Data Sourcing Still a Major Bottleneck for AI, Appen Says

Data is the lifeblood of machine. But organizations continue to struggle to obtain good, clean data to sustain their AI and machine learning initiatives, according to Appen's State of AI and Machine Learning report published this week. Of the four stages of AI–data sourcing, data preparation, model training and deployment, and human-guided model evaluation–data sourcing consumes the most resources, takes the most time, and is the most challenging, according to Appen's survey of 504 business leader and technologists. On average, data sourcing consumes 34% of an organization's AI budget, versus 24% each for data preparation and model testing and deployment and 15% for model evaluation, according to Appen's survey, which was conducted by the Harris Poll and included IT decision makers, business leaders and managers, and technical practitioners from the US, UK, Ireland, and Germany. Finally, 42% of technologists find data sourcing to be the most challenging stage of AI lifecycle, compared to model evaluation (41%), model testing and deployment (38%) and data preparation (34%).