Data Science Basics: HR Analytics 101
Data science is quickly becoming an important part of understanding organizations and their workforce. As noted by the ADP Workforce Analytics Workbook, "while 75% of large organizations have access to data, only 46% have deployed workforce analytics." Even those, do not always know what to do with the information and results of those analytics.
Human resources in particular is primed to make effective use of this data analytics because of the massive amounts of people data HR collects and uses every day. With new insights into how both organizations and people work, HR Analytics have important business value to organizational leaders. Here's a primer to help jump-start the HR Analytics conversation.
What Is Data Science?
Data science is a way to organize, manage and understand information that incorporates computer science, modeling, statistics, analytics and mathematics. As NYU explains, "At its core, data science involves using automated methods to analyze massive amounts of data and to extract knowledge from them." As much of our work is now digital and the way we work can be observed more easily, there are tremendous amounts of information available to explore and learn from. Data science is the disclipline of gathering, analyzing, and explaining all this information in ways that are reliable and useful.
How is Data Analyzed?
As noted by HR Bartender, Sharlyn Lauby, there's a shift occurring in HR from "people science" — which relies on organizational and industrial psychologists — to "data science about people." Instead of people asking workers how they're feeling or surveys to get answers about their job satisfaction, new data analytics tools can deliver benchmarks and establish a foundation for people-centric solutions, often using existing data.
Also data analysts can evaluate and explain the relevance, value and credibility of organizational data. For example, not all metrics are relevant to the long-term satisfaction of employees. Trained data analysts monitor and filter the information and target the right data sets, then evaluate the results. They often help to create charts and graphics that describe what the data means and its value.
Decide What Questions to Ask.
Many HR departments are reluctant to embrace new technologies, especially those related to data — what if they don't generate expected ROI, or what if they uncover problems? These concerns can be addressed by getting clear on the objectives. What do you want to find out? Why is it important? What will you use it for? For example, are significant numbers of employees leaving after a certain period of time? Are specific performance shortfalls as a result of personal challenges or corporate cultural concerns?
Then, identify the data to collect and explore the options for gathering the information, understanding it, and using it effectively. A data scientist can guide this process and offer help choosing and using software or other tools.
Empowering Your Efforts
Next, consider using data from outside the HR deparatment. For example, financial and operations data can help HR better understand the organization, it's performance, and the questions you are interested in. Also think about what data you will want to track over time to establish benchmarks internally and to compare with other organizations.
Remember, analytics techonology can only reccomend actions; it cannot make decisions. Humans are still needed to interpret reccomendations and decide whether they make sense for the organziation.
Learning to understand and effectively use data analytics a powerful tool that offers real business value to organizations. When combined with HR's understanding of the organization and its people, HR Analytics will be an essential part of understanding organizations and their performance.
To learn how to turn your people data into business objectives your HR department can deliver on download the ADP Workforce Analytics Workbook.
