The recent advent of remote sensing. mobile technologies, novel transaction systems, and high-performance computing offers opportunities to standardize trends, behaviors, and actions in a manner that has not been previously possible. Researchers can thus leverage "big data" that are generated from a plurality of sources including mobile transactions, wearable technologies, social media, ambient networks, and business transactions. An earlier Academy of Management Journal (AM oditorial explored the potential implications for data science in management and highlighted questions for management scholarship as well as the attendant challenges of data sharing and privacy (George. Haas, & Pentland, 2014) This nascent field is evolving rapidly and at a speed that leaves scholars and practitioners alike attempting to make sense of the emergent opportunities that big data hold with the promise of big data come questions about the analytical value and thus the relevance of these data for theory development - including concerns over the context-specific relevance, its reliability and its validity.To address this challenge, data science is emerging as an interdisciplinary field that combines statistics, data mining, machine learning, and analytics to standard and explain how we can generate analytical insights and prediction models from structured and unstructured big data Data science emphasizes the systematic stu dy of the organization, properties, and analysis of data and their role in inference, including our confidence in the inference (Dhar, 2013). Whereas both big data and data science terms are often used interchangeably, "big data" refer to large and varied data that can be collected and managed, whereas "data science" develops models that capture, visualize, and analyze the underlying patterns in the data. In this editorial, we address both the collection and science for management Al the current time, practitioners suggest that data science applications tackle the three core elements of big data: volume, velocity, and variety (McAfee & Brynjolfsson, 2012; Zikopoulos & Eaton, 2011). "Volume" represents the sheer size of the dataset due to the aggregation of a large number of variables and an even larger set of observations for each variable. "Velocity" reflects the speed at which these data are collected and analyzed, whether in real time or near real time from sensors, sales transactions, social media posts, and sentiment data for breaking news and social trends. "Variety" in big data comes from the plurality of structured and unstructured data sources such as text, videos, networks, and graphics among others. The combinations of volume, velocity, and variety reveal the complex task of generating knowledge from big data, which often runs into millions of observations, and deriving theoretical contributions from such data. In this editorial, we provide a primary or a "starter kit" for potential data science applications in management research. We do so with a caveat that emerging fields update and improve upon methodologies while often supplanting them with new applications. Never the less, this primary can guide management scholars who wish to use data science techniques to reach better answers to existing questions or explore completely new research questions.