What happens when you can ask your data a question?

The session explored how conversational analytics is changing the way life sciences teams interact with complex data.

Instead of relying only on predefined dashboards and reports, users can ask questions in natural language, generate analysis and visualizations, refine the results through follow-up questions, and continue exploring the data as new questions emerge.

Access the discussion

Discussion Highlights

Beyond Static Dashboards

Dashboards remain valuable. But conversational analytics creates a more dynamic way to explore information.
Users can move from a question to an analysis, refine the results, and continue investigating without waiting for a new report to be built.

AI-Enabled Analytics Within the Enterprise Environment

The discussion explored how AI-enabled analytics can work with an organization's own data, infrastructure, and security practices.
The approach can be applied across different types of enterprise data, including clinical, manufacturing, commercial, and portfolio data.

When the Data is incomplete

AI should not fill gaps with unsupported assumptions.
The discussion highlighted the importance of working from validated data and making limitations visible when the available information cannot support a conclusion.

Trust Requires Visibility

As data changes, users need more than a new answer.
They need to understand where the data came from, when it was refreshed, how the analysis was generated, and what may have changed.

Questions from the Discussion

Is Orion pulling live data from source systems such as Planisware and finance systems?

The discussion explored how Orion can work with an organization's existing data sources and enterprise environment. Data freshness and availability depend on how the relevant source systems and data connections are configured.

If I want to see how things have changed over time, do I need to refresh old reports?

Does Orion use the client's LLM? What does this mean for data privacy and security?

Can Orion show where it needs to do more work to clean the data or where there are gaps? Will it try to fill those gaps?

How do you keep Orion's answers trustworthy as the underlying data changes week to week?

Do users need an interpretation guide to understand AI-generated research reports and get value from them?

The Bigger Question

What if analytics started with the question, not the dashboard?

The conversation explored a shift from finding the right report to asking the right question.

From there, teams can explore the data, refine the analysis, understand what changed, and continue the investigation as new questions emerge.

Missed our live session on Conversational Analytics for Life Sciences Teams?

Explore how conversational analytics is helping life sciences teams uncover faster insights, improve portfolio visibility, and make more informed decisions

Request the session recording and our team will get back to you with access.

By submitting this form, you agree to our Terms of Use and Privacy Policy. Information provided by you may be collected and automatically stored in our database and may be used for sending you additional information about i2e Consulting and our services.