The Datafication of HR: Graduating from Metrics to Analytics

vViissiieerr
l
l
THE
“DATAFICATION”
OF
HR:
GRADUATING
FROM
METRICS
TO
ANALYTICS
Ian
J.
Cook
Director,
Product
Management,
Visier
a ananlayl&ycti
cap applipcalic&aotnios
nfosr
fpoer oppeleo
ple Page 1

Workforce Analytics and Planning.
Smart. Intuitive. Complete.
visier l analytic applications for people Page 2

TODAY’S
AGENDA
§ Trends
Shaping
the
“Datafica&on”
of
HR
§ How
to
Graduate
from
Metrics
to
Analy&cs:
– Talent
Reten&on
– Recrui&ng
Effec&veness
– Performance
– Total
Rewards
– Employee
Movement
§ Common
PiMalls
to
Avoid

TRENDS
SHAPING
THE
“DATAFICATION”
OF
HR
vViissiieerr
l
l
a ananlayl&ycti
nfosr
fpoer oppeleo
ple Page 4

ECONOMIC
DRIVERS
Hire
Right
Demographic
ShiD
Retain
Top
Talent
Skills
Shortages
Ensure
Diversity
Economic
Flux
Op&mize
Spending
CompeKKve
Pressures
more
than
ever
before
workforce
insight
and
planning
agility
are
crucial
to
business
performance

FACTORS
DRIVING
CHANGE:
HEIGHTENED
COMPETITION
“…
stock
market
returns
are
30%
higher
than
the
S&P
500,
they
are
twice
as
likely
to
be
delivering
high
impact
recrui&ng
solu&ons,
and
their
leadership
pipelines
are
2.5X
healthier.”
Josh
Bersin,
October
2013
“…
improve
talent
outcomes
by
12%,
leading
to
a
6%
improvement
in
gross
profit
translated
into
$18.9M
in
savings
for
every
$1B
in
revenue.
“…have
a
margin,
which
CEB,
Analy&cs
Survey,
2013
hard-‐to-‐replicate
compeKKve
advantage.”
Harvard
Business
Review
Compe&ng
on
Talent
Analy&cs,
October
2013

FACTORS
DRIVING
CHANGE:
ECONOMIC
INFLUENCE
OF
HR
SUCCESS
“Compared
with
low
performing
companies,
high
performing
companies..
1. Build
stronger
people
leaders
2. Do
more
to
a]ract
and
retain
talented
people
3. Treat
and
track
performance
with
transparency”
Source:
BCG,
From
capability
to
profitability,
2012

“THE
WAR
FOR
DATA
IS
ON”
JOSH
BERSIN,
BERSIN
BY
DELOITTE
(OCTOBER
2013)
Level
4:
Predic&ve
Analy&cs
Predic&ve
models,
scenario
planning
Level
3:
Strategic
Analy&cs
Segmenta&on,
analysis,
people
models
4%
Level
2:
Proac&ve
–
Advanced
Repor&ng
Rou&ne,
benchmarking,
dashboards
Level
1:
Reac&ve
–
Opera&onal
Repor&ng
Ad
hoc,
reac&onary
Source:
Bersin
by
Deloi]e
2013
If you are not
investing in an
integrated analytics
capability within HR
and creating a Big
Data solution …
you’re going to fall
behind.
56%
10%
30%

BIG
DATA
GOES
MAINSTREAM
§ Big
Data
has
one
or
more
of:
– Volume:
large,
or
rapidly
increasing,
amounts
of
data
– Velocity:
rapid
response
or
movement
of
data
in
and
out
– Variety:
large
differences
in
types
or
sources
of
data
§ Big
Data
lets
you
ask
and
answer
ques&ons
that
historically
were
impossible,
or
prohibi&vely
expensive
–
thanks
for
hardware
and
sodware
technology
innova&ons

IN-‐MEMORY
“BIG
DATA
READY”
TECHNOLOGY
The
“brain”
CPU
Like:
Short-‐term
memory
Long-‐term
memory
Can:
Do
1
billion
things
a
second
Fetch
25
million
pieces
of
data
a
second
Fetch
100
pieces
of
data
a
second
250,000
Kmes
faster
It
takes:
1
second
2.9
days
1
minute
25
weeks

DEFINITIONS
vViissiieerr
l
l
a ananlayl&ycti
nfosr
fpoer oppeleo
ple Page 11

DEFINITIONS
Metrics
§ A
system
or
standard
of
measurement
AnalyKcs
§ The
systema&c
computa&onal
analysis
of
data
or
sta&s&cs

HOW
TO
GRADUATE
FROM
METRICS
TO
ANALYTICS
vViissiieerr
l
l
a ananlayl&ycti
nfosr
fpoer oppeleo
ple Page 13

RETENTION
≠
TURNOVER
§ Turnover
is
not
sufficient
because….
§ Lots
of
reasons
people
turnover
–
some
good
/
some
bad
§ Once
someone
has
led
it
is
hard
to
get
them
back
§ One
number
tells
you
nothing
about
how
to
change
the
outcome

RETENTION
ANALYTICS
Modern
algorithms
deliver
a
far
more
sophis&cated
analysis
of
exits
and
provide
insight
into
how
to
reduce
them.

EFFECTIVE
HIRING
≠
TIME
TO
HIRE
FAST
GOOD
CHEAP
§ Speed
is
highly
dependent
on
the
market
condi&ons
affec&ng
the
type
of
talent
being
hired
§ Priori&zing
speed
over
quality
can
have
nega&ve
results
§ EffecKveness
is
not
a
single
concept
§ For
example,
hourly
paid
staff
vs.
execu&ve
level
hires

RECRUITING
ANALYTICS
Analy&cs
applies
powerful
visualiza&on
techniques
to
put
cri&cal
business
answers
in
front
of
decision
makers
–
in
an
intui&ve
way.

PERFORMANCE
≠
APPRAISAL
PARTICIPATION
§ The
change
in
focus
for
performance
is
the
essence
of
the
shid
in
HR
from
transac&onal
to
strategic
§ It
is
more
important
to
analyze
the
impact,
quality
and
fairness
of
your
performance
process…
than
to
count
the
number
of
people
who
took
part!

PERFORMANCE
ANALYTICS

TOTAL
REWARDS
ANALYZED
Analy&cs
are
designed
to
provide
answers
to
important
business
ques&ons
like:-‐
“What
caused
our
compensa&on
budget
to
change
in
Q1?”
By
providing
these
types
of
answers
the
business
can
make
be]er
decisions
–
leading
to
be]er
results.

HEADCOUNT
REPORTING
Business
Unit
Q1
2013
Q2
2013
Q3
2013
Q4
2013
Q1
2014
Sales
554
549
557
560
550
Manufacturing
1320
1314
1328
1345
1355
Services
432
430
424
420
425
R&D
45
40
44
48
40
Finance
15
15
14
15
14
HR
17
15
16
18
16
Total
2383
2363
2383
2406
2398
Forecast
2440
2420
2390
2398
2409
Difference
-‐57
-‐57
-‐7
8
-‐11
This
is
an
example
of
the
typical
headcount
report.
It
is
extremely
limited
in
its
ability
to
support
decisions
and
can
hide
important
detail.

HEADCOUNT
ANALYZED
Analy&cs
shows
you
the
whole
story
related
to
the
change
in
headcount.
There
are
a
total
of
546
moves
that
make
up
a
net
change
of
3.

COMMON
PITFALLS
TO
AVOID
vViissiieerr
l
l
a ananlayl&ycti
nfosr
fpoer oppeleo
ple Page 23

MY
DATA
IS
BAD,
I
NEED
TO
CLEAN
IT
FIRST….
§ You
are
not
alone
§ HR
data
is
inherently
“bad”
and
difficult
to
integrate
§ But
you
do
not
need
to
let
this
hold
you
up
with
analy&cs
“Our
workforce
data
is
bad,
inconsistent,
incomplete,
constantly
changing….”

DO
NOT
LET
BAD
DATA
HOLD
YOU
UP
§ Analy&cs
is
about
making
decisions,
but
not
all
decisions
are
equal
Inefficient
Risky
decision
Impact
of
decision
Quality
of
data
Aim
for
the
green
zone!

DO
NOT
LET
BAD
DATA
HOLD
YOU
UP
§ People
enter
data,
therefore,
Bad
Data
is
a
given
§ Aim
for
con&nuous
improvement
§ Create
auto-‐rules
that
correct
common
mistakes
Battery Park
N.Y.
Chelsea
Midtown
NYC
Manhattan
New York City
Bronx
NY
Big Apple
N. York
Harlem
Queens
= New York

MY
IT
DEPARTMENT
IS
TOO
BUSY
§ IT
oden
lacks
the
resources
to
support
HR
beyond
transac&onal
systems
§ Tradi&onal
Business
Intelligence
/
analy&cs
solu&ons
take
a
year+
and
$1
Million+
to
implement,
and
more
to
maintain
§ Look
for
cloud
solu&ons,
provided
as
a
service,
which
remove
the
burden
and
cost
from
IT

WE
NEED
TO
CREATE
A
DATA
WAREHOUSE
§ More
than
50%
of
data
warehouse
projects
have
limited
acceptance
or
fail
(Gartner)
§ Between
70%
to
80%
of
corporate
business
intelligence
projects
fail
(Gartner)
§ The
average
price
for
a
data
warehouse
is
$2.3M
(IDC)
§ The
&me
to
implement
a
data
warehouse
ranges
from
12-‐36
months

INSTEAD
OF
TRADITIONAL
DATA
WAREHOUSE…
§ Look
at
cloud
solu&ons
that:
– Use
modern
technologies
–
in-‐memory
data
warehouse
– Have
dedicated
expert
resources
who
have
implemented
many
&mes
before
– Have
a
well-‐defined
but
flexible
data
model
• Pre-‐built
=
speed,
low
risk
• Flexible
=
adjust
to
your
business
needs.
Change
as
your
business
changes
(new
ques&ons,
new
sources
of
data)

The Datafication of HR: Graduating from Metrics to Analytics

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Destacado

Destacado (14)

Similar a The Datafication of HR: Graduating from Metrics to Analytics

Similar a The Datafication of HR: Graduating from Metrics to Analytics (20)

Último

Último (6)

The Datafication of HR: Graduating from Metrics to Analytics