宾夕法尼亚大学

人力资源分析

ratings

Course 3 of 5 in the Specialization 商业分析

People analytics is a data-driven approach to managing people at work. For the first time in history, business leaders can make decisions about their people based on deep analysis of data rather than the traditional methods of personal relationships, decision making based on experience, and risk avoidance. In this brand new course, three of Wharton’s top professors, all pioneers in the field of people analytics, will explore the state-of-the-art techniques used to recruit and retain great people, and demonstrate how these techniques are used at cutting-edge companies. They’ll explain how data and sophisticated analysis is brought to bear on people-related issues, such as recruiting, performance evaluation, leadership, hiring and promotion, job design, compensation, and collaboration. This course is an introduction to the theory of people analytics, and is not intended to prepare learners to perform complex talent management data analysis. By the end of this course, you’ll understand how and when hard data is used to make soft-skill decisions about hiring and talent development, so that you can position yourself as a strategic partner in your company’s talent management decisions. This course is intended to introduced you to Organizations flourish when the people who work in them flourish. Analytics can help make both happen. This course in People Analytics is designed to help you flourish in your career, too.

From the lesson

Talent Management and Future Directions

In this module, you explore talent analytics: how data may be used in talent assessment and development to maximize employee ability. You'll learn how to use data to move from performance evaluation to a more deeper analysis of employee evaluation so that you may be able to improve the both the effectiveness and the equitability of the promotion process at your firm. By the end of this module, you'll will understand the four major challenges of talent analytics: context, interdependence, self-fulfilling prophecies, and reverse causality, the challenges of working with algorithms, and some practical tips for incorporating data sensitively, fairly, and effectively into your own talent assessment and development processes to make your employees and your organization more successful. In the course conclusion, you'll also learn the current challenges and future directions of the field of people analytics, so that you may begin putting employee data to work in a ways that are smarter, practical and more powerful.

Talent Analytics: The Importance of Context12:51

Interdependence6:20

Self-fulfilling Prophecies9:40

Reverse Causality4:30

Special Topics: Tests and Algorithms5:56

Prescriptions: Navigating the Challenges of Talent Analytics15:14

Course Conclusion: Organizational Challenges 110:14

Course Conclusion: Organizational Challenges 2 and Future Directions19:21

Goodbye and Good Luck!0:32

Meet the Instructors

Cade Massey
Practice Professor
The Wharton School
Martine Haas
Associate Professor of Management
The Wharton School
Matthew Bidwell
Associate Professor of Management
The Wharton School

The fourth and final challenge with talent analytics that I wanna emphasize today is

reverse causality.

And this is the tendency,

when we see two correlated factors, to believe that one causes the other.

Especially if there's an intuitive direction, we look for

this kind of casuality as natural for us to seek it.

But the trouble is, often the casuality, there may be no casuality at all but

worse, the casuality might actually go in the other direction.

So for example, are charismatic leaders more successful?

This is a widely held opinion and it makes a lot of sense.

One can make the argument that well there's all these benefits to being

charismatic.

It's naturally associated with successful leaders.

And so sure.

Let's just go with that.

Charismatic leaders are more successful.

Fortunately for us, a few people have studied this.

Agle et al a few years ago published a paper in the Academy of Management where

they looked at charisma and success among 128 CEOs of major US firms.

They used a longitudinal design so

they could look at the perceptions at one time and the outcomes at another.

So, what they found was that charismatic CEOs did not actually have

more organizational success down the road.

But interestingly, successful CEOs were perceived as more charismatic.

So it is not the case that charisma leads leaders to be more successful, but

rather just the opposite.

That the success itself breathes in us the perception that they're charismatic.

We find similar dynamics around power and the warning

is that we have to be very careful about what causal stories we tell ourselves.

So we're driven to make sense of the world.

We're driven to make sense of the world we live in,

to build causal stories from what we observe.

The trouble is that leads us to see things that don't exist.

It leads us to give people credit and

people blame, in situations they may not deserve it.

An interesting study that showed this in kind of profound ways was

an early study,1977 study by Jim March,

the very famous organizational scholar Jim March, and his son, also James March.

They looked at the careers of school's superintendent in Wisconsin.

So they got this great data set,

they could look over a long stretch of time, 32 years.

Everybody who had been a superintendent in the state, and

the trajectory of their career.

So you can imagine, how do you think this goes?

What would you expect to be the story?

You would expect that there's this great range in abilities among superintendents.

And there are a range of differences in the jobs.

You might want the jobs in the bigger cities and not be as interested,

or maybe a few people want them, in the you can call it small towns.

But you'd expect great variation in both places, and there's probably this formal

evaluation system that makes sure that the best people get placed in the best jobs.

This is what we'd expect, this is kinda what the system is designed to do.

What they found, however,

is that when you look at what happens, it is very difficult to distinguish

the observed career paths from what you'd expect from a completely random process.

That even though we might look and say, well this superintendent achieved this

success, or this superintendent achieved this failure, or this guy's in this place,

this woman is in that place, for certain stories.

We can tell the stories.

It turns out there's very little difference.

It's hard to find any difference from what we observe in the data and

what we would've expected if it was just completely randomly distributed.

Matching people to jobs by chance.

So, these guys conclude their study with a profound statement that's a good

warning to all of us about reverse causality.

The normative lesson is, the stories we tell each other about success and

failure in top management, like the stories we tell about success and

failure in gambling, are in large part

fictions intended to reassure us about justice and encourage the young.

So a little bit of a cynical note from Jim March, his son there.

But it's well intended.

It's well intended because he wants to urge us, don't draw these inferences.

Be careful about causal reasoning.

Be aware that often the causality runs the other way.

And you've gotta run the data, basically.

The principle means by which you do this with talent analytics is make sure you

have some causal structure in your data, that you're using longitudinal panels,

that you're very careful about the inferences you have to draw when you're

stuck with just correlational data.