Volume and quality of training data are the largest barriers to applying machine learning - Help Net Security

IDC predicts worldwide spending on artificial intelligence (AI) systems will reach $35.8 billion in 2019, and 84% of enterprises believe investing in AI will lead to greater competitive advantages (Statista). However, nearly eight out of 10 enterprise organizations currently engaged in AI and machine learning (ML) report that projects have stalled, and 96% of these companies have run into problems with data quality, data labeling required to train AI, and building model confidence, according to Alegion. Data issues are causing enterprises to quickly burn through AI project budgets and face project hurdles. The new report, "Artificial Intelligence and Machine Learning Projects Obstructed by Data Issues" was conducted by Dimensional Research. The findings include feedback from 227 participants including data scientists and business stakeholders involved in active enterprise AI and ML projects, addressing the maturity of ML in the enterprise, today's ML project challenges, and the tools and resources used in these projects.