Special Topics: Tests and Algorithms

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来自 University of Pennsylvania 的课程

人力资源分析

个评分

University of Pennsylvania

人力资源分析

个评分

课程 3（共 5 门，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.

从本节课中

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

与讲师见面

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

So, we've covered the four challenges that form the heart of this module.

The four challenges of dealing with talent analytics and

drawing correct inferences from the data that you're crunching.

We have one special topic, we wanna cover before drop into the prescriptions.

The special topic is Test and

Algorithms, which are receiving a great deal of attention lately.

So, for example,

the cover story on Time magazine this summer was How High Is Your XQ?

And XQ is intended to be your personality quotient, and

this comes from the rise in firms promising to help

companies hire more effectively by giving tests to the potential hires.

So, a real cottage industry has grown up around this and it's related to big data.

It's related to the increase in computing power and the quest,

the never-ending quest for a better way to hire employees.

So, we're going to talk about this separately.

Matthew's going to talk about this separately in a hiring module, but

I want to say a quick few words about it,

especially because it's related to other news that's been covered lately.

So, another front page story,

this time the New York Times, on can an algorithm hire better than a human?

Again, something that's arisen in recent years.

Fostering the people on the next topic we're here talking about, but

a few comments on these very important and relatively new topics.

The first is, let's understand some of the pros and cons,

because there are pros undoubtedly, but there are also cons.

So, clearly you can process more efficiently through these test and

through these algorithms, you can process many more candidates.

You can aggregate them much more easily than doing so manually or intuitively.

So, processing efficiency is a plus, so is broad search.

Because you've got this efficiency, you can bring many more people

into the filter, into the funnel that you're trying to process.

And this can be very helpful.

We, in general, think that people search too narrowly, they're too

confident in being able to identify, and so broad search is a good thing.

Finally, done well this is unbiased.

The machines that are programmed don't have the kinds of biases we worry about.

Don't have the human kinds of stereotypes,

self-fulfilling issues that we talked about in our previous segment.

Now, that's once their programmed.

The programming itself can be biased or

the historical data they're based on can be biased.

But the machines themselves were unbiased.

And this can be a real advantage for us.

Downsides though, and this is important.

And this is what gets lost in these glossy articles, or in so

many sales people pushing for these new tools is that there's some cons.

One is they're hyper-focused.

These algorithms and

these tests will do exactly what they're programmed to do and nothing else.

They don't have the sense to balance it in the way that humans would, so

if you go out, and tell them you want X they're gonna focus exclusively on X and

if you just forgot to put Y in the algorithm,

you're not gonna get it, you're not get any Y, it's gonna get exactly.

And this can be dangerous.

It can be very powerful, but it can be very dangerous.

It's not that they don't work well enough, the problem is they work too well.

And it's a very sharp tool for us to be working with.

Second is most of these tools have relatively low explanatory power.

Meaning, there might be a little signal, they might be picking up on something.

But they're not in most cases explaining a lot of the variance downstream.

They're not helping us really understand that much of what's going on.

And so, we might draw on them, but you don't wanna put too much weight on them,

if they're not really explaining much of the downstream variance.

Some prescriptions that come from using these because by all means we

need to be developing new ones, we need to be trying to take advantage of

the ones that are good out there, but they need to come with some prescriptions.

So, one, do the science, do the rigorous testing and importantly,

identify what works best in your setting, very few of these are general,

very few of these are tests or algorithms that are gonna work everywhere.

Even something as fundamental as GPA, you can think of it as an algorithm even

though it's been widely available for a long time, even something like GPA can,

the applicability varies across organizations.

I was at a conference a few months ago, where someone from Google stood up and

said, we've crunched the numbers, and we've established once and for

all that G.P.A. is not a valid predictor for performance inside our firm.

Interesting, right?

Very interesting.

But, Goldman Sachs stands up and says, we've crunched the numbers and

we've established definitively that G.P.A.

is a valid predictor for performance inside our firm.

Same numbers, different environments, different validity.

You've got to run the numbers inside your organization and

take with a big grain of salt anything that somebody tells you about

the generality of a particular task or a particular algorithm.

Second is provide human oversight.

These are very sharp tools, and they need to be carefully used.

And sadly,

I speak from experience in that, you could design a very good algorithm, and then

once you start using it you start sawing off edges of furniture here and there.

You need human oversight of this.

You need humans to be the once programming.

You need humans testing these things,

very importantly, you need humans error checking.

You gotta make sure that the output on the other end makes sense and

broadly corroborates what you expect.

And until you've got a lot of experience with a particular algorithm,

you've gotta be careful.

Finally, use multiple tools.

This is one of the best prescriptions we can give.

It kind of licenses sampling these new tools,

it licenses using them if used in conjunction with many other tools.

So, by all means, take advantage of new technology, bring the new test in, drop

them into the, see what they relate to, just don't put too much stock in any one

of them, any one test or any one algorithm until you've really proven it up.

But really, in general, with performance evaluation and talent evaluation,

we want as many diverse signals as possible.

So, this is a great new role for us.

It brings in some new signals from some new places and we want that diversity.

But it should only be a complement to the other more traditional measures.

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