R Tutorial: HR data architecture

Want to learn more? Take the full course at https://learn.datacamp.com/courses/human-resources-analytics-predicting-employee-churn-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Segmentation refers to a process of dividing a population into various subgroups which have similar characteristics. Without segmentation, your target group is a diverse population and it would be very difficult to obtain relevant and meaningful insights. Talent segmentation helps in designing customized HR interventions. Generally, an organization categorizes their employees into top management, middle management, and entry-level. Top and middle management work profiles are strategic and tactical, which require different skill set and hence face different labor market conditions as compared to entry level employees. Including them in the analysis may influence your insights, and thus, it is recommended to exclude these profiles for analysis. In this course, we will focus on entry-level employees, i.e., Analyst and Specialist level roles who form a majority of the workforce. In order to include employees at specific levels, you will use the filter() function from the dplyr package which helps subset a dataset based on certain conditions. Here we use the filter() function to return all rows where the level is Analyst or Specialist. Employee data resides in various HR data sources. For example, talent acquisition data can be pulled from Taleo or ADP, while engagement or exit survey data can be sourced from SurveyMonkey etc. It's time to bring more information captured across the employee life cycle to build one dataset comprising of maximum available information about each employee. By bringing relevant data together you can derive more insights. Here is a sample representation of HR data architecture. In an ideal scenario, various data sources are pulled into a data warehouse, which is then connected to t