Enormous amounts of data are nowadays collected and stored by organizations, with the hope of being useful in the future. This poses the challenge of managing such loads of data and extracting from it appropriate knowledge for supporting decisions. Big Data is currently globally spread and widely accepted, representing also a synonym of vanguard in terms of information management, although this does not come without controversy (Fan & Bifet, 2013). In fact, as Chen, Chiang, and Storey (2012) argued, practitioners need to step forward “from Big Data to Big Impact” for effectively benefiting from the advantages provided by Big Data. The support of decision making is a key issue for marketers, by providing insights to assist in answering critical questions such as: what is the most suitable product for a specific market; how to advertise such product in that market; through what communication channels; at what points in time and for what price; and supported by what kind of promotional and advertising actions. Given these premises, it is with no surprise that Marketing has become from the start a field for experiments with Big Data approaches (Bendle & Wang, 2016). Recent sources of loads of data include social media and mobile applications: both have proven to have a huge impact on customers’ decisions, directly affecting brand building (Moro, Rita, & Vala, 2016). This paper outlines a research literature analysis on Big Data in Marketing framed in the 2010–۲۰۱۵ period. Considering Big Data in itself only holds value if it is harnessed aiming at extracting insightful knowledge, analytics has emerged as a pivotal trend to answer the challenges of Big Data in Marketing. In fact, marketing analytics solutions are now being disseminated throughout organizations in an attempt to benefit from Big Data solutions (Laurent, 2013), although most implementations are not yet achieved to effectively take advantage from such integrated Marketing-Big Data solutions (Chen et al., 2012). While analytics also apply to smaller volumes, emerging challenges regarding large volumes have recently risen, such as integrating a wide number of distinct sources, dealing with both structured and unstructured data, and performance issues. Therefore, analytics have also been included within this study. Nevertheless, it should be highlighted that the main focus of this study is to understand how Big Data is being managed and tackled to leverage Marketing challenges. Thus, by analyzing research conducted in this domain,the present study provides an overall picture of the current state of the art, contributing to a better understanding of this subject. With the aim of covering a large spectrum of the literature, a semi-automated text mining approach using topic modeling was adopted. The topics discovered provide the needed research insights on Big Data in Marketing.