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From dashboards to dialogue
Ognen Loparski, Data Analytics and Insights Manager, hipages Group
Ognen sees conversational analytics as the final layer of a mature self-service data strategy designed for humans, building on behavioural analytics, exploratory dashboards, and robust data products.
It enables stakeholders to ask complex, ad-hoc questions in natural language, unlocking deeper insights without technical barriers. He wants to share practical lessons from integrating Claude, MCP, and Databricks Genie to make this vision a reality.
He may also touch on the organisational shift that happened after business users were exposed to Claude, or on next-level analytics with Agentic BI, though that may be better suited for a separate talk.
Ognen Loparski is a data analytics leader with an engineering background and over 20 years in the software industry. He has led data and engineering teams, coached senior leaders, and applied hands-on expertise in machine learning to solve complex business challenges.
His current work focuses on Agentic BI and self-service scalability, including pioneering the use of Claude Code/Desktop and Databricks Genie, connected via Model Context Protocol (MCP) to power Conversational Analytics.
Everyone is tracking AI visibility. Almost nobody is measuring AI impact
Frederic Chanut, GAICD, Executive Chairman, In Marketing We Trust
Citation Rate, AI Visibility Score, Share of Model. These metrics tell you whether your brand appears in an AI answer. They tell you nothing about whether that appearance is driving revenue.
That gap is the AI attribution crisis, and it is likely sitting inside your "Direct" traffic bucket right now.
In this session, Freddy Chanut will introduce the 7-Layer AI Signal Stack: a ground-truth measurement framework that connects AI crawls directly to pipeline revenue.
We will cover why 70% of AI traffic is invisible to standard analytics, why the Crawl-to-Refer ratio