Data quality: The not-so secret sauce for AI and machine learning
The promise of artificial intelligence has always felt more like a future state, but the reality is that many companies are already adopting AI initiatives. This is especially true in the scientific R&D realms. Over the last few years, there has been a huge increase of machine learning and AI initiatives in everything from QSAR models to genomics. According to a 2018 survey, AI adoption grew drastically from 38% in 2017 to 61% in 2018. This occurred across a variety of industries, including healthcare, manufacturing and financial services. However, most early adopters noted one of the biggest challenges to successful implementation involved data, specifically, accessing, protecting, integrating and preparing data for AI initiatives.