Conversational analytics in practice: Coolgreen demo

Conversational analytics is a way to analyse and explore data by asking questions in natural language. Instead of searching through reports, dashboards or datasets, users can ask a business question in the same way they would ask a colleague. The conversational analytics solution interprets the question using the available data and returns an understandable answer, potentially supported by a data visualisation.

No. Conversational analytics complements dashboards rather than replacing them. Dashboards remain useful for recurring monitoring, predefined reports and standard KPIs. Depending on the technology and data environment, conversational analytics can also let users ask questions about the data behind existing dashboards. It is particularly useful for follow-up questions or information that is not covered by a predefined report.

No. Conversational analytics changes the role of Data Analysts rather than replacing them. Business users can answer more straightforward and recurring data questions independently, reducing the number of individual ad-hoc requests for analysts. This gives Data Analysts more capacity for complex questions, high-impact analyses and improvements to the analytics environment. Their expertise also remains important for monitoring and improving the quality of the conversational analytics solution.

The reliability of conversational analytics depends on the quality of the data, the definitions used and the context available to the solution. Conversational analytics is therefore not a magic wand: incomplete data, unclear definitions or insufficient context can affect the quality of an answer. Data governance and ongoing monitoring are important parts of implementing and maintaining a reliable solution.

No. Conversational analytics should operate within the access controls of your organisation. Which data an individual user can access depends on the configuration of the conversational analytics solution and the underlying data environment. Governance and authorisation should therefore be included from the start of the implementation.

Not automatically. Whether company data is used to train a large language model (LLM) depends on the selected technology, its configuration and the agreements with the technology provider. During implementation, we therefore assess where data is processed, which information the model can use and which privacy, security and governance requirements apply.

Conversational analytics works best when your data is well managed and accessible. If definitions are still unclear, data quality is insufficient or access rights are not properly set up, it is often better to address those foundations first. The solution will also add less value if employees have little need to explore data or answer questions independently.

Conversational analytics combines multiple disciplines, including Data Analytics, Data Engineering, AI, data governance and organisational adoption. That is why we look beyond the implementation of an LLM or conversational interface.

We can support the full journey: from the data foundation and business questions to implementation, governance, monitoring and adoption. The goal is not simply to introduce another AI tool, but to help employees use data more quickly and independently in their daily work.