We are living in the era of Big Data. Our current ability to amass huge data sets combined with innovative methods of analysis has led to an unprecedented push toward the use of analytic tools in both the public and private sector. One of the most recent developments in the world of Big Data is the use of predictive analytics as a decision making tool, which has been described as a way to “predict the future using data from the past” (Davenport, 2014, p. 1). These predictions require analyses that sift through enormous sets of data in order to identify patterns.
Although there is no standard method for the analysis, these predictions often rely on statistical algorithms and machine learning. Both the public and private sectors already employ predictive analytics to make key decisions in a variety of industries, including advertising, insurance, education, and, of particular interest, child welfare.
The field of child welfare has a long history of using risk analysis to guide institutional decision-making (Russell, 2015). Many in the field look toward predictive analytics as the next big innovation for understanding the risks associated with child maltreatment. Proponents of predictive analytics point to a variety of potential benefits, such as the ability to access hidden patterns, streamline service delivery, and decrease operation budgets. Beyond these benefits, the biggest push for predictive analytics use comes from the potential to prevent youth maltreatment before it occurs by identifying who is most likely to need care (Russell, 2015).