# Artificial Intelligence Imagery Analysis Fostering Big Data Analytics
## 1 Introduction  
Innovative technologies have triggered the current omnipresence of visual data. These large amounts of visual data are the raw material for obtaining rich insights for research and practice. A number of approaches to big data analytics have evolved that increasingly offer a set of tools for analyzing large amounts of data. One area of big data analytics that has recently come to maturity is computer vision, which in itself involves an array of technologies and methodologies. Artificial Intelligence Imagery Analysis is one approach that is relevant to computer vision, and it is starting to gain attention among researchers and practitioners. Using machine learning and neural networks, Artificial Intelligence Imagery Analysis detects various types of imagery features in pictorial data. It provides insights on those data and thereby helps to cluster, filter, or otherwise analyze large amounts of images. Until a few years ago, pictorial data largely constituted a black box for research and practice, even though such data drive important and tangible outcomes such as the adoption and use of systems or the consumption and sales of digital content. Recently, information systems researchers have called for the increased use of images as a data source, since the field is \"overwhelmingly visual in nature\" [1]. So far, information systems research has relied mostly on traditional methods in responding to the call for the increased use of pictorial data. The dominant quantitative approach for analyzing imagery data is the experimental approach, which can be further differentiated into traditional, neuroimaging, and psychophysiological approaches. In traditional experiment-based designs, participants typically view imagery stimuli on a screen and answer questions or make choices to determine attitudinalor behavioral outcomes [2]. Neuroimaging studies use data obtained via electroencephalography (EEG) [3] or functional magnetic resonance imaging (fMRI) [4]. Functional near infrared spectroscopy (fNIR) provides a third neuroimaging approach [5]. The main psychophysiological approach is eye tracking [6; 7]. Surveys are often used to complement quantitative, experimental approaches [6]. However, so far it has hardly been possible to dig deeper into the thematic and semantic levels of large amounts of pictorial data and systematically analyze them and the responses they trigger. Recent advancements in Artificial Intelligence (AI) has made it possible, for the first time, to analyze the features of a large number of images with a precision that recently exceeded the rating and classification precision of human raters [8]. We distinguish (1) basic features such as dominant colors, shapes, or symmetry, (2) textual or conceptual representations of important topics, and (3) human features (e.g., faces and emotion). Hence, Artificial Intelligence Imagery Analysis seems to be a promising method for information systems research as it allows for comprehensive pre-testing, simplifies longitudinal research designs that require collecting data over weeks or months, and reduces the cost and time for data collection by a factor of 100 to 1000. Further, it overcomes typical human-related biases that are common in research using human raters. Below, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that mostly rely on manual work by humans. Further, we discuss applications of Artificial Intelligence Imagery Analysis for research and in practice and provide a case study for its use in research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos.