What is Data Science and How is it Changing the Face of HR?


What is Data Science and How is it Changing the Face of HR?


The topic of “data science” has been surfacing frequently in HR circles in the last decade or so. But what does it mean? Data science in relation to HR functions is the complex process of data mining and data analytic techniques applied to people-related data. It is mainly used to draw powerful insights to effectively manage employees in order to reach business goals quickly and with high productivity.

While in many ways still in the early stages of understanding and application, the HR metric trend is becoming more and more widely accepted as an HR-related business strategy. According to a 2015 report by Deloitte, 35 percent of companies surveyed said they were actively developing data-analysis capabilities for HR.

To help companies determine whether building a strategy around HR data science mining is for them, we’ve compiled a list of some HR data science basics, as well as the caveats to be mindful of with each.

1. Cost per hire

A commonly-used HR metric, this calculation factors in all costs related to hiring, including time spent by hiring managers (and their salaries), job board postings, relocation expenses, etc.

Don’t: Consider cost-per-hire numbers on their own. Some potential hires will inevitably cost additional resources in order to woo, especially those filling more high-profile roles.

Do: Keep in mind for budget planning. Just remember to think big picture – at the end of the day, hiring comes down to quality over cost.

2. Revenue per employee

This metric is a simple calculation of total revenue divided by total number of employees. Paying attention to this year-over-year trend can be useful when comparing against other companies in the same industry.

Don’t: Use these numbers as a way to reprimand individual employees that may not be operating as efficiently as possible.

Do: Use these numbers to consider employee productivity overall and determine whether strategy is needed to improve productivity levels.

3. Employee turnover costs

This trend-based calculation looks at how much it costs your company each time someone leaves, based on the cost to hire and train a replacement for that position. According to a study by the Center for American Progress, the cost of losing an employee can cost up to 213% of the salary for a highly trained position.

Don’t: Hold onto employees just to avoid the cost of hiring and training replacements.

Do: Be intentional about regularly measuring employee satisfaction to hopefully minimize turnover.

4. Predicting retirement

Data science is also being used to predict which employees will be considering retirement next, using historical data and complex algorithms. These estimations used to be based on age and tenure, but experienced data scientists are now taking into account recent changes in role, pay level and incentive eligibility, etc., when calculating retirement predications.

Don’t: Use these predictions to neglect employees deemed as likely being close to retirement.

Do: Use retirement prediction data to start planning ahead for the roles that will likely become open positions in the near future.

While the use of data science in HR is increasingly becoming an important tool for companies, it’s important to remember that these calculations should help inform decisions made by management, but not definitively make them. Be intentional about which data from internal sources will be used for what purposes, and then use it to help improve management and performance – not to weed out the weakest link.

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