How Google Searches for Performance Measures If there’s one thing Google knows, it’s how to use software to wade through massive amounts of data and find what is most relevant.

How Google Searches for Performance Measures
If there’s one thing Google knows, it’s how to use software to wade through massive amounts of data and find what is most relevant. So it should come as no surprise that when the information technology powerhouse wanted to develop better managers, it started by looking at the data. As it turns out, Google found plenty to learn.
Like most businesses, Google had files of data about managers-results of performance reviews, surveys measuring employee attitudes, and nominations for management awards. Unlike most businesses, Google figured out how to analyze all that data to come up with a profile of the kind of manager whose team is most successful. The company’s people analytics group (which brings together psychologists, MBAs, and data-mining experts) analyzed 10,000 observations about managers in terms of more than 100 variables, looking for patterns. The initial finding was a surprise to some at a company that had once operated without managers: teams with good managers outperform teams with bad managers. But what makes a good manager? Under the leadership of Google’s HR vice president, Laszlo Bock, the company distilled its findings into a list of the behaviors that get results:
1. Be a good coach.
2. Empower your team, and don’t micromanage.
3. Express interest in team members’ success and personal well-being.
4. Don’t be a sissy: Be productive and results-oriented.
5. Be a good communicator, and listen to your team.
6. Help your employees with career development.
7. Have a clear vision and strategy for the team.
8. Have key technical skills so you can help advise the team.
Perhaps those points sound obvious. But keep in mind that someone hired as a programming or analytic whiz and later promoted to a managerial role might not have given much thought to, say, cultivating the ability to express interest in team members’ success, which ranks far above technical skills. Seeing this on a list identifies the behavior as something statistically related to superior performance not just in general, but at Google specifically. Furthermore, this is a behavior that can be measured (for example, by asking employees if their supervisor expresses interest in them), and it can be learned by managers who want to improve.
By building performance measures in the eight key areas, Google was able to evaluate its managers’ performance and identify those who needed to improve in particular areas. It developed training programs in the eight types of desired behavior. Before and after providing performance appraisals, training, and coaching, Google conducted surveys to gauge managers’ performance. It measured a significant improvement in manager quality for 75% of its lowest-performing managers. But Bock isn’t resting on that success. Google intends to keep crunching the data, in case the criteria for a successful Google manager change at some point in the future. One thing is for sure: Google will continue to follow the data.
What errors could arise in the way Google collects performance data on managers? How could it minimize these errors?

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