Core Insights - The article discusses the evolving landscape of data infrastructure and business intelligence (BI) as organizations prepare for 2025 and beyond, emphasizing the need for AI-ready data and a shift towards data productization [1][2]. Data Infrastructure Needs - By 2025, data infrastructure will require a transformation to support AI, analytics, and real-time decision-making, moving away from traditional, fragile data pipelines [1]. - Organizations must adopt a mindset shift from ad-hoc data delivery to treating data as a controlled product, which can lower costs and complexity while enabling scalability in the AI era [1]. Business Intelligence Market Growth - The BI market is projected to grow from $38.62 billion in 2025 to $116.25 billion by 2033, with a compound annual growth rate (CAGR) of 14.98% [2]. Key Business Intelligence Trends for 2026 - **Augmented Analytics**: AI will enhance analytics capabilities, benefiting mid-sized businesses by detecting anomalies and uncovering patterns that would take humans days to find [5][8]. - **Natural Language Query (NLQ)**: NLQ is transforming user interaction with data, allowing users to obtain insights without needing to learn complex languages or interfaces [9]. - **Self-Service Analytics**: This trend enables users to perform data analysis and generate reports without IT assistance, promoting a standardized approach to data products [10][11]. - **Data Democratization**: Trust in data is crucial for decision-makers, and data products embed quality and governance to ensure reliability [12][13]. - **Data Governance and Trust**: Effective data governance addresses structural and compliance issues, ensuring data quality and ethical handling of personal information [14]. - **Cloud-Based BI Solutions**: Organizations are increasingly adopting cloud-native BI tools for flexibility and scalability, with data products facilitating cross-platform consistency [17]. - **Data Storytelling**: This approach goes beyond visualization to explain the significance of data insights, relying on structured and contextualized data [19][21]. - **AI Integration in BI**: AI will automate data analysis and generate insights, but its effectiveness hinges on the quality of input data, necessitating data productization [22]. Conclusion - The future of BI is not just about adding visualization tools but reimagining it as a product ecosystem built on modern data platforms, managed by reusable data products, and supported by self-explanatory AI [23].