HR

During the 1800s, people problems were a very real concern in the workplace.
For the average blue-collar worker, most jobs were low-paying, monotonous and unsafe. Some industries experienced difficulty recruiting and retaining employees because of the poor working conditions workers were exposed to. As the means of production continued to shift from farmlands and guilds to city factories, concerns grew about wages, safety, child labor and 12-hour workdays. Workers began to band together in unions to protect their interests and improve living standards. Government stepped in to provide basic rights and protections for workers.


Forward-thinking employers recognized that productivity was connected to worker satisfaction and involvement and realized they could not meet production schedules with bands of disgruntled employees. In the late 1800s and early 1900s, the personnel profession that grew out of concerns about employee absenteeism and high turnover attempted to solve worker problems with such basic personnel management functions as employee selection, training and compensation. However, later it was realized that employee and workforce management, analysis and prediction is something which cannot be left totally on pre defined dispositions and beliefs.

HR Analytics and warehousing enable organizations to get meaningful insights from HR data collected from various enterprise-wide HR and non-HR systems. Human Resources has long been looked at as a touchy-feely business. The stereotype of its practitioners is that they give warm and fuzzy answers to most business queries. While they put Talent Management as a priority, yet they lack the analytical tools to be able to deliver the agenda. As Zach Thomas of Forrester says, “Forward-looking analytics that push well beyond traditional metrics are the cornerstone of this effort. But siloed systems, inconsistent data, and a lack of benchmarks and tools have made this increasingly difficult to achieve. To address this problem and become more strategic, HR professionals must get their arms around the data, identify key performance indicators (KPIs), settle on a technology approach, and infuse the data into their organization.”