Machine Learning technology is helping companies across industries derive better business insights from their data. However, for reliable results, you need quality data. Poor quality means low (or no) value. This is something that big data has in common with AI.
In an article for The Enterprisers Project, Ness CTO, Moshe Kranc, is quoted on the importance of cleansing big data and suggests that the best way to do this could be by using Machine Learning. “Fortunately, machine learning data can be cleansed using… machine learning!” Kranc says. “ML algorithms can detect outlier values and missing values, find duplicate records that describe the same entity with slightly different terminology, and normalize data to a common terminology, etc.”