KDD 2021 Tutorial: Data Quality for Machine Learning Tasks - overview
The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Data remains susceptible to errors or irregularities that may be introduced during collection, aggregation or annotation stage. This necessitates profiling and assessment of data to understand its suitability for machine learning tasks and failure to do so can result in inaccurate analytics and unreliable decisions. While researchers and practitioners have focused on improving the quality of models (such as neural architecture search and automated feature selection), there are limited efforts towards improving the data quality.
Assessing the quality of the data across intelligently designed metrics and developing corresponding transformation operations to address the quality gaps helps to reduce the effort of a data scientist for iterative debugging of the ML pipeline to improve model performance. This tutorial highlights the importance of analysing data quality in terms of its value for machine learning applications. Finding the data quality issues in data helps different personas like data stewards, data scientists, subject matter experts, or machine learning scientists to get relevant data insights and take remedial actions to rectify any issue. This tutorial surveys all the important data quality related approaches for structured, unstructured and spatio-temporal domains discussed in literature, focusing on the intuition behind them, highlighting their strengths and similarities, and illustrates their applicability to real-world problems. Finally we will discuss the interesting work IBM Research is doing in this space.