Data Maturity

The AI & Big Data Expo in London shows a shift from generative AI excitement to practical integration challenges. Day two focused on crucial infrastructure like data lineage, observability, and compliance. Data maturity is key, as flawed data leads to unreliable AI. Regulated industries face complex deployment needing accuracy, attribution, and audit trails. AI is also reshaping developer workflows, with copilots accelerating coding but demanding new validation skills. Low-code/no-code platforms are democratizing AI development. The most effective AI applications solve specific, high-friction problems, emphasizing the need for robust data governance and training for successful AI transitions.