If you’ve been following BigDATAwire coverage, you would have noticed the data and AI industry leaning toward real-time systems. Aligned with that trend, the enterprise intelligence platform Starburst has launched a new AI data assistant called AIDA that connects AI to governed enterprise data across distributed systems. The aim is to replace delayed reporting with real-time decision support. Along with timing, having trust in the data is another key objective of AIDA, according to Starburst.
The combination of speed and trust is sought after but not easy to achieve. It represents a real bottleneck for enterprise AI and data management. Most organizations are not struggling to run models. They are struggling to apply them in a way that produces business ready outcomes. It doesn’t help that data remains fragmented and context is missing. Governance is often an afterthought layered on top rather than built into the system.
Starburst’s approach is not just to add AI on top of analytics. AIDA is positioned as a new interface to enterprise data. They want to take focus away from dashboards and reports toward continuous interaction with data.
Enterprises often use dashboards as the primary way to look at their data. You build them around a set of expected questions, and they update on a schedule. That worked when things moved slower. But now, most decisions are not made by staring at a fixed dashboard. Things change quickly, and the questions change with them.
AIDA is built for that kind of environment. Instead of clicking through charts, you can just ask questions in plain language. But it is not only turning those questions into queries. It tries to understand what you are really asking, pulls data from different systems, adds business context, and gives you an answer that actually makes sense.
That is where Starburst is trying to stand out. A lot of AI tools in analytics today just make querying faster. They speed things up, but the process stays the same. AIDA is trying to go a step further, closer to real decision support, where the system is not just answering, but helping shape the answer itself.
“Most companies are still approaching AI the wrong way, focusing on models instead of the data those models depend on,” said Justin Borgman, Co-founder and CEO of Starburst. “The real challenge is applying AI to business decisions without moving data or compromising governance. Starburst’s AI Data Assistant is built to solve that by providing access to trusted, distributed data from across the enterprise.”
Under the hood, AIDA is built around a few practical capabilities that matter in real enterprise environments. The first is how it works across distributed data. Instead of forcing everything into one system, it queries data where it already lives. That reduces delays and avoids the risk and cost of moving sensitive data around.
A key objective is to go beyond basic query generation. When a user asks a question, AIDA breaks it down, figures out which datasets are relevant, and builds toward an answer step by step. Starburst claims that AIDA provides “on-demand access to trusted enterprise data, enabling faster, more context-aware decisions.”
Context is a big part of how it works. AIDA relies on metadata, data catalogs, and governance layers to guide responses. That helps keep answers tied to trusted definitions and reduces the chances of incorrect or misleading results.
The system can also adjust answers based on the user. For example, a business leader might see a more summarized view, while an analyst might get more detail or even the underlying query. Starburst refers to this as “persona-specific reasoning”, making AIDA usable across different roles without forcing everyone into the same format.
Starburst is not alone in pushing this shift, but it is among the few framing it as a direct replacement for traditional BI. Others are moving in the same direction from different angles.
Dremio is building an agentic lakehouse, where users and AI interact with data through a semantic layer instead of dashboards. Databricks is integrating AI-driven interaction into its broader platform, though it still centers the platform itself. MindsDB focuses on querying distributed data in natural language directly at the database layer, while Collibra emphasizes governance and context as foundations for reliable AI.
The direction is the same for most of these companies where static dashboards are giving way to systems that interpret intent, access distributed data, and return answers in real time. However, the difference is how far each goes. Most extend existing workflows, while Starburst is more openly questioning whether those workflows should exist at all.
“As enterprises seek to democratize analytics with agentic AI, they need governed access to distributed datasets,” said Kevin Petrie, Vice President of Research at BARC US. “Starburst meets this requirement and goes further to enable intent- and persona-specific reasoning on federated inputs. This helps diverse stakeholders make smarter decisions in the context of the business.”
Put together, AIDA features show where data analytics is heading. It is less about building reports and more about getting trustworthy and real time answers directly from data, with enough context to actually use them in a meaningful way. The idea is not to make the dashboard disappear entirely but instead use them in a more specialized manner using conversational interface and more interactive data.
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The post Starburst Rolls Out AIDA Using AI to Redefine BI Dashboards appeared first on BigDATAwire.
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Author: Ali Azhar