GoodData Kicks Off 2026 with MCP Server Launch and Major Enterprise Wins
15.4.2026 13:00:00 EEST | ACCESS Newswire | Press release
Financial services wins and three platform launches signal accelerating enterprise demand for agentic analytics.
SAN FRANCISCO, CA / ACCESS Newswire / April 15, 2026 / GoodData, a leading AI-native decision intelligence platform, today announced strong Q1 2026 results driven by the launch of its MCP Server for agentic analytics, significant new contracts in global financial services, and continued expansion into new markets. The quarter built on a record-breaking 2025, reinforcing GoodData's position as the platform enterprises trust to move AI from pilot to production.
Continued growth across enterprise and financial services
GoodData's commercial momentum remained strong throughout Q1, with notable wins across the enterprise and financial sectors. The company secured a new three-year contract with one of the world's largest asset managers, and a key long-term customer in global payments renewed for three years while expanding its enterprise license.
"Our Q1 results demonstrate that the market is moving past the AI hype phase and into governed, agentic reality," said Roman Stanek, CEO and Founder of GoodData. "By giving AI agents the tools to not just report on data, but to build and iterate on analytics themselves, we are helping the world's largest financial institutions achieve a level of velocity that was previously impossible."
Building on previous expansion in the US, GoodData further extended its enterprise sales presence into Germany, deepening its footprint across key international markets. The company was also recognised in the Gartner Market Guide for Agentic Analytics category, a resource for data and analytics leaders evaluating how to scale agentic AI responsibly
Serving up the future of Agentic BI
Q1 was headlined by the launch of GoodData's MCP Server, enabling AI agents to build, update, and operate analytics end-to-end within a governed framework. Covering semantic models, metrics, and dashboards, the MCP Server delivers 10-50x faster time to value compared to manual BI workflows, giving enterprises a practical path to agentic analytics at scale.
The quarter also saw the introduction of AI-Driven BI Modernization, a new solution that uses AI to extract and refactor business logic from legacy BI systems into a governed semantic layer. The result is 2-5x faster iteration cycles and up to 10x faster dashboard loading, without taking existing infrastructure offline.
Rounding out the quarter's announcements, GoodData launched a dedicated Context Management layer, a governed contextual foundation for production AI that addresses the trust gap through enforced semantics, grounded knowledge, and full observability. Together, these launches reflect GoodData's focus on closing the distance between AI ambition and operational reality.
About GoodData
GoodData is an AI-native decision intelligence platform built to help enterprises turn trusted data into confident action. Designed for governed, scalable analytics, GoodData enables organizations to operationalize insights, automate decisions, and embed intelligence directly into products and business workflows.
With a composable architecture and a governed semantic layer at its core, GoodData ensures AI-powered analytics are transparent, auditable, and aligned with how enterprises define and trust their data. Organizations use GoodData to move from insight to impact faster, while maintaining enterprise-grade security, governance, and performance.
GoodData serves over 123,000 of the world's leading companies and 3.9 million users, helping enterprises close the gap between data and decision-making.
For more information, visit GoodData's website and follow GoodData on LinkedIn, YouTube, and Medium.
© 2026 GoodData Corporation. All rights reserved. GoodData is a registered trademark of GoodData Corporation in the United States and other jurisdictions. Other names and brands may be claimed as the property of others.
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SOURCE: GoodData Corporation
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