Ten Critical Mistakes We Keep Seeing in Data Warehouse and AI Projects
Many data warehouse and AI initiatives fail not because of technology, but because of avoidable strategic and operational mistakes—from unclear business goals and poor data quality to overly complex architectures and weak governance. By recognising these common pitfalls early, organisations can build data platforms and AI solutions that deliver real, scalable business value.