News | September 7, 2026
Executive Summary: You may build dashboards around the questions you expect, but business conversations rarely stay there. Enlyta's new AI Data Assistant removes that dependency entirely. Instead of being limited to the metrics a dashboard was built to show, Brand Insight Directors and Managers can now ask questions against any part of the underlying dataset in plain language, with full visibility into how every answer was reached, and within an environment built for the security demands of sensitive research data.
Stop scrolling through dashboards. Just ask.
Brand insight teams spend significant time preparing for anticipated questions, but some of the most important conversations happen around questions they didn’t plan for.
Until now, those moments meant leaving the room, rebuilding a dashboard view, or creating ad hoc analysis afterward. Enlyta's AI Data Assistant changes that.
Ask a question in plain language and get a data-grounded answer directly from the underlying dataset without searching through dashboards, applying filters, or building a new view from scratch.
Query your data with questions, not just dashboards
The AI Data Assistant lets insight teams ask questions against any part of the underlying dataset in plain language, whether answering an unexpected question in a live leadership meeting, exploring a hypothesis before committing to deeper analysis, or following a line of inquiry across multiple prompts. Context is preserved across the conversation, so teams can build on previous questions rather than starting from scratch each time. Suggested and follow-up prompts help guide exploration, making it easier to go further into the data without needing to know exactly what to ask next.
See the thinking behind the answer
A fast answer is useful, but understanding how that answer was reached is what makes it valuable. Enlyta's assistant exposes the reasoning, assumptions, and filters used to generate every response, so researchers can validate that the question was interpreted correctly and the right data was used before sharing findings more broadly.
Go deeper with comparative and driver analysis
The AI Data Assistant is built to handle the analytical questions that matter most to insight teams. Comparative analysis allows users to dig across geographies, years, demographic cuts, and multiple brands simultaneously, examining how a brand performed on multiple metrics over time or how different audience groups view competing brands across markets. Driver analysis goes further, surfacing cause-and-effect relationships within the data: which brand imagery attributes or equity metrics are influencing consideration, and to what extent. These are not surface-level summaries. They are the kinds of structured, multi-dimensional questions that previously required significant manual analysis to answer.
To learn more about Enlyta Insights or speak with the team, get in touch with Hall & Partners.
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Key Questions
The core problem it solves is reactive analysis: the time spent after a meeting rebuilding a chart, responding to a one-off stakeholder request, or generating a new data cut that wasn't anticipated when the dashboard was built.
By making the underlying dataset directly queryable, the assistant gives insight teams a way to respond in the moment, so their energy goes toward interpretation and strategic guidance rather than data preparation.
You can ask natural-language questions about your brand tracking data, from straightforward performance queries to more complex, multi-dimensional analysis. For example, you might ask how brand consideration has changed over time, which audiences are driving a shift, or how your brand compares with competitors across geographies, demographic cuts, and multiple years simultaneously. The assistant also supports driver analysis, helping you understand cause-and-effect relationships within the data, such as which brand imagery attributes or equity metrics are influencing consideration and to what extent. Rather than limiting analysis to the views already built into a dashboard, the assistant helps you follow the questions that emerge as you work with the data.
General-purpose AI tools are designed to work across a wide range of information. Enlyta’s AI Data Assistant is purpose-built to work with structured research data, allowing insight teams to query their brand tracking datasets directly. It also provides visibility into the reasoning, assumptions and filters behind its answers, alongside security and governance controls designed for sensitive research data.
Yes. The AI Data Assistant is designed with the security and governance requirements of sensitive research data in mind. This includes client environment isolation, user access controls, audit logging and secure cloud hosting. Client data is also never used to train the underlying AI models, giving insight teams greater control over how their research data is accessed and used.
