3 Data Quality Stages for Preparing Machine Learning Data

This is part of Solutions Review's Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, dotData Founder and CEO Ryohei Fujimaki offers commentary on data quality strategies to get your data machine learning-ready. As the world embraces machine learning (ML) and Artificial Intelligence (AI), data leaders are adjusting and perfecting data quality management frameworks. Traditionally, there are two stages in data quality: raw unprofiled data and cleansed data, free of common errors and commonly used for business intelligence (BI). But, companies at the forefront of data-driven decision-making have realized that data quality needs to level up--and this is where ML-ready data comes in.