Lydia Wu: I started with Panasonic back in February of 2018.
And what I was asked to do was actually quite vague, they
essentially wanted to start a talent analytics function
because at the time in the market, everyone seemed to be
building up the analytics practice. And it seemed that
Panasonic at the time also wanted to jump in and really get
more insights from the data that they were collecting
through their core HCM system. And talent analytics was one
of those things that essentially when you put the two words
separate in English, talent made sense, analytics makes
sense. But when I first joined, it was sort of a mystified
elusive concept, if you will, of what the heck is she
actually doing here? And how is she going to contribute to
the organization?
So some of the biggest hurdles we face was actually getting
stakeholder buy-in, and not just buy-in, but an initial
comfort around numbers, around the concept of "analytics."
Because if you think about HR as a field, in a way people
sort of self select into being HR. And when you think about
HR in general, it's about the people. It's about
relationships. It's about helping people soothe certain
aspects of their professional careers. None of that really
has anything to do with numbers. If you're a numbers person,
you typically ended up in finance from a stereotypical
career selection perspective. So when I first went to in,
working with a lot of our HR business partners, working with
our, really our HR teams across the board, it's getting them
comfortable with numbers, helping them understand that
numbers are not scary. They actually help paint the picture
and help them better sell the stories at the end of the day.
At a very high level, similar to what I mentioned in this
session, is just pulling the demographics data. It's helping
our business leaders fundamentally understand who actually
works in the organization? Who these people are, how much
are we paying them? Are they engaged or not? And how are
they doing on a day to day basis? And from there, we
essentially started layering some of our financial data. It
was apparently the first time that we've looked at two
things together, to say, yes, people actually do drive your
financial results, we are looking at productivity, not just
from a certain sales pipeline, but across the company as a
whole.
When you think about the concept of analytics, I think
there's still a little bit of misperception in the market
where analytics is this elusive thing that only the big tech
companies or companies with a lot of budget can afford to
do. But reality is we use data on a daily basis in decision
making in HR, whether it's to promote somebody, whether it's
to help somebody fund their tuition, anything along those
lines, we look at numbers. And fundamentally speaking, at
the end of the day, not everybody has the budget to buy the
tools. So instead of making the hard sell of, "hey, I need
$100,000 to get started before I even produce any results,"
the way I look at it is essentially use Excel, use
PowerPoint, proof the concept, prove the fact that it works,
prove the fact that data exists in their day to day basis of
what we do, and then go from there.
We've been exploring a few tools here and there. Power BI is
our current favorite just because we are a Microsoft Office
shop. And Power BI essentially came with our suite. So it's
an easy integration for us. It produces slightly better
visualization where I don't need to cut and paste, pie
charts bar graph into a PowerPoint presentation. We're also
talking to a few vendors in terms of how can we rapidly
scale analytics without having to throw bodies at the
practice, because I think one of the other challenges in the
market is that when people think of analytics, they think of
the scaling challenge, they think of the fact that in order
to do it well you need a team of ten, twenty headcounts. But
if you look at the technology out there, especially the ones
that are exhibiting on the expo floor today, that's really
not the case.
My philosophy is that if anybody has found the magic bullet
to talent or to HR analytics, we would have known this by
now because it would have been a multi-million of not
billion dollar solution at this point. Reality is everyone
has done stumbling around a little bit and learning as they
speak. So going into something like talent analytics with
the expectation that your path to success is going to be
linear, tends to set you up for failure. Whereas if you go
in for more of an exploratory perspective, in terms of let's
test the hypothesis, if it works, it works, if not, let's
pivot, that tends to lead organizations to solutions that
actually makes sense for them and their audiences.
My group has four head counts in there, but more
importantly, we are actually having conversations where our
business units are coming to us to say, "Hey, I need to do
this. Can you guys help me? Is there data that you can find
for me, I need to have this conversation with our senior
management team. Can you help me build a case for as
supported with our internal data," or even something along
the lines of like, "Hey, we think there might be an issue
with this unit. Let's look into it from an engagement data
perspective. Let's actually do a deep dive into it." That's
really the challenge. Or if we need to look at the unit
differently.
We are not quite seeing results in turnover yet. But what we
are seeing is a faster decision cycle. We are seeing our
leadership saying yes to ideas to business cases quicker
because no longer are we going to table with "Oh, I feel
like this or my experience says that." We are going into it
with numbers. We're looking at it from a turnover
perspective. We're looking at it from potential retention
perspective, potential financial savings perspective, and
all that to a business leader is incredibly valuable.
If you look in the market, I'm sure there's a lot of
research in terms of skill sets, that really is critical to
a talent analytics team. My secret sauce is, I look for the
hustle factor and the get-it quotient because when you're
starting up a team, they just need to be scrappy. They need
to get the business solution, digging to get the business
challenges, and figure out how to make do with what we have
today and figure out that solution. So I've always told my
team, "You can't say the word can't." In any of our
meetings, that word does not exist. If you don't think it's
possible, let's look for an alternative. Let's figure out
how we can make this work. If it's not a one step solution,
let's at least figure out how to get to step 0.2 so we're
moving somewhere.