Robust Analytics Lab brings together an interdisciplinary team of researchers to understand the reliability and robustness of data analytics, particularly in the context of its complex interplay with societal issues such as privacy, disparity, fairness, freedom of information, etc. Our expertise spans multiple areas under the umbrella of responsible data practices, including data management, data analytics, privacy, and cybersecurity. Leveraging such expertise, we seek to better understand the dynamic and dialectic nature of the interplay between data analytics and societal issues, and to develop theories, tools and techniques that help IT professionals, business leaders and policy makers bring system requirements, business strategies and policies into better alignment.
One of our focuses today is to establish the confidence - or the lack thereof - in the inconsistent handling practices of organic data among scholarly publications. The notion of a “reproducibility crisis” has been raised and debated in multiple disciplines (e.g., social psychology) closely related to business research. The field of meta-science - the scientific study of science itself - has been examining the existence and prevalence of threats to reproducible and robust research. However, social sciences in general, and business research fields in particular, have paid limited attention to the reproducibility and robustness of findings from analytical studies using “organic data”. While there is not yet a universally accepted, precise, definition of “organic data”, the common understanding in the research community is that the term refers to data not collected following an explicit research design, but documented by a technology, device, or interface capturing natural “digital footprints” of human activities, such as data from sensing devices, mobile applications, or online social networks. With the growing popularity of using these “digital footprints” in management and marketing research, it is important for the research community to recognize and anticipate potential issues associated with the usage of organic data, so that practices for promoting reproducible and robust research can be established “ahead of the curve”. Our vision is to better raise awareness across many fields of business research about standards and tools for collecting, cleaning, and processing organic data. Further, we aim to develop new analytical frameworks and methodologies useful for evaluating replicability and robustness of empirical studies with organic data.