GoodData's Context Management aims to make AI trustworthy
The new context layer includes semantic modeling and governance to make data consistent and discoverable for AI and could help the vendor distinguish itself from some competitors.
With many enterprises investing in developing agents and other AI applications that don't deliver consistent enough outputs to make it past the pilot stage, GoodData on Thursday launched Context Management, a new layer aimed at better enabling customers to build AI tools that can be trusted.
Research and advisory firm Gartner predicts that worldwide spending on AI projects will total $2.5 trillion in 2026, up from $1.6 trillion in 2025 and just under $1 trillion in 2024.
However, despite enterprises increasing their investments in AI initiatives since OpenAI's November 2022 launch of ChatGPT marked significant improvement in generative AI technology, most AI pilots never make it into production.
While numerous factors contribute to the high failure rate of AI projects, one of the main ones is that AI pipelines can't discover and deliver enough pertinent data to provide contextually relevant outputs. For AI tools to operationalize the right data to deliver appropriate outputs, data must be consistent across an organization's entire data estate, and it must be high-quality.
Context Management integrates semantic modeling, governance, observability and knowledge grounding that bases responses on governed data into a single context layer. Its intent is to enable GoodData users to make data consistent and discoverable, so AI-powered analytics tools can call on the contextually appropriate data they require.
As a result, Context Management is a valuable addition for GoodData users, according to Michael Ni, an analyst at Constellation Research.
"Context Management is significant for GoodData customers because it extends the platform beyond delivering consistent metrics to governing how AI uses those metrics in production," he said. "Historically, GoodData's semantic layer ensured everyone agreed on the meaning of the business. With Context Management, GoodData … will also guide how AI applies that meaning."
Based in San Francisco, GoodData is a longtime BI vendor providing a platform that, like peers such as Qlik and ThoughtSpot, is increasingly focused on helping customers build AI tools for analyzing data. In January, GoodData added a Model Context Protocol (MCP) server, enabling customers to automate connections between AI models and data sources such as databases and lakehouses to fuel AI pipelines.
Context is crucial
Throughout 2025, many data management and analytics vendors unveiled development suites aimed at making it easy for users to build agents and other AI applications. For example, Databricks, Snowflake and Teradata -- among many others -- unveiled dedicated environments for building agents.
Context Management is significant for GoodData customers because it extends the platform beyond delivering consistent metrics to governing how AI uses those metrics in production.
Michael NiAnalyst, Constellation Research
While those suites simplified AI development, they did not improve the high failure rate of AI projects. They provided simple frameworks for development, and often included support for MCP, but did not address the data that provides AI tools with contextual awareness.
In response, numerous vendors so far in early 2026 have introduced capabilities aimed at improving the accuracy of data retrieval pipelines so they feed AI tools with more contextually appropriate data. Among them, Databricks launched Instructed Retriever to provide an alternative to traditional retrieval-augmented generation, MongoDB released new embedding and reranking models, and vendors including Pentaho and Insightsoftware have added semantic layers.
GoodData has provided semantic modeling capabilities since 2020 and modernized its semantic layer with AI capabilities in 2025. With the release of Context Management, GoodData is augmenting semantic modeling with additional capabilities, including governance and observability to improve data retrieval for AI.
A combination of customer feedback and an understanding that context is key to successful AI development provided the impetus for developing Context Management, according to Peter Fedorocko, GoodData's field CTO.
"As customers began using our AI capabilities more heavily, they naturally wanted more control over how answers are generated -- how to guide, refine and tune AI responses," he said. "That demand highlighted the need for stronger context management. At the same time, we believed … that successful enterprise AI deployments depend more on robust context management than impressive demos."
Context Management is a contextual layer that is designed to make AI-powered analytics tools more accurate. Its capabilities include the following:
Semantic modeling that consistently defines metrics, dimensions of data and business logic so that agents, dashboards and APIs use the same definitions to access relevant data.
Automatically applied governance to ensure that agents and other AI tools access and use data according to preset guidelines to prevent misuse and potentially unsafe actions.
Knowledge grounding that bases every response on governed data and makes outputs traceable so they can be trusted.
Observability that tracks prompts, inputs, outputs and expenses to make AI-powered analytics more transparent and auditable.
Guidance to help define how AI tools should behave, including setting consistent terminology and priorities.
Mike Leone, an analyst at Omdia, a division of Informa TechTarget, like Ni noted that Context Management is a significant addition for GoodData users.
"It formalizes what every analytics vendor is scrambling to figure out right now, which is how to make AI answers trustworthy in production," he said. "For GoodData's embedded customer base, specifically, where you're shipping AI-driven insights inside someone else's product, governed context is a requirement to get there."
Meanwhile, the most valuable feature of Context Management is semantic modeling, Leone continued.
"Everything else falls apart without it," he said. "If your metrics aren't defined once and enforced everywhere, governance and the rest of the pillars fall apart."
While Context Management is an important addition for GoodData users developing AI-powered analytics tools, it also has significance for GoodData from a competitive perspective, according to Ni.
He mentioned that many analytics vendors are adding semantic layers. What they don't offer, however, are capabilities integrated with the semantic layer that govern how AI systems use definitions in production.
"Semantics answers the question, 'What does this number mean?'", he said. "Context goes further. It answers which definition should be used in this situation, how should AI apply it and what actions are allowed once that answer is produced."
Leone likewise noted that, while the concept of Context Management isn't unique, the composable, embeddable way it makes such capabilities available could be a differentiator for GoodData.
"Every analytics vendor is trying to figure out governed AI with varying degrees of success," he said. "Where GoodData has an edge is the delivery model. ... Those are the things that resonate with product teams building AI into their own applications."
Next steps
As GoodData plots upcoming product development, focal points include enabling AI-driven analytics, improving Context Management, creating an agentic platform and adding analytical resources for agents, according to Fedorocko.
"That means moving from analytics-as-code toward AI-assisted development, extending semantic modeling into a governed context layer, giving assistants and agents more tools and workflows, and enabling GoodData's analytics infrastructure to be used by agents through things like MCP and agent-to-agent integrations," he said.
Leone suggested that GoodData could improve Context Management by making the governance capabilities visible in the user interface.
"[Customers] that are embedding GoodData need to see what context the AI used and why an answer changed without touching code," he said. "That's what builds trust at scale and what every BI and analytics vendor is chasing."
Ni, meanwhile, noted that GoodData has opportunities to expand the breadth of its semantic layer and foundation for enabling contextual awareness by making them easier to integrate with other systems.
Most large enterprises use more than one cloud, more than one data management provider and more than one analytics vendor. Synchronizing semantic modeling capabilities and Context Management with those other platforms could benefit existing GoodData users and appeal to potential new customers, according to Ni.
"If GoodData wants to capitalize on its semantic and context foundations, the next step is making those assets easier to operationalize and integrate," he said. "Enabling stronger synchronization would help position GoodData as a system of record for business definitions that can be embedded across analytics tools, applications and AI systems."
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.
