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How Big Data Is Redefining Human Resource Practice

  Oct 26, 2018

Companies rarely make their strategic direction public, at least the finer grains of it. Its however possible to introspect the strategic direction that a company is taking by looking at the job they are advertising. One such noticeable trend is the increase in job adverts for data science talent. One particular job listing that caught my attention is a people data scientist listed by Pay Pal, below is an excerpt from their website.

“As the People Data Scientist – Principal, you will work with the and team lead and other Data Analysts, Data Engineers and Data Scientists responsible for strategic analytics initiatives, research and experimentation. You will solve essential business challenges such as: Improving collaboration across the organization; Predicting which employees are at risk of leaving the organization before they leave; Predicting which of our 1,000,000 + applicant each year would make great hires and many other; Through data allowing employees to plan and navigate their future careers at PayPal; Identifying what characteristics make a great sales person, engineer, etc.; and, Any many other key questions we will leverage data to solve. Reporting to the Head of Global People Analytics, you will be directly engage with PayPal People Strategy, Analytics and Technology Team to solve scope complex human and data questions, analyze, model, test, and build solutions that provide insights and recommendations”

HR has a history of using their gut feeling to make decisions. To remain competitive, we need to use metrics-driven data to build credibility as a strategic partner.

Suffice to say, big data for HR encompasses these three things at its core; predictive analysis, infusing data from other systems related to finance customer service marketing and other functions, and having the ability to make daily decisions based on evidence that are directly related to HR processes, using both to then design workforce structure and planning.

Data will talk to you if you’re willing to listen, however, the challenge comes when data transforms into bundles and stacks of unorganized and unstructured data sets. HR metrics and workforce analytics are not a guarantee return on investment. You need to be able to decipher what data is appropriate to collect. Learn how to use this information to increase managerial decision-making efficiency.

HR business partners need many skills to perform their job effectively. An important skill that is lacking in some HR business partners is the ability to analyze and interpret data. With this skill in their toolkit, the HR business partner can apply business analytics to analyze returns on investment on an HR program. Analytics can be used to support decisions with data through business cases, such as developing a leadership program, or piloting an employee satisfaction survey. Business analytics provide a wealth of value that can save organizations money. By connecting HR metrics to business performance, executives can see how HR impacts the bottom line.

There are very few prerequisites to begin predictive analytics: access to workforce data; having a workforce issue where understanding the probability of the future outcome is desirable; access to, or the ability to resource, advanced statistical analysis talent. It is imperative, however, that HR teams strive to progress in all areas of workforce analytics, rather than focusing on just one or two areas. Proficiency in one area, like metrics reporting, will not automatically lead to progress in predictive modeling. Predictive modeling is not about having the most data at your fingertips— despite the buzz about “Big Data.” It’s about testing the right hypotheses. For example, if you’re testing for attrition risks for call center employees, you will want to identify a set of potential reasons (or hypotheses) why people are leaving— long work hours, low pay, difficult commutes, poor job fit, and the like. You will need to collect the data to test those hypotheses, and that does not mean “all possible data.”

Timothy Oriedo

Executive Coach and Data Scientist Strathmore Business School


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