Data quality and governance identified as critical factors for AI success

Experts emphasize that the success of artificial intelligence initiatives depends heavily on the quality, availability, and governance of underlying data. At the AI Everything event, Shadi Hatoum of Tealium noted that data is often the primary obstacle to AI implementation. He stated that for AI to succeed, data must be consented, trusted, and available in real time to feed decisioning models. Hatoum advised organizations to avoid data silos and focus on building unified, first-party datasets that resolve identity across all customer channels.

In Africa, a survey conducted by Veeam and ITWeb highlights a gap between organizational confidence and actual operational readiness for AI and cyber recovery. While 85% of respondents expressed moderate confidence in their ability to recover usable data following a cyber incident, many struggle to substantiate this claim. The survey revealed that 32.4% of organizations had never performed a full technical recovery test or had not tested in over a year. Furthermore, 45% of those that did complete end-to-end recovery tests reported only partial success or significant gaps.