Algorithms and Formulas Are Us
We are moving rapidly into an increasingly “data driven” age of Human Resources and Compensation. This data is translated into information. Some of it is quantitative, some of it is visual, all of it is probably biased. Understanding the origin and reasons for potential biases will give the most successful companies an edge that may be impossible to beat.
In the end HR and Compensation are about winning. Winning the battles for talent. Winning the battle for performance. Winning the battle for consistency. Winning the battle for a pleasant and rewarding place to work. Having an advantage in those battles is what we strive for when we create unique programs, pay differently than our peers and build a culture that inspires. The age of data geeks is upon us and we need to be able to use that to our advantage.
The gold standard of data algorithms may be those used by search engines like Google. Here’s the rub, every piece of data is selected because someone wrote a program that indicated it should be selected. Every data outlier rejected didn’t fit a set of guidelines that was first created by a person. Stanford recently did a study in 2018 that showed a distinct pattern of bias in image-based search results. The results would probably not be categorized as discriminatory, but they also cannot be considered totally accurate.
When the result of an algorithm is a group of photos that appear on your screen, bias may not be that harmful. When an algorithm drives pay decisions, or more, we should understand how and why the algorithm works the way it does. A perfect example of this is survey data. The first step in a survey is deciding what data to collect. People make those decisions
Data that is hard to gather is often made a low priority. For example, we seldom see the details behind incentive plan terms, conditions and features. When data is messy, it may be avoided because the final report is simply too hard. For example, the lack of reward and recognition program details in most pay surveys. Data that is hard to understand may be summarized at a level where it lacks any impact. A good example of this is equity compensation data.
Finally, if the collected data just seems weird, it is often minimized or ignored entirely. Some of the most unique and effective pay programs never seem to appear in survey data, even when we know those companies are participants. We don’t love surprises. We like things to match our expectations. We want smooth transitions. We will modify data, job leveling, structures and more in order to fit the data into our models.
The pitch made by the data pros is that the data will fit an objective model and ignore our biases. But, it’s tough to ignore the biases of the data pros, especially if we don’t know what those biases may be. When data always meets your expectations, it may be due to the fact that your expectations are driving the data, instead of the other way around. In a data-driven age the art of HR and Compensation is even more important than ever before. Your expertise, insight and experience may be the secret sauce that gives your company the edge against peers who simply take the data at face value and execute accordingly.