In insurance, real-time data should mean real-time decisions. But today, underwriting and risk analytics are stuck in a multi-hop pipeline: data flows through batch jobs, ETL processes, and static BI tools before anyone can act on it. By then, the risk is already on the books. Kafka powers real-time applications, yet analytics remain an afterthought. Insurers need to break free from the lag, enabling underwriters and finance teams to explore data as it happens, without waiting for reports. Because when a storm is forming, you need a live radar, not yesterday's forecast.
Leo Delmouly
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Shifting Left
In the modern data stack every data job has an offline and online component. Typically these are served by vastly different systems and employ a multi-hop architecture to make data sets available for each. This can be enormously inefficient as each hop typically involves physical data movement, transformation and processing. These hops are commonly developed in isolation across many technologies, languages and approaches resulting in a Data MESS rather than a Data Mesh.
Tom Scott