Are you a “good” boss?  Google’s “Project Oxygen” has taken a lot of time — a couple of years, actually — to study what makes a good boss. Their “people analytics” staff has come up with eight key attributes of good managers within their organization.¹ Among those eight attributes are:

• Technical expertise (as you might expect of a tech firm like Google);
• Having a clear vision and strategy for “the team”;
• Helping your employees with career development;
• Be productive and results-oriented; and
• Be an effective and empathetic leader.

What shouldn’t surprise us is that of the eight attributes of a good manager, the “ability to work well with one’s employees” was ranked first in Google’s study. “Technical expertise”, which Google had considered an absolute necessity to being a team leader, was ranked at the bottom.

You may recall that in the Bizmanualz blog, we’ve talked about the qualities of great leaders and what makes true leaders different from others.   Mostly, what separates leaders from mere managers are those intangible qualities, those “quirks” of personality that stump psychologists and sociologists to this day.

What makes for a good manager and exactly how do we quantify it? Well, it’s been tried — a number of times — but Google is putting their own spin on the concept. Despite past failings elsewhere, the people at Google think it’s possible to make the process of grooming leaders a reliable, repeatable process. Their goal is to make the process of hiring and training leaders like any other human resources procedure.

This should be welcome news to every other HR department if Google’s HR can do it right. Managing people, with all their complexities and variations, is (at best) extremely difficult and statistical analysis is helpful only to a point. The chief problem with “data driven management” is that people can’t easily be reduced to a set of predictable behaviors and outcomes — we are only human. Every statistic has to be taken with a grain of salt² but even more so when human behavior is the focus.

I’m going to follow Google’s Project Oxygen to see if there’s any merit to it. I sincerely hope so but I don’t harbor lofty expectations, either. Google may have a world of resources behind them but — people being people – it’s not a sure bet that Project Oxygen will deliver the goods.

So, what do you think? Can Google be successful — at something not so technical — when many other companies before them haven’t been?

NOTES

¹Bryant, Adam, “Google’s Quest to Build a Better Boss”, New York Times, 12 March 2011 — http://www.nytimes.com/2011/03/13/business/13hire.html.

²Seife, Charles, Proofiness: The Dark Arts of Mathematical Deception, Viking Press (23 Sept 2010). ISBN-13 #978-0670022-16-8.

For those who work with processes, we know that variability is the key factor.  The desired state is more consistency and less variability.  When processes have wide variability and inconsistent results, we call the process out of control.  When processes operate within established limits, the process is considered in control.

Typically, we attribute process variability to two causes—common cause and special cause.  Common cause variation is expected.  It is a result of the process design, machinery, and activities.  For example, I walk to the train station every day after work, and it takes six to 10 minutes.  The variation is due to factors like how long I have to wait for the elevator, how many times the elevator stops, and how long I have to wait at crosswalk lights.  These variations occur every day, and they are expected.  They are common cause variations.

Then one day it took 12 minutes to walk to the train station.  It took longer because someone approached me on the sidewalk and asked for directions.  They were lost, so I took a few minutes to explain to them where they are and how to get to where they are going, plus exchange a few pleasantries.  But that doesn’t happen very often.  In fact, it hardly ever happens.  The next day I return to the six to 10 minute window for my walk to the station.  It was a special cause of variation.

When addressing variation in a process, you have to understand if the variation is due to common cause or special cause.  The type of variation determines the activities we need to take to reduce variation.

To reduce common cause variation, it usually takes experimentation and/or statistical analysis to optimize the process.  Experimentation means changing something and measuring the results over time.  Statistical analysis means looking at results in different ways—stratifying and categorizing data in diverse manners and employing varying statistical methods like Pareto charts.

For example, I might experiment and collect data and find that if I leave at 4:45 instead of 5:00, the elevators are much less busy, and variation in the time to reach the station is reduced.

Special cause variation is typically discovered using root cause analysis.  In my example it was easy to identify why it took extra time to reach the station, but frequently the cause of unexpected variation is not so easy to see.  It takes an investigation using quality tools like 5 whys or fishbone charts to understand what happened.  Then, you can take action to prevent the unexpected cause of variation or simply ignore it because you realize that it happens rarely and the consequences are acceptable (as in my example).  I don’t mind missing a train to help someone out.