Towards a business driven data architecture
summary
Sector Alarm presents a concrete framework: start from business drivers, map to data products (for example churn propensity model feeding CRM views and dashboards), draw end-to-end data flows, then pick a consolidated stack. Built outputs include a RAG-powered customer chatbot and alarm-event routing that auto-handles many false alarms, plus lead scoring and agent-performance pipelines using call transcripts and LLM analysis. The team chooses OneLake and Fabric on Azure with PowerBI and D365 integration; the framework is working in production and drives measurable churn and cost signals.
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chapters (12)
- 0:00Introduction
- 2:07Business-driven data framework
- 5:39Business objectives and revenue
- 9:32Mapping goals to data products
- 14:56Data products for acquisition & retention
- 19:30Reducing costs with data
- 23:26Key metrics and KPIs
- 23:47Data flows for real-world use
- 25:54Customer chatbot data flow
- 30:12Optimizing alarm handling
- 34:03Tech stack consolidation
- 37:57Q&A
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