This is the outline for the day.
We're going to talk chiefly about challenges involved with talent analytics.
There's some well-known traps that people fall into.
Despite the fact that data is helpful,
data can actually exacerbate some of these traps.
Before we leave we're going to take a special topic,
one of unique interest in the last year or two around tests and algorithms.
And then finally we're going talk about some prescription,
some practical tips for you in doing talent analytics better.
So, first up challenges, data's good.
A lot of data typically better, but data can also be misleading.
If you're doing talent analytics, you're probably working with data.
You're crunching numbers.
You're crunching performance evaluation, test scores, 360 feedback, sales figures,
employee morale, whatever you can get your hands on.
If you're resourceful, you're probably crunching those numbers.
But what do they mean?
Before you can draw any strong inferences, you've got to navigate a few challenges.
It's critical, in fact, it's critical if you're going to do talent analytics
well that you navigate these challenges.
Four chiefly that we're interested in and that we're going to focus on today.
Context, interdependence, self-fulfilling prophecies,
and reverse causality, each of these will be a separate segment.
And we wanna start with context.
So, it is well understood in psychology, and increasingly understood
in organizations, that people neglect context when evaluating performance.
We tend to believe when we see someone perform that that performance is due to
some unique individual skill or unique individual personality trait.
And we underestimate, we under-attribute the situation the person was in.
So what are situational factors?
They might have had an easy task or a difficult task.
They might have had a helpful team or a harmful team.
They might be working in a good economy or a bad economy.
These are very influential factors.
These are situational factors that don't have anything to
do with whether they're a nice guy or a bad guy, a hard worker or slacker.
These are situational factors that we tend to underestimate
when we're trying to infer whether this person is good at their job.
So this is so well known it's considered the fundamental attribution error.
In psychology it's been this focus of study for more than 40 years now.
And we know that people are inclined to blame personality traits,
individual traits as opposed to situational traits.
In this segment, what we're trying to get you to do is go back and
consider situational trades, consider the context.
When you're crunching the numbers,
you have to figure out ways to make sure you're considering the context.
So, there's a saying in Wall Street that is designed to help offset this bias.
The saying is, don't confuse brains in a bull market.
What they mean by that is, in a bull market, everybody's making money.
Everybody's trades seem to work out.
And people very readily infer then that they are good or
that their portfolio manager, their investment manager is good.
And these guys are saying, hey, hold on.
That was an easy situation basically.
The context was everyone made money.
So they've boiled that down to a very pithy comment, don't confuse brains and
a bull market.
We want to build those kind of heuristics,
those helpful corrective heuristics in the way you evaluate your talent.
So an example, a couple of examples.