Google Experiences with People Analytics

This paper reviews Google experiences with people analytics. Basically, the concept of people analytics originated with Google at around 2007. By 2007, the company was hiring about 200 workers every week and by 2015, the company was already getting two million job applications to review before hiring on weekly basis. Google is said to give higher importance in hiring compared to training, based on believe that having the right workforce reduces the cost of helping them to perform. Thus, the company’s people analytics focuses more on hiring criteria and accuracy, compared to other aspects of people management. The process was based on different approaches. In the first approach, the company people analytics group compared the predictions of the interviewer with the actual performance of new hires to determine interviewers’ efficiency in predicting performance (Titapu, 2017). This demonstrated disappointing results, were bias ruled in most cases. Consequently, the company considered automating its interview process to eliminate biasness. The company currently uses qDroid application to guide interview. Interviewers only key in the job functions of the candidate and the interview questions are generated by the application. The qDroid formulated questions are extensively tested to predict job performance of a candidate accurately. Further analysis also demonstrated that accurate prediction on a candidate can be achieved with more interviews. However, when they go beyond four, the interviewer feedbacks bring about diminishing returns. The company thus settles for at least four interviews, where by positive interview results from the four interviews are likely to give an employee with reliability level of about 86% (Titapu, 2017).

Although the company has managed to solve a lot of people analytic issues using data, it is reluctant to outsource all these functionalities to algorithms. The company’s people analytic engineers recently developed an algorithmic based logistic predictive model, which was effective in predicting decisions on promotion with 0.1 error rate, based on minimal easily measurable attributes. However, the engineers refused to outsource this attribute, claiming that some decision are best made by humans and can be made better by learning form mistakes. The company is basically able to effectively apply data in talent management issues, especially with change of culture in the organization, by recognizing where application of data is useful and where it is not. This recognition helps the company to identify its people analytics process shortcoming, based on the existing data. This act prevents overextending the attrition and engagement data, which despite their usefulness, the data fail to offer enough resolution to gain clear guidance on leaders that should be changed or retained (Vail & Dudek, 2017).

Google depends highly on people analytics to make important hiring, development and retention decision in the company. The company has been working on people analytic effectiveness since around 2006, and has been trying to refine it to meet the company’s general people analytic needs. The company’s people analytics group is under the Vice President, who the group reports to, directly. It also has a representative in every main HR function. The group produces various products which include dashboards and workers surveys. The group also tries to identify intuitive correlations and to offer recommended actions. The main aim of this group is to substitute metrics and data for opinions use (Shrivastava, Nagdev & Rajesh, 2018). Majority of people analytics approaches in Google are very powerful and useful, which include Google extensive range of fun activities, 20% time and free food that are maintained and implemented based on data. Some of essential current and past Google people analytic practices that are data driven include research to analyze internal data reams to establish the kind of great managers are important for retention and top performance. The data demonstrated that periodic one-on-one coaching that involves expressing interest in workers and personalized feedback are additional aspects to superior technical knowledge, in creating a successful leader. Another approach is PiLab that performed applied experiment in the company, to establish the most effectual techniques for upholding productive environment and managing people. The approach also enhanced health of workers by lowering intake of calorie using scientific experiments and data to control the free food program (Sullivan, 2013).

The company also has retention algorithm which successfully and proactively predict the employees that are more probable to be hard to retain, permitting the company’s managers to act on time and also allowing personalization of retention solutions. Google also has predictive models that apply “what if” analysis to repeatedly enhance the projection of impending people management opportunities and problems (Shrivastava, Nagdev & Rajesh, 2018). It in addition employs analytics to create more effectual workforce planning that is important in changing and rapidly growing the organization. Google also use analytics to address diversity issues. The team of people analytics carried out analysis to determine the source of weak promotions, diversity, retention, and recruitment, especially among women engineers. The produced results in promotion, retention and hiring were measurable and dramatic. The company also used algorithms to compute top performers value, by determining the variation between average technologists and exceptional technologists. Obtaining the value of top performers persuades executives to offer resources needed to develop, retain and hire exceptional talents (Sullivan, 2013).

Google has also used people analytics to discover that increased innovation originates from a combination of three aspects that include fun, collaboration and discovery. It has thus deliberately structured its workplaces to maximize on collaboration, fun and learning. Although fun management may appears unnecessary, the company data designates that fun is a major factor in collaboration, retention, and attraction. Google has also used data to enhance learning and discovery in the organization. Instead of centering on traditional classroom, the company stresses on hands-on learning such as project rotations, coaching through more experienced people and also learning from past failures. The company people analytic team does not force people or the management to change. On the contrary, the team acts as a company’s internal consultants where it influence change through data power and presented action recommendations. The group uses data to influence and change people opinions (HR Grapevine, 2017).

People analytics which involves the use of algorithms and data is central to Google’s DNA and plays great role in enhancing workforce management in the company. It has played great role in in enhancing the company’s innovative ability, through talent management. The company utilizes data to develop and recruit workers, an approach that has been found to be highly effective. The use of people analytics has brought great management advantage to the company and helped it to save cost on its recruitment process. It has also lowered attrition in cost effective manner for instance, using data to extend new mothers maternity leave has reduced attrition among new mothers by 50%. Generally, the use of people analytics has played a great role in promoting company’s growth and enhancing high level of productivity in the company. Google acts an example on how organizations should use analytics to add to their competitive advantage (Digital HBS, 2017).

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