Building
blocks
Getting the make-up of a people analytics team right is
key to it solving business issues. RACHEL SHARP reports
‘Data is the new oil.’ It’s one of
those phrases few recall when
and where they first heard it, but
which many now reel off confident
they’ll be greeted with knowing nods
of agreement.
In fact the quote can be traced back
to data scientist (and brains behind
the Tesco Clubcard) Clive Humby,
who coined it back in 2006 to explain
the inexorable value of data today.
Humby explained that – like oil –
“if unrefined, data cannot really be
used”. That is, for data to have any
value it must be broken down into
simpler forms and analysed.
In the HR world this data
refinement process is no less
important. It is commonly known as
‘people analytics’ and has led, over
the years, to the birth of the people
analytics team.
But while it has been around for
some time, progress in people
analytics has been slow. Deloitte’s
2019 Human Capital Trends report
found that, despite an intense interest
in better data management (with
71% of organisations citing people
analytics as a high priority in the
2017 report), just 26% of
organisations are effectively using
technology and analytics.
“If we got in a time machine and
went back 10 years, I was saying the
same things about people analytics
then. That’s because there’s still an
unrealised potential for it,” says Alec
Levenson, senior research scientist at
the Center for Effective Organization
at the Marshall School of Business,
University of Southern California.
“What typically happens is people
focus on the data in front of them as
opposed to figuring out the right
questions to ask. They say ‘there must
be something we can learn from
looking at this data’ and, yes, there is
always some kind of insight,” he says.
“But if you’re only looking at the data
and not embedding it in the larger
business context and asking bigger
questions about what problems
you’re trying to solve, then it can
lead you down dead ends and rabbit
holes and Alice in Wonderland kind
of adventures.”
Teams have focused too heavily on
reporting rather than tapping into
the possibilities of predictive or
prescriptive analytics (see our piece
on prescriptive analytics on p8), adds
Oliver Kasper, director of people
analytics and digital HR at Swarovski.
“People analytics can go in two
directions,” he explains. “One
direction is looking back, so
reporting what happened in the past.
Then there’s activities that look into
the future – that’s predictive and
prescriptive analytics. I’d say only
1-2% of the biggest companies are
doing the second one.
“And that’s like talking about the
difference between a steam train and
an electric car. Reporting is the steam
train and predictive and prescriptive
analytics is the electric car.”
So with data’s importance only
mounting, how can HR build a
people analytics team that will truly
deliver business results?
Remit
First, HR should take a step back and
determine what it actually wants out
of people analytics, says Aaron
Alburey, CEO of HR transformation
consultancy LACE Partners.
“It’s about understanding what’s
the purpose of the function and why
you’re setting it up in the first place,”
he says. “Until you understand what
you want to cover and how, you can’t
understand what you need.”
As far as Alexis Fink, VP of people
analytics and workforce strategy at
Facebook, is concerned, the purpose
of the function should be three-fold.
“I try to think of this along three axes.
First, along the x axis, there’s the
employee lifecycle. There’s great
opportunities to connect dots from
the candidate pool through selection,
onboarding, employee attitudes and
exit research. The y axis is the level of
analysis – individual, team,
organisation, enterprise, and even
HR Technology Supplement People analytics teams
14 HR October 2019 hrmagazine.co.uk
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