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: “I 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.” People analytics expert Josh Bersin captures the essence of the practice with his observation that “We used to think the secret to productivity at work was ‘skills.’ Now, through the use of [scientific methods], we can understand that the secret is also ‘behaviors,’ ‘habits,” and ‘patterns’ that highly successful people adopt.” 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.
For example, here are some of the questions people analytics consultant Patrick Coolen has used analytics to address:
- What HR factors (e.g. engagement, leadership, individual and team characteristics, competencies and skills) are impacting client satisfaction, sales, net growth and the quality of our offerings?
- What factors are driving employee engagement worldwide?
- How is diversity within teams positively influencing our business goals (financial and customer satisfaction) and to what extent?
- Can we predict successful hires based on our selection data, resulting in hires that are more likely to perform and stay with our organization?
- What leadership characteristics have a positive impact on business goals including engagement?
- How is the notion of a shared purpose between employee and our organization contributing to engagement and business performance?
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’ll close this module with a case that is the modern counterpoint to the Hawthorne studies referenced in Module 2: Human Resource Strategy and Planning.
Google’s Project Aristotle
In 2012, Google launched an initiative to discover the secrets of effective teams. Channelling Aristotle’s “the whole is greater than the sum of its parts” idea, the initiative was named Project Aristotle. The project goal: answer the question “What makes a team effective at Google?”
Researchers started by reviewing the academic literature on teamwork and then began analyzing the composition of Google’s 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, “most confounding of all, two teams might have nearly identical makeups, with overlapping memberships, but radically different levels of effectiveness.” In their attempts to understand the dynamics, the team honed in on an idea that kept coming up in their research: the concept of “group norms,” or the “unspoken and often unwritten set of informal rules that govern individual behaviors in a group.” The influence of norms can be profound, since they typically override individual behavior preferences. Ultimately, the team determined that performance wasn’t 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:
- Psychological Safety. Team members feel safe with one another and are willing to take risks and be vulnerable
- Dependability. Team members get things done on time
- Structure & Clarity. Team members have clear roles, plans, and goals
- Meaning. Work is important and meaningful to individual team members
- Impact. Team members believe their work creates a meaningful change
Project Aristotle taught Googlers that “no one wants to put on a ‘work face’ when they get to the office. No one wants to leave part of their personality and inner life at home.” Perhaps more importantly, the project taught them about being human. As one Googler stated: “We can’t be focused just on efficiency. We want to know that work is more than just labor.” Times senior editor Charles Duhigg notes: the ‘‘employee performance optimization’’ movement [people analytics] has given us the tools to quickly teach lessons that once took managers decades to absorb. In conclusion, Rozovsky observes: ‘‘just 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.’’
Watch Google Project Aristotle team leaders Abeer Dubey and Julia Rozobsky’s summary:
An interactive or media element has been excluded from this version of the text. You can view it online here: http://pb.libretexts.org/hrm/?p=134
For additional perspective on team effectiveness, read The New York Times article “What Google Learned From It’s Quest to Build the Perfect Team,” which also includes findings from a related study into team effectiveness and intelligence.
- Bersin, Josh. "People Analytics: Here With A Vengeance." Forbes. December 16, 2017. Accessed August 06, 2019. ↵
- Ibid. ↵
- Coolen, Patrick. "The 10 Golden Rules of HR Analytics (crowd Version)." LinkedIn. September 16, 2016. Accessed August 06, 2019. ↵
- "Group Norms." BusinessDictionary. Accessed October 28, 2019. ↵
- Green, David. "40 People Analytics Case Studies – Part 3." LinkedIn. December 7, 2016. Accessed August 06, 2019. ↵
- Putting It Together: People Analytics and Human Capital Trends. Authored by: Nina Burokas. Provided by: Lumen Learning. License: CC BY: Attribution