It is natural for the human mind to participate in a practice called apophenia, which is when our brains instinctively find patterns that contribute to meaningful relationships in various forms of collected data. This phenomenon has also been referred to as “patternicity” or “big data snooping”. While this tendency can be quite beneficial in the collection and analysis of many data forms, it also has the potential to lead to invalid predictions and biased findings. Many may presume that this phenomenon only impacts new hirees in various data collection fields, but many well-renowned scientists have been shown to participate in big data snooping as well. This is why it is vital for new hire training practices to put a large emphasis on how to detect big data snooping, and implement ways to avoid it.
This August, the website Datanami interviewed Ryan Sullivan, the CEO of a California-based analytics company, and he spoke about how to ensure that these relationships are not formed in a false context. For instance, one of the main ways a data analyst forms a proper hypothesis, is by identifying the errors that are found in the previously established relationships in order to weed out any findings that may be unreliable. By identifying and fixing these errors, we are less likely to form invalid predictions due to big data snooping that our minds naturally engage in. It is important for any data processing employers to provide new trainees with advanced training programs that teach them how to identify these errors, and how to ensure that they do not make the same errors in the future due to data snooping. This may seem like an easy concept, but with apophenia being a natural thing for humans to engage in, it can be difficult to stop the process in its tracks in order to produce more high-quality findings.
Although big data snooping has the potential to appear in any analytics field, it is most prominent when someone is working in a financial position. The human mind is even more susceptible to the formation of data bias when it is working with large sequences of numbers and equations. Andrew W. Lo of the Massachusetts Institute of Technology illustrates in his article entitled, “Data-Snooping Biases in Financial Analysis” how even when humans are given number sequences that are completely unrelated to each other, the human mind is still apt to form a pattern that is irrelevant to the data provided when given enough time and imagination. These sequences are often very difficult to process and solve, so it is not uncommon for financial analysts to naturally form big data bias, even when a pattern is not present at all.
Big data snooping and data bias formation is almost impossible to avoid when it comes to just one employee on the clock, but there are ways that companies can educate their trainees and hire other employees to identify and manage any data bias that is found. For instance, many analytic companies are hiring quality-control employees who have the sole responsibility of finding any potential data snooping and inapplicable patterns that may be present in collected data. By hiring someone whose only job is to focus on identifying biased data patterns, your company is more apt to produce more high-quality and reliable findings that were not subjected to large amounts of big data snooping and data bias.
Ensuring that your new employees also have a thorough training program on big data snooping, data bias, and correlation versus causation will also help ensure that your company is not a victim of big data snooping. Many times, people form these data biases due to falling victim to the causation process. For instance, if an employee notices a pattern in data that correlates to another pattern, they may assume that these patterns are the cause of why a relationship develops. It is important for employees to remember that correlation does not always equal causation, and teaching them how to keep hold of these natural impulses of data bias will provide you with optimal findings.
Big data snooping and the formation of data bias may be a completely natural practice for humans to participate in, but it is equally important to find ways to combat these practices in order for your company to remain reputable and overall successful. By implementing these training programs and new positions, your company will be sure to run more smoothly with much more reputable results.
