We live in an era of big data with data sets that require computational analysis to gain insights and knowledge. The volume of big data has been increasing steadily, and will only continue to climb. Since we started the WiDS initiative in 2015, Statistica estimates that the volume of data has increased from 15.5 to 74 zetabytes, and they forecast that data volume will double again by 2024.

Yet with all of this data, one of the biggest challenges that data scientists and researchers face is dealing with missing data. In some cases, the missing data is due to not readily having access to the data sets that are required to perform the analysis, while other cases involve data sets that are incomplete and not uniformly populated.

Data science is being applied in a growing number of domains that affect everyone’s lives, in healthcare, financial services, agriculture, resource management, and beyond.  While data science has huge potential for good, there are also unintended consequences. Data scientists need to take steps  to mitigate as many unintended consequences as they can using Responsible Data Science — a set of policies, procedures, and best practices to ensure algorithmic fairness, transparency, and explainability.