Why Data Is The Achilles' Heel For Enterprise AI Adoption

AI has the potential to add $15.7 trillion to the global GDP by 2030 due to labor productivity improvement and automation. Enterprise AI adoption is accelerating in the market, with the projected value of AI (hardware, software, services) to reach $190.61 billion by 2025. The primordial ingredient that is required to make AI deployments successful is the access and availability of quality and clean data to train and feed machine learning and analytical models. Enterprise data architectures have been deployed and developed over decades, leading to a hodge-podge of different technologies, physical and logical architectures, formats and frameworks in organizations. Since machine learning (ML) algorithms rely on data to learn from to evolve prediction models, the need for sufficient, quality, prepared data is paramount.