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.
Measuring Outcomes
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來自 University of Pennsylvania 的課程
人力资源分析
個評分
從本節課中
Collaboration
In this module, you'll learn the basic principles behind using people analytics to improve collaboration between employees inside an organization so they can work together more successfully. You'll explore how data is used to describe, map, and evaluate collaboration networks, as well as how to intervene in collaboration networks to improve collaboration using examples from real-world companies. By the end of this module, you'll know how to deploy the tools and techniques of organizational network analysis to understand and improve collaboration patterns inside your organization to make your organization, and the people working within in it, more productive, effective, and successful.
與講師見面
Cade MasseyPractice Professor
The Wharton School
Martine HaasAssociate Professor of Management
The Wharton School
Matthew BidwellAssociate Professor of Management
The Wharton School
So I really wanna just build on that last piece a little bit more with
something that's a bit of an aside.
But it's only an aside because it's so
important that it doesn't just apply to this particular segment,
it applies throughout the people analytics piece of your course, right.
And that is about measuring outcomes because in any one of the modules
that you've been listening to and learning from in terms of people analytics,
you will have heard a lot about different kinds of outcomes that we care about.
Now we were just talking about outcomes, which is why I'm gonna connect it here,
but again, it applies across the modules.
So the main message here can be summarized in a nutshell,
which is basically garbage in, garbage out.
So the idea here is that if you don't have good measures,
you can plug whatever you like into your statistical program, and
it can generate lots and lots of lovely results.
But your results will mean nothing if those original measures were bad.
Right?
So if you put garbage in, in the form of poor measures,
whatever results you get are really gonna be meaningless and
may actually be harmful if you try to act on them.
So it becomes very, very important in any form of analytics and
particularly people analytics what we are talking about today,
be able to measure your variables really well.
And we're focusing here on outcome variables, although I've already talked
about those network variables that we might care about and
how to measure them really well.
But let's think about outcome variables here because that's gonna apply again
across the field of people analytics.
So if we look at how do collaboration patterns matter for important outcomes?
That question we were just talking about and
we have well our five building blocks in terms of networks, we discussed a lot in
segment two how to actually map those building blocks effectively.
Actually we talked a lot in segment three on how to actually map those building
blocks effectively.
Now we're gonna focus on how to connect those to individual outcomes and as we
were pretty just talking about we're going to focus particularly on performance.
So we've already discussed a little bit, we need to map network attributes,
network features to performance but what I just want to emphasize is
the importance of trying to measure performance well and what that takes.
So let's think about measuring performance.
The question is, what is a strong measure of performance?
I have just picked performance, but this could be many other things.
It could be satisfaction.
It could be commitment.
It could be intention turnover.
What's a strong measure of anything?
Here we're gonna use performance.
There are several key criteria whenever you're trying to
measure anything in order to get away from the garbage in, garbage out scenario and
have really good measures that can give you really good results.
The first is you have to get a measure that's at the right level of analysis,
so if I'm trying to measure individual performance,
I have to make sure that what I'm measuring in terms of performance
is actually an individual performance measure.
It's not something that was generated by the whole unit or the whole group and
the individual didn't really have that much control over it.
It needs to be at the right level of analysis if I'm
trying to measure individual performance.
The second and third criteria are reliability and validity.
And if you've taken any measurement type courses or
been involved in this field at all, you'll have heard of reliability and validity.
Reliability means are your assessments, are your measures, consistent?
And that might mean over time and it might mean across raters or
across people that are taking the measures.
The third criteria here is validity, which means are the assessments accurate?
Are your measures accurate?
Are they measuring what they're actually supposed to measure?
So if you're measuring performance in terms of reliability,
you need to say well if I measure this person's performance in the morning,
and I happen to look at how well they're performing in the morning, am I gonna get
the same result if I measure their performance in the late afternoon?
Because if not, it's not a very reliable measure of their performance, right?
Maybe it's their typing speed, right?
So if their typing speed in the morning is different from their typing
speed in the afternoon, it's not a very reliable measure.
So we need measures that are consistent over time and across raters.
In terms of performance, if you're looking at validity,
is it actually capturing their performance?
So if you're measuring typing speed, is that an aspect
of the performance that's actually important and relevant to the task?
Or are you actually trying to measure something about how good they are at
writing?
And typing speed is really not a good measure of that.
So, when we talk about reliability and validity,
we often use this kind of bullseye motif.
And it's helpful just for understanding the difference between them.
So something that's reliable again has to be consistent over time and across raters.
In other words you're hitting the same spot on the bullseye over and over again.
That makes it reliable, but that doesn't necessarily make it valid.
For something to be valid, you need to be hitting the bullseye, right, or
you need to be sort of equally distributed around the bullseye, so
there's no sort of bias in the way you're hitting the target.
So a measure that's valid but not reliable will be sort of hitting,
it's equally distributed around the bullseye, but
it's not very reliable, you're hitting different points all the time.
Neither reliable nor valid, you're kind of biased to one part of the target, and
both reliable and valid you're consistently hitting the bullseye.
This is just a little way that we sometimes use to help us
keep the difference between reliability and validity separate in our minds.
But going back to these criteria for measuring performance, so there
has to be the right level of analysis, has to be reliable, has to be valid.
The next three criteria, it has to be comparable.
So, if you're measuring performance across lots of people or across lots of units or
lots of teams say, you need a measure that's comparable for all those people.
You can't measure everybody on different criteria, right?
It needs to be comparable.
It needs to be comprehensive, in other words you need to have that measure for
everybody whose performance you're trying to measure.
And it needs to be cost-effective usually.
If it's very expensive to collect performance measures you could have
the best performance measure in the world that involves, you know,
really following people's performance over time, and monitoring it, and observing it,
and all sorts of great stuff.
And it can be a really great, reliable, valid measure of performance, but
it's just not cost-effective, so we also have to kind of come up with
measures that are relatively cost-effective.
And then finally, causality.
So in terms of causality, the measure of performance, if you want it to be
an outcome variable, you have to be able to make a plausible claim,
a plausible argument for why it is actually an outcome variable.
In other words, for why something else causes that outcome variable.
Now I'm not going to go into that in more detail here.
It's a very big and a very important topic.
If you want to claim that this kind of network configuration or
this kind of career path for
example affects your performance, you need to be able to make causal claims.
The topic of causal claims is gonna be covered much more depth
by Matthew Bidwell in his module where he'll talk much more about causality.
So that's all I'm gonna say about it here, other than that, it's a really big and
important issue.
Okay, so when we have these criteria for what's a strong measure of performance and
we're thinking about what is the sets of network attributes that predict
an individual outcome like performance,
again, lots of different performance measures we could be choosing between, but
we want to use these criteria to help us decide which are good.
Is sales per quarter a good measure of performance?
Well, you know sales per quarter may not be at the right level of analysis if it's
the whole unit or the whole team and
you're trying to measure individual outcomes.
If it's a whole team that produces sales, it's not so good.
Cost savings, same thing.
Maybe cost savings are a result of decisions are taken somewhere else in
the organization and not really a good measure of individual performance.
Self reported ratings.
We sometimes have to rely on, in surveys, people measuring their own performance.
We know there's a lot of problems with self reported ratings.
They don't tend to be very reliable or valid.
Manager reported ratings might be better, but
there can be problems with bias there too.
May not be very cost effective to collect that kind of data.
May not be very comparable across managers.
Measures like bonuses often capture something quite different in terms of
the bonus pool, for example, and don't really capture performance.
So again, there's a measure of validity there.
So, any measure of performance that you can get is gonna have some problems, but
understanding what those are and trying to apply these criteria for what's a strong
measure of performance or a strong measure of any outcome that you care about
is gonna be critical to avoiding that garbage in garbage out kind of trap.
Okay if the measure is poor in the first place, the results are gonna be very,
you can't trust them.
It's not worth it.
You gotta have good measures going in.
So the role of people analytics again,
people analytics is a data driven approach to managing people at work.
But, and again this applies across all the modules here, collecting and
analyzing high quality data is absolutely critical.
If you don't have good data, none of the rest of it's going to matter at all,
gotta have good data.
So we have now had a chance to think about how to evaluate collaboration networks in
terms of how can we compare collaboration attributes and how collaboration
features of the network across people and how can we map that and connect that
to outcomes that we might care about like their individual performance.
In our last segment,
we're gonna think about, how can we intervene to make changes in
collaboration networks based on the kind of data that we've collected and analyzed?
