How to Build a Modern Data Strategy in the Age of AI
Every company today wants to be an AI company. Leaders are pushing for predictive churn models, generative agents for customer success, and automated forecasting. But there is a silent killer stalling these initiatives before they even launch: The Data Mess. Most data platforms don’t fail because of the tooling. They stall because raw, messy inputs are rushed straight into analytics, teams patch problems locally in spreadsheets, and every dashboard bakes in a different "truth". In this four-part series, we are going to break down how to build a data foundation that actually works. We will eventually cover specific implementation paths — using Microsoft Fabric, Databricks, or an Open Source DIY approach
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