leader this year. Their Looker platform, part of Google Cloud, offers robust analytics, self-service visualizations, dashboards, and a code-first semantic modelling layer.
Key features and strengths include:
- Integrated Platform: Looker Studio integrates with Looker’s semantic layer for comprehensive analytics, supporting both self-service and production reporting.
- Composability: Looker’s modular architecture, including the VizQL data service, enhances flexibility and integration with other platforms.
- AI and Data Integration: Looker integrates with Google’s broader data and AI ecosystem, including BigQuery, Vertex AI, and Google Workspace, enabling advanced analytics and AI capabilities.
- Market Momentum: Looker has seen significant market interest and customer growth, highlighted by a 16% revenue increase in FY23.
- User-Friendly Features: Looker offers natural language queries, automated insights, and data storytelling through its Gemini AI models, making data interaction intuitive and efficient.
Looker’s composable platform supports custom data applications and scalable analytics solutions, emphasizing collaboration, flexibility, and reusability. This recognition reflects Google’s innovative approach and commitment to enhancing data-driven decision-making for businesses.
Google is advancing AI for business analysts through Looker Studio and improved NLQ but falls short in augmented analytics like automated insights and data storytelling. Looker’s visual data prep is code-first and lacks AI, limiting the appeal to business users. Native automated insights are underdeveloped, requiring external Google products for advanced features like key driver analysis and outlier detection.
Even though there are various challenges that Google need to sort out to compete with well-established tools like PowerBI or Tableau, for now, they seem to be on the right path.
4. Oracle’s OAC — From Visionary to a Leader
Oracle has moved from being a Visionary to a Leader, largely due to its Oracle Analytics Cloud (OAC). OAC is embedded in Oracle Fusion Apps, integrating business data, prebuilt data pipelines, and AI models to deliver insights and recommendations tailored to specific roles.
Key strengths and developments include:
1. Decision-Centric Business Apps: Oracle embeds decision workflows in business applications, empowering specific roles with actionable recommendations.
2. Comprehensive Enterprise Cloud Ecosystem: Oracle offers a complete cloud solution encompassing infrastructure, data management, and analytics, supported by a global network of data centres.
3. Advanced Data Management and Integration: Oracle’s prebuilt data pipelines and AI models accelerate business outcomes by simplifying data integration, a major obstacle in analytics.
4. AI Innovations: In 2023, Oracle extended its AI capabilities in OAC to include document understanding and generative AI for data storytelling. The introduction of Fusion Data Intelligence further enhances analytics by embedding AI content directly into business workflows.
5. AI-Infused Solutions: Oracle continues to innovate with AI-powered solutions, such as OCI Vision and Document Understanding, and advanced data storytelling features like the Analytics Assistant.
However, Oracle’s OAC has limitations for non-Oracle ecosystems and smaller businesses due to higher costs and complexity. Additionally, there are challenges in finding knowledgeable resources familiar with both OAC and OCI, though this is expected to improve as these offerings gain popularity.
Despite these challenges, Oracle’s position as a Leader underscores its ability to deliver top-tier analytics for effective decision-making.
5. AWS’s QuickSight — Challenger that stands apart
AWS has taken the place where Google existed last year, with Amazon QuickSight as its key offering.
- Competitive Pricing: QuickSight offers flexible pricing starting at $3 per user/month, avoiding overprovisioning with per-user and capacity-based options.
- AWS Ecosystem Integration: Strong integration with AWS data and analytics services like Redshift, Athena, and EMR.
- Scalability and Performance: Serverless architecture allows for high performance and scalability based on usage.
- Advanced NLQ Capabilities: Amazon Q incorporates generative AI and LLMs for enhanced dashboard building, data storytelling, and executive summaries.
- Data Science Integration: Improved capabilities for forecasting and predictions.
However, QuickSight is only deployable on the AWS cloud, not on-premises or in hybrid/multi-cloud environments. Enhanced governance and monitoring require separately licensed AWS products like DataZone and CloudTrail. Additionally, QuickSight lacks native data preparation capabilities, requiring AWS Glue DataBrew and AWS Glue for these tasks, which are pay-for-use.
AWS’s strengths lie in its competitive pricing, integration with its ecosystem, and advanced AI capabilities, while its limitations include restricted deployment options and reliance on additional services for governance and data preparation.
AWS seems a bit lagging in the Analytics and BI space as compared to the other two Cloud service providers.
6. Pyramid Analytics — Visionary that stands apart
It offers a robust suite designed to cover the entire data life cycle, integrating modern capabilities across data preparation, analytics, and data science.
- ML-Based Data Preparation: Pyramid Analytics leverages machine learning for data preparation and data wrangling tasks, enhancing efficiency and accuracy.
- Deployment Flexibility: The platform supports deployment across major cloud providers such as AWS, Microsoft Azure, GCP, Oracle Cloud, and Alibaba, as well as on-premises environments.
- Generative AI Capabilities: Introduced in 2023, Pyramid Analytics integrates generative AI for flexible natural language processing (NLP) and automated data storytelling.
- Multiple Data Preparation Experiences: Users benefit from a range of data preparation options including Smart Modeling, Direct Model, Model Lite, and advanced ETL-like processes in Model Pro.
- Data Science and ML Integration: Pyramid Analytics extends support for Jupyter Notebooks, AutoML, and comprehensive capabilities for training, testing, and deploying machine learning models.
However, challenges remain in its connectivity with other ABI platforms, limited availability of user resources and training, and achieving broader market visibility despite recent improvements in media coverage.
Overall, Pyramid Analytics aims to empower organizations with a unified platform for business analytics and data-driven decision-making in a user-friendly, no-code environment.
