Stacia Garr: We looked at how the market has changed, really
since the 1970s, in terms of the number of vendors who have
come into the space, and we at the moment, how about 86
different people analytics vendors on our radar. And we saw
a growth of about 146% in just the last 10 years. So it's
been a pretty substantial growth in terms of numbers of
vendors. We also asked them about, we asked the vendors in
our study about how quickly they were growing. And we asked
them both about their customers as well as their revenue
growth rates. So their customer growth rates across from
2017 to 2018, where it was about 80%. But, which is
remarkable, but what was even more remarkable was actually
the growth in the revenue numbers, which was about 420%. So
we're seeing growth In the number of customers, but even
much more substantial growth in the amount of revenue.
Yeah, so we're seeing a lot of organizations building people
analytics teams. And a big part of this is that there was a
period of, I would say, experimentation, where organizations
were trying to figure out what the value was of these teams
and and how they could have a positive impact on business
outcomes. And in many organizations that experimentation has
resulted in decision to to go ahead with it. So we've seen
yes, some pretty substantial growth in the number of people
who are coming into into the field.
It's a mix of skills, many of them are, I mean, they all
have some sort of data background, but it doesn't
necessarily have to be kind of in in the ways that you might
expect so. So they're not, for instance, all you know,
psychologists, PhD types. But they are data scientists, many
of them you know, bringing machine learning and other types
of capabilities into this. Many of them will have just a
strong kind of statistics background, but lack the HR
expertise. And then there are some who are coming in from
something like compensation, which is a very heavy data
focus, but also an understanding of the people. So really a
range of backgrounds, but certainly a real strong anchor in,
in data analysis and insight.
I think there's more clarity on how to do it. But it varies
based on the maturity of the organization, meaning that some
of them are still just trying to kind of get their, their
feet wet to get some strong numbers that they can look at
things like attrition numbers, or you know, a predictive
turnover model or something like that. And, and then there's
some who are doing some really truly sophisticated things
that are, you know, with a sophisticated people analytics
team. I think there's a greater clarity in terms of how you
might approach if you're kind of on the on the less mature
side versus the more mature And some of the myths that we
had at the beginning have been erased in many ways. So there
used to be this perception that you end up all your data,
right? And you had to be able to do reporting before you
could do predictive analysis. We actually know that's not
true. You can do those things, you know, yes, you have to
get your data mostly right. But, you know, you can do a lot
of that stuff at the same time. So there's is more of a
settling in where I think there are some potential
difficulties is that there are really two audiences for
people analytics, there are people analytics practitioners,
who are very sophisticated in their capabilities, and then
there are HR business partners and leaders who just didn't
tend to not have the same level of sophistication. So some
of the products that are out on the market, it's not clear
who exactly they're designed for and how they expect them to
be used. So while there's some understanding of kind of the
processes that we should be doing, I think there's still
some mix up in need for further clarification on the
products that they're using to support them and doing it.
There are a lot of different types who claim the people
analytics moniker. So that can range from an organization
that is doing kind of analysis on a single or single primary
data source. And so that might be something like your HRS
provider who has an analytics package that's associated with
their their technology, all the way to a type of provider
that's doing complex iterative analysis from multiple
different sources. And so that, but they all fit in the
people analytics bucket. And so the way we're thinking about
it is in terms of kind of who owns the data and do the, is
IT a data owner or a data consumer, and what's the type of
analysis? Is it occasional analysis or is it complex and
iterative analysis? Because that can lead to different types
of certainly use with the technologies, but also different
insights. So that's we're thinking about those on really a
two by two and then categorizing vendors according to that.
We didn't, mostly because everybody claims they have some
AI. And we think that right now it's a lot of noise. It's
unclear who is got, who has real AI if you will, versus who
is just some kind of advanced models. That I think is kind
of its own study that needs to be done, but not we didn't do
it in the context of this research. I think it needs to be a
But in terms of some success stories, I'd be thinking about,
model that's, that's truly learning. You know, I mean, there
there's a lot that have to do, which is, you know,
are, I think, what is it, 10 different accepted models of
addressing attrition in that type of thing. But where we
tend to see better, much better improvements is less about
AI. And there's two that tend to apply most specifically to
kind of turnover because that's a little bit of an older
area to focus on, but much more, instead we're thinking
HR. And so, you know, some AI that kind of fits within those
about how do we increase workforce productivity. So we see
for instance, we have an example of an organization that use
two models is is you know, a starting point. But then moving
organizational network analysis in conjunction with
leadership development program to understand how people were
on to just simple things, like how is it actually learning,
in engaging with each other, and then using the leadership
development program to help those leaders, change their
was it what are the data sets that is trained on, kind of
behaviors, monitor them through the organizational network
analysis over time. And then at the end of it be able to say
having that level of understanding of what is underneath and
"actually, because these leaders have changed their
behaviors, they want have strong relationships with their to
their team second have been much more productive." And then
with, within really the I think is important. So it's
we would expect that to replicate. So that's kind of a
complicated. And I would say for practitioners, if a vendor
different way of thinking about, we always think about
turnover and some of that stuff.
can't explain to you exactly what they do, and too many of
But there's actually a whole amount of additional ways, a
them kick you over to the real data science people and you
never get the answer that's probably assigned to you, that
whole number of additional ways we can use the analytics to
it's not really AI. We're in the, in the report when we
finally because we have a kind of, we're still working on
power productivity and culture change and other things in
it. Actually, this event was good, because we were able to
get some feedback based on it.
organizations. We see really kind of two different models.
So one is where it is a sort of Center of Excellence within
HR reporting into HR, you know, sometimes into HR operations
and sometimes directly into the chro in. Then the other
model that we're seeing it, we're starting to see it, I
wouldn't say it's widely popular yet, but that is putting
all of the business analytics into a single function. And
having people analytics is one component of that. The thing
that's nice about that approach is in the analytics team can
learn from others in the organization about different types
of projects or approaches that they've used in the past, and
they can kind of get more sophisticated more quickly. The
downside obviously, is is that they could potentially get
more removed from what is most necessary according to HR or
or to the business. So obviously benefits and drawbacks to
both approaches, but those are the two models we tend to see.