Business Intelligence Software Top Companies and Market Size Analysis
The global business intelligence software market size is projected to surpass around USD 132.98 billion by 2033 from USD 36.70 billion in 2023, expanding at a solid CAGR of 13.74% between 2023 and 2033. The top companies in business intelligence software market are Oracle, IBM Corporation, Microsoft, Yellowfin International Pty Ltd (Yellowfin), QlikTech International AB, SAP SE, Board International, MicroStrategy Incorporated., Tableau Software, LLC (Salesforce, Inc.), Sisense Ltd, and Others.
Market Report Coverage
| Report Attribute |
Key Statistics |
| Market Revenue in 2024 |
USD 41.74 Billion |
| Market Revenue by 2033 |
USD 132.98 Billion |
| CAGR |
13.74% from 2024 to 2033 |
| Quantitative Units |
Revenue in USD million/billion, Volume in units |
| Largest Market |
North America |
| Base Year |
2023 |
| Regions Covered |
North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Business Intelligence Software Market Key Players
- Oracle
- IBM Corporation
- Microsoft
- Yellowfin International Pty Ltd (Yellowfin)
- QlikTech International AB,
- SAP SE
- Board International
- MicroStrategy Incorporated.
- Tableau Software, LLC (Salesforce, Inc.)
- Sisense Ltd
Latest Announcement Made by Market Leader
- In November 2024, IBM was named a leader in the Gartner Magic Quadrant for custom software development services. The company said, ‘the recognition highlights our ongoing efforts in cloud development services and our commitment to driving innovation through generative AI and cloud-native solutions.’
Recent Developments
- In September 2024, Zoho released Zoho Analytics 6.0, adding AI and machine learning capabilities, including a custom ML model-building studio, OpenAI integration, and expanded data connectors to enhance data analytics and insights.
- In April 2024, Tech Mahindra and Microsoft launched a unified workbench on Microsoft Fabric to help organizations streamline data workflows, enhance business agility, and improve operational efficiency with AI and machine learning integration.
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