As a society and as organizations, we are inundated with data and information. However, in an organizational context, that data has no value until it can be applied to solve business problems. As one people analytics leader put it: \u201cI am not in the curiosity business. We need to know the relevance to the business before we spend time and energy to work on a problem.\u201d[1]<\/sup><\/a> People analytics expert Josh Bersin captures the essence of the practice with his observation that \u201cWe used to think the secret to productivity at work was \u2018skills.\u2019 Now, through the use of [scientific methods], we can understand that the secret is also \u2018behaviors,\u2019 \u2018habits,\u201d and \u2018patterns\u2019 that highly successful people adopt.\u201d[2]<\/sup><\/a> The amount of available data and processing capacity also allows us to solve questions of greater complexity, quantifying issues so management can evaluate the significance and respond accordingly.<\/p>\n For example, here are some of the questions people analytics consultant Patrick Coolen has used analytics to address:[3]<\/sup><\/a><\/p>\n People analytics is still, relatively speaking, in its infancy, with practitioners reporting case data with a mixture of wonder and excitement. Hype and adoption challenges aside, the potential is real. We\u2019ll close this module with a case that is the modern counterpoint to the Hawthorne studies referenced in Module 2: Human Resource Strategy and Planning<\/a>.<\/p>\n In 2012, Google launched an initiative to discover the secrets of effective teams. Channelling Aristotle\u2019s “the whole is greater than the sum of its parts\u201d idea, the initiative was named Project Aristotle. The project goal: answer the question \u201cWhat makes a team effective at Google?\u201d<\/p>\n Researchers started by reviewing the academic literature on teamwork and then began analyzing the composition of Google\u2019s teams, including social habits, hobbies and educational backgrounds. However, regardless of how they parsed the data, the researchers were unable to find patterns or evidence that the composition of a team made a difference. As The New York Times reports, \u201cmost confounding of all, two teams might have nearly identical makeups, with overlapping memberships, but radically different levels of effectiveness.\u201d In their attempts to understand the dynamics, the team honed in on an idea that kept coming up in their research: the concept of \u201cgroup norms,\u201d or the \u201cunspoken and often unwritten set of informal rules that govern individual behaviors in a group.\u201d[4]<\/sup><\/a> The influence of norms can be profound, since they typically override individual behavior preferences. Ultimately, the team determined that performance wasn\u2019t a function of who was on the team per se but how the team worked together, resulting in the five key characteristics of effective teams:[5]<\/sup><\/a><\/p>\n Project Aristotle taught Googlers that \u201cno one wants to put on a \u2018work face\u2019 when they get to the office. No one wants to leave part of their personality and inner life at home.\u201d Perhaps more importantly, the project taught them about being human. As one Googler stated: \u201cWe can\u2019t be focused just on efficiency. We want to know that work is more than just labor.\u201d Times senior editor Charles Duhigg notes: the \u2018\u2018employee performance optimization\u2019\u2019 movement [people analytics] has given us the tools to quickly teach lessons that once took managers decades to absorb. In conclusion, Julia Rozovsky observes: \u2018\u2018just having data that proves to people that these things are worth paying attention to sometimes is the most important step in getting them to actually pay attention.\u2019\u2019<\/p>\n Watch a summary of the Google Project Aristotle findings by Charles Duhigg:<\/p>\n\n
Google’s\u00a0Project Aristotle<\/h3>\n
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