Source URL: https://cloud.google.com/blog/products/data-analytics/2025-forrester-wave-data-management-for-analytics-platforms/
Source: Cloud Blog
Title: Google Cloud named a Leader in the 2025 Forrester Wave™: Data Management for Analytics Platforms
Feedly Summary: The rise of AI is revolutionizing data management platforms, where advanced automation, built-in data intelligence, and AI-powered data management are changing how organizations manage traditional tasks like data ingestion, data processing and governance.
We’re excited to announce that Google was named a Leader in The Forrester Wave™: Data Management for Analytics Platforms, Q2 2025 report. In the report, Google received 5 out of 5, the highest score possible, across 13 different criteria. We believe this is a testament to our strengths in several key areas, particularly in delivering agentic experiences that automate manual tasks and accelerate gen AI use cases, built-in intelligence to unlock new insights from structured and unstructured data, real-time capabilities driving insights to action, and a secure and governed multimodal data foundation with governance across the data-to-AI lifecycle.
According to the report:
Google’s distinctive and forward-thinking vision is to provide a unified, agentic, intelligent, and seamlessly integrated data platform that blends data management, advanced analytics, and AI capabilities at scale. The platform continues to evolve rapidly, focusing on advanced automation, open standards, global scale, self-service, and deeper integration with other Google services. The vendor’s roadmap is exceptionally well-defined, delivering a powerful strategic direction and alignment with AI positioned at its core.
Google placed furthest on Strength of Strategy and received above-average customer feedback in the evaluation, denoted by the halo around Google’s circle. Customers such as Dun & Bradstreet, Shopify, General Mills and many more choose BigQuery for its autonomous data and AI capabilities when building their data management platforms. Let’s take a closer look at the capabilities that differentiate Google Cloud’s data platform.
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Agentic and AI-assisted capabilities to power your analytics
Data management isn’t just about storing and querying data; it’s also about intelligent automation and assistance. As highlighted in our recent announcements from Google Cloud Next 25, BigQuery has evolved into an autonomous data-to-AI platform, where specialized data agents, advanced engines, and business users can all operate on a self-managing multimodal data foundation built for processing and activating all types of data. With assistive capabilities powered by gen AI and integrations with Vertex AI for model building and deployment, you can reduce the complexities of data management and smooth the path from raw data to actionable AI-driven insights.
BigQuery’s AI-powered data management capabilities are designed for users of all skill levels. Data analysts can use natural language to query data, generate SQL, and summarize results. Data engineers can automate manual tasks like data preparation, building data pipelines, and performing anomaly detection to accelerate analytics workflows. Data scientists can use AI-driven notebook experiences and new engines to process complex data and support advanced analyses in real time.
A multimodal data foundation with unified governance
BigQuery helps unify analytics across diverse data types by allowing data teams to build on an open lakehouse foundation. It combines highly performant native data management capabilities with support for open formats like Apache Iceberg, Delta, and Hudi. Multimodal support lets you store and analyze structured and unstructured data within the same table, streamlining complex analytics workflows. Finally, BigQuery’s universal catalog lets you work across SQL, Spark, AI, BI, and third-party engines, all with a flexible and open data lakehouse architecture, supporting interoperability.
Beyond the universal catalog, BigQuery data governance (powered by Dataplex) provides a unified experience for discovering, managing, monitoring, and governing data across data lakes, warehouses, and AI models. It also enables consistent policy enforcement, automated data quality checks, and comprehensive lineage tracking. Combined with a robust security infrastructure and fine-grained access controls, it helps you manage your data and AI assets with confidence, supporting compliance and building trust. Features like managed disaster recovery, enhanced workload management for aligning budget with performance needs, and flexible pricing with spend-based commitments further reinforce enterprise readiness.
Built-in intelligence for real-time insights
BigQuery enables your teams to build and deploy machine learning models using their existing SQL skills. This helps to eliminate complexity and accelerates the adoption of AI across the organization. BigQuery’s integration with advanced AI models helps to extract insights from multimodal data in documents, videos, and images. Scalable vector search supports intelligent recommendations, while the new BigQuery AI query engine allows analysts to use familiar SQL and LLMs for real-world context when analyzing unstructured data.
Real-time data capabilities are important for bringing fresh data to your AI models. BigQuery is designed from the ground up to support high-throughput streaming ingestion, allowing data to be analyzed as soon as it arrives. Real-time data combined with built-in machine learning and AI enables use cases like real-time fraud detection, dynamic personalization, operational monitoring, and immediate response to changing market conditions. Combining real-time data pipelines with the Vertex AI allows you to build and deploy models that react instantly, turning real-time data into real-time intelligent action.
Google is your partner for data to AI transformation
Google’s recognition as a Leader in The Forrester Wave™: Data Management For Analytics Platforms, validates our strategy and execution in delivering a comprehensive, AI-powered platform. Our focus on AI-driven assistance, a multimodal data foundation, and real-time intelligence helps to reduce manual data management tasks, so you can accelerate insights, and innovate faster.
As we evolve BigQuery into an autonomous data-to-AI platform, we are committed to helping you navigate the complexities of the modern data landscape and lead with data and AI. Thank you, our customers and partners, for choosing BigQuery to power your data management and analytics. Learn more about BigQuery today by visiting our website. Read the full Forrester Wave™: Data Management For Analytics Platforms, Q2 2025 report here.
Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here .
AI Summary and Description: Yes
**Summary:** The text discusses Google’s recognition as a leader in data management for analytics platforms, emphasizing its autonomous data-to-AI capabilities, real-time intelligence, and a unified multimodal data foundation. This relevance speaks particularly to security and compliance professionals focusing on AI and data management.
**Detailed Description:**
The announcement highlights several significant aspects of Google’s data management platform, particularly as it pertains to leveraging AI and advanced automation:
– **Recognition in Reports:**
– Google is recognized as a Leader in The Forrester Wave™: Data Management for Analytics Platforms, Q2 2025.
– Achieved a 5 out of 5 rating across 13 criteria, demonstrating strength in various areas of data management and analytics.
– **Key Features of Google’s Data Platform:**
– **Agentic Experiences:** Automation of manual tasks and acceleration of generative AI use cases enhance the efficiency and effectiveness of data management.
– **Unified Data Platform:** Google aims to provide a seamless integration of data management with AI and advanced analytics capabilities, appealing to a wide range of users from data scientists to business analysts.
– **Autonomous Data Management:** Tools like BigQuery are evolving into self-managing platforms with features that allow both data agents and users to process and activate data smoothly.
– **Capabilities Highlighted:**
– **AI-Powered Management:** Users can interact with data using natural language, automate tasks, and build data pipelines without extensive technical skills.
– **Multimodal Support:** Enables storage and analysis of both structured and unstructured data, facilitating complex analytics workflows.
– **Comprehensive Governance:** A robust governance framework ensures data quality, lineage tracking, and secure access, which is crucial for compliance and trust-building.
– **Real-Time Capabilities:**
– The platform is designed for high-throughput streaming ingestion, allowing for real-time data analysis and application in areas like fraud detection and operational monitoring.
– Integrates with advanced AI models, supporting intelligent recommendations and context-aware analysis through SQL and large language models (LLMs).
– **Commitment to Data and AI Transformation:**
– Google emphasizes its role as a partner in transforming data into actionable AI insights, highlighting the focus on reducing manual tasks and accelerating data insights.
This analysis demonstrates the platform’s strategic relevance for professionals engaged in AI, data infrastructure, and security, particularly those focusing on compliance, governance, and innovative data management solutions. The integration of AI and real-time capabilities can significantly impact organizational efficiency and effectiveness in handling data.