say ‘these are the people in my network who are maybe the
super-influencers or have similar interests to me, I need to catch
up with them’.
“Or it’s telling people how they should optimise their productivity,
showing them the difference between their average day and the
average day of someone who’s hyper-productive,” he adds,
explaining that such capabilities could be incredibly powerful in
future in democratising and decentralising HR. “There’s no reason
things like performance have to be so top down,” says Modi.
The danger though, warns Cook, is that such recommendations
will only ever be as good as the data behind them. “You could have a
system that says ‘you’ve not talked to Jane in two weeks, you should
catch up’. That’s a great example of where it could be helpful, or
extremely annoying – because I sit next to Jane so I interact with her
in person all the time.”
Quality issues aside, the quantity of data needed also poses a
challenge. “The issue is that no organisation will have sufficient
data to refine those recommendations to a point where a human
would say ‘that is adding value’. Even thousands of people won’t be
enough. Even with hundreds of millions of people – as Netflix has –
you get some wonky recommendations,” says McNulty.
Which perhaps spells
out the case for
prescriptive analytics to
be fuelled not just by
data collected by one
employer, but numerous
external datasets.
“There are so many
datasets becoming
more available that you
can now draw on,”
says Ed Houghton,
head of research and
thought leadership at
the CIPD.
“For example, in
workforce planning:
you’re a London-based
business but you know
from your data that
many of your employees
are based in Brighton,
and that the train line
will be disrupted. So
you can use macroeconomic
datasets to
Data definitions: Analytics glossary
If HR is doing analytics full stop, that’s positive.
I don’t think the categories matter too much
plan around whether you plant someone in a certain location or put
people up in hotels.”
“The art of the possible is huge,” agrees Britnell. “For example,
we’ve seen vendors recently that look at publicly available data on
Facebook and LinkedIn so you know as a recruiter the best time to
pick up the phone.”
But the issue for McNulty is that “once the data has moved into
centralised, aggregated portals, it becomes much lower quality”.
“For example, I get endorsements on LinkedIn from people who
have never met me and never worked with me,” he says, adding that
legislation such as the General Data Protection Regulation (GDPR)
Descriptive analytics – The use of historical data to
describe things today. Many organisations already do
this for things like absence rates, skills level, and pay and
remuneration – even if they refer to data generated by
HR systems simply as ‘reporting’.
Predictive analytics – The use of statistical techniques
to understand current or historical facts to then use them
to make future predictions. Some regular business ows
can be very predictable, such as absence and the time
it takes to hire people. Others are less predictable, such
as when someone will leave an organisation. It’s in
getting to grips with these that HR can deliver signi cant
strategic advantage, many feel.
Prescriptive analytics – Examining the outcomes of
computerised modelling exercises for predictions using
different variables – including data from outside the HR
function – to recommend the best course of action.
has put a serious
dampener on the
amount of personal data
freely shared.
The other barrier is
HR skillsets, says Mark
Judd, VP of HCM
product strategy, EMEA
at Workday and former
head of HR operations
for shared services at
Rolls-Royce. “We know
that even among those
HR people familiar with
this world, still only 1%
actively use data
engineering,” he says.
“Most often they’re
pulling it into an Excel
spreadsheet then
presenting it to their
exec team.”
Which brings us back
to how important it is
for HR to get stuck into decision-making based on data – whether
spurred on by a shiny new term or not.
“If HR is doing analytics full stop, that’s a positive move in the
right direction,” says Cutler. “I don’t think the categories matter
too much.
“What matters is: are HR people numerate? Are they fluent in the
language of the business? Are they able to leverage tech to interpret
multiple and diverse datasets, combine that with understanding of
the business and their people, and then make strong, well-backed-up
recommendations?
“If you’re doing that, you can call it whatever you like.” HR
HR Technology Supplement Prescriptive analytics
12 HR October 2019 hrmagazine.co.uk
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