Introduction to People Analytics

Loading...

來自 University of Pennsylvania 的課程

人力资源分析

個評分

Explore Related Videos

At Coursera, you will find the best lectures in the world. Here are some of our personalized recommendations for you

University of Pennsylvania

人力资源分析

個評分

課程 3（共 5 門，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.

從本節課中

Introduction to People Analytics, and Performance Evaluation

In this module, you'll meet Professors Massey, Bidwell, and Haas, cover the structore and scope of the course, and dive into the first topic: Performance Evaluation. Performance evaluation plays an influential role in our work lives, whether it is used to reward or punish and/or to gather feedback. Yet its fundamental challenge is that the measures we used to evaluate performance are imperfect: we can't infer how hard or smart an employee is working based solely on outcomes. In this module, you’ll learn the four key issues in measuring performance: regression to the mean, sample size, signal independence, and process vs. outcome, and see them at work in current companies, including an extended example from the NFL. By the end of this module, you’ll understand how to separate skill from luck and learn to read noisy performance measures, so that you can go into your next performance evaluation sensitive to the role of chance, knowing your environment, and aware of the four most common biases, so that you can make more informed data-driven decisions about your company's most valuable asset: its employees.

Introduction to People Analytics8:45

Goals for the Course1:40

Course Outline and Overview3:50

People Analytics in Practice4:55

Performance Evaluation: the Challenge of Noisy Data6:46

Chance vs. Skill: the NFL Draft22:08

Finding Persistence: Regression to the Mean11:56

Extrapolating from Small Samples5:53

The Wisdom of Crowds: Signal Independence5:56

Process vs. Outcome7:47

Summary of Performance Evaluation3:36

與講師見面

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

Hello and welcome to our People Analytics course.

This is Cade Massey, Professor of the practice here at the Wharton School

in the Operations Information and Decisions Department.

I'm joined by >> Matthew Bidwell, I'm

an Associate Professor in the Management Department also at the Wharton School.

>> I'm Martine Haas.

I'm also an associate professor of management in the Wharton School.

>> A lot of corporations say that people are their most important asset.

In a sense, people analytics is about taking that idea seriously and

behaving as if that were true.

So if you think about what's happened in a lot of other fields over the last 10,

20 years, it's being the increasing use of data and analytics to make decisions.

So if you think about finance, it's really being revolutionized by these

quantitative models which they use to price assets,

understand markets, occasionally to blow up the financial markets.

But when they're not doing that, really to try and

understand what's the way we can make profits?

If you think about marketing, increasingly, and

we have a marketing analytics course, which is really focusing on

how people are now using more and more data to understand their customers better.

To segment, to focus on how can we create products that they want, and

how can we then really target our marketing towards them?

The idea is to apply the same ideas to people, okay?

In an organization when we manage people, we have to make a lot of

different decisions, those decisions should be informed.

So people analytics is really just the use of data and

analytic tools to make better decisions about how we manage people.

So a lot of the energy around people analytics are stemmed from two places.

One is obviously this interest in if we can manage our people better,

it's obviously gonna benefit the organization.

The second place it comes from is the sense that we now have more and

more data about what people are doing that we could actually put at the service

of these decisions.

Also for a while now organizations have been gathering data

on the basics of their personnel.

Their performance ratings every year, how they're being paid, what their jobs are,

to give us a better sense of what's going on in the organization.

Maybe be able to identify our high performers and so on.

Increasingly that's being supplemented by other data.

So we now see applicant tracking systems where we put in the details of everybody

who applies to the job, so we can start to understand how we're screening them.

We have to addition to that now workflow data.

If you work in a call center, you're going to have your calls monitored, so

we know how many calls you make every hour.

What's the rate?

How many people hang up on you?

We have increasing data on customer satisfaction, as well,

through the surveys you fill out at the end of those calls.

So you know more and more about how people are performing.

You can start to think about what's driving them.

You see some of the same with software engineers, where more and

more of their work is being monitored as they check various pieces of code into

repositories and so on.

We can start to analyze that.

There are new dimensions of this with communication.

More and more of our communication is happening in technology mediated ways.

Through email, through telephone, through bulletin boards and so on.

So, we can see who's talking to who.

Start to understand where does collaboration happen?

Who are the hubs within the organization?

And even now we know where people are increasingly with wearables and

other devices tracking people's location.

And so we know more and more about what people are doing in the organization.

And the idea is we can start to bring some of that data to bear

on understanding how could we manage that better.

So we've seen this in a wide variety of different kinds of decisions.

So from the basics of actually figuring out what good performance means, and

who's exhibiting it to making decisions about how to manage them.

Who should we be hiring?

Who should we promote?

What are kind of the career paths that make sense within the organization?

What do the collaboration patterns look like here?

Who are our critical communicators?

What can we do to get communication improved?

On top of that, increasing so I wanna put data behind some of our decisions about

how to manage overall programs.

So there are often issues around is our training working?

What's our return on investment on the training?

Our new fast-track promotion scheme, is that a good idea or a bad idea?

If we change incentives, what happens?

Traditionally we've had to go by hunch and feel for making these decisions.

Increasingly, though, we can put data behind them.

That's really the promise here.

People analytics is a field that's really been emerging over the last few years and

where there's tremendous energy at the moment.

Part of what we want to do during this course is transmit a little bit of that

excitement to you.

So we thought we'd just start by saying why do we get excited about it?

At least Martine and I both have English backgrounds, so

getting exciting is obviously challenging for either of us.

But Cade is American, so I think he'll be able to tell us slightly better.

Cade, what is it about people analytics that excites you?

>> Well, where I come from academically is the decision making world,

which mostly studies the mistakes people make,

the biases they have in their judgment and in their decisions.

And in recent years,

we've gotten a little more interested in helping people fix that.

Not just documenting the mistakes, but actually improving their decision-making.

And data provides this great opportunity.

It's not a panacea.

There's no guarantee because you have data you make better decisions.

But it is an opportunity to improve decision-making.

And so it's this great new avenue for the decision-making world, and

then the people side of things is just what I care the most about.

So we have colleagues that study different elements of the business world.

And you kind of choose what you are most passionate about,

what I really care about is individuals.

This is one of the main ways they are affected, how organizations treat them,

who hires them, who promotes them.

And it's one of the main ways that organizations then prosper or

don't, because individuals and people are so important.

So it's this intersection of better decision-making,

which is my academic background, and

people's lives and what people provide to organizations that I care most about.

>> Martine?

>> So my research is in the area of organizational behavior.

And my main interest, what I'm really, spend a lot of time thinking about and

studying and trying to figure out is how organizations can learn better from what

they do in one part of the world, how they can transmit that learning so

that they can do better in their operations in another part of the world.

And of course it's people who are doing that, right?

So it's people who are learning from each other, who are collecting information and

experiencing and developing expertise in one part of the organization,

one part of the world.

How do they, how do other people,

especially in large distributed organizations, learn from that?

And so this is a very distinctive area in terms of people analytics.

It's kind of how do people work together better in order to get their work done?

So it's not so much the selection and hiring components,

which you guys think about a lot.

But really my focus is mostly on these collaboration elements.

How can people work together more effectively and

perform better as a result?

And so, again, people analytics provides a really wealth of

possible data to look at people collaborating because

previously we've just had to sort of observe what people are doing.

Are they talking to each other?

Are they making phone calls?

We've had some data, we can ask people in surveys some of this information.

But especially in large organizations, where there's a lot of stuff going on,

now we have increasing sources of data to enable us to really track how much people

are using databases to store what they know, that somebody

else can access from somewhere very far away, and maybe many months later.

We have all sorts of interaction forms,

communication through question and answer online formats.

And obviously through conferences and all sorts of other ways.

That we now are collecting data on to see where these things are effective and

are helping people to do their work better.

Or maybe they're just a time sink and you're spending way too much time

trying to kind of access these sources of information.

So trying to figure out what works and what doesn't work, what enables teams,

individuals, and the organization as a whole to collaborate more effectively is

really where my passion and interest in this field lies.

>> Yeah, and I think my motivation is fairly similar.

I mean I came at this from a study of employment relations and

there I really got into this because I think many of the things that Cade's

talking about that struck me.

Where do we spend our time?

At work.

Where does our money come from that enables us to live?

At work.

Who produces staff, people in jobs within organizations and

so to kind of, for me in order to understand what makes a good society or

bad society is really now how well does employment work?

How well is it organized?

Certainly the hope is that by better analytics, putting better data,

we can make better decisions, which would obviously be good for organizations,

but hopefully a big part of this will also be providing better places for

people to work where they will enjoy their work more and get more out of it.

So that's my hope for this field.

探索我們的目錄

免費加入並獲得個性化推薦、更新和優惠。

Coursera 致力於普及全世界最好的教育，它與全球一流大學和機構合作提供在線課程。

© 2019 Coursera Inc. 保留所有權利。

通過 App Store 下載

通過 Google Play 獲取