Can we automate data quality to support machine learning?

Over the last decade, companies have begun to grasp and unlock the potential that artificial intelligence (AI) and machine learning (ML) can bring. While still in its infancy, companies are starting to understand the significant impact this technology can bring, helping them make better, faster and more efficient decisions. Of course, AI and ML is no silver bullet to help businesses embrace innovation. In fact, the success of these algorithms is only as good as their foundations -- specifically, quality data. Without it, businesses will see the very objective they've installed AI and ML to do fail, with the unforeseen consequences of bad data causing irreversible damage to the business both in terms of its efficiency and reputation.