Abstract
For its volume, velocity, and variety (the 3 Vs), big data has been ever more widely used for decision‐making and knowledge discovery in various sectors of contemporary society. Since recently, a major challenge increasingly recognized in big data processing is the issue of data quality, or the veracity (4th V) of big data. Without addressing this critical issue, big data‐driven knowledge discoveries and decision‐making can be very questionable. In this paper, we propose an innovative methodological approach, an archaeological‐ethnographic approach that aims to address the challenge of big data veracity and to enhance big data interpretation. We draw upon our three recent case studies of fake or noise data in different data environments. We approach big data as but another kind of human behavioral traces in human history. We call to combine ethnographic data in interpreting big data, including problematic data, in broader contexts of human behaviors.
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