Measuring and managing people in the Big Data age

There’s so much to be said about measuring people to gauge and reward performance, to identify potential, and to shore up areas that need work. But the darker side of measuring people…the side where numbers are used to decide who stays and goes, is a tougher thing. Systems that require ‘averaging out’ the scores force managers to rack and stack their people regardless of how many of the best people they’ve hired and cultivated. These scales punish the managers and members who’ve built the highest-performing teams.

Just yesterday, Ron Webb talked about the bully as boss. These impersonal tools become a tool to create emotional distress rather than fostering a creative and collaborative work environment.

What’s worse, as we move toward a SaaS-based world of performance management tools like SuccessFactors, there’s high likelihood that analytics and dashboard-based systems will be broadly adopted. Will this be the way to drive higher performance levels from the workforce or just a way to support poor people management and/or bullying with heartless numbers?

Microsoft and the Pony Express

In Performance Management and the Pony Express on Harvard Business Review just this morning, Marcus Buckingham talks about Microsoft’s decision to dump the numerical scale altogether. Marcus is a smart guy we’ve followed for years who understands that numerical performance management end up being something very different from what these systems intend:

It appears that, when it comes to rating someone else, our own strengths, skills, and biases get in the way and we end up rating the person not on some wonderfully objective scale, but on our own scale. Our rating of the other person simply answers the question: “Does she have more or less of this strength or skill than I do?” If she does, her rating is high; if she doesn’t, it is low. Thus our rating is really a rating of us, not of her.

Management is far more complicated than simply rating others. It involves coaching, encouraging, often directly mentoring to tease out the best qualities in the people who work for us. It involves figuring out the best use of an individual’s skills and talents rather than simply trying to put expectations on them based on narrow role definitions.

More knowledge workers, less routine work

Reading Keith Swenson on Social Enterprise Today, in a world where more people are becoming knowledge workers rather than performing routine work, the need to use less rigid measures than cold, hard numbers is becoming key to team building. The workplace is less a factory floor than ever before, yet we’re using more and more industrial analytics to find the best of our people? Does that make sense?