Date post: | 14-Sep-2014 |
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Recruiting & HR |
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A key trend in 2014: talent.datafication
and the rise of the underdog
@Nicole_Dessain
HR.com, June 19, 2014
Key trends will shape the way we think about
talent in 2014
We used a unique method to identify ten talent trends that will shape 2014.
Download the FREE talent.trends 2014 report at http://talentimperative.com/resources/talent-trends-2014/
1. A key imperative: solving the skills mismatch riddle
2. Progress at Last? Women in top leadership roles
3. Employment remix: Talent-as-a-Service
4. talent.datafication and the rise of the underdog
5. The growth market conundrum
6. talent.experience is king
7. From innovation to talent.preneurship
8. talent.driven leadership is the new black
9. The contemporary CEO – emperor with new clothes no
more
10. Talent as a board level imperative
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What does big data in HR really mean?
Big data is all over the news…
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… and here to stay!
Board members say that “attracting and
retaining top talent” is considered one of
the most important levers for achieving
strategic objectives. (Harvard)
Employee turnover rates are forecasted
to rise with 160 million workers
preparing to leave their jobs in 2014.
(Hay Group)
Head of HR Analytics is one of the
top 10 executive jobs in 2014.
(Fortune)
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Scared yet?
Image credit: LinkedIn postImage credit: LinkedIn post
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Wanted: definition, training, support, and jobs
“Big data is high-volume, -velocity and –variety information
assets that demand cost-effective, innovative forms of
information processing for enhanced insight and decision
making.” (Gartner)
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The evolution of people analytics
talent.datafication is the ability to quantify talent-driven
organizational value creation and fundamentally change the way
companies view talent and predict business outcomes.
HR/Workforce
Analytics
“Employee data
for HR – the
what”
Examples:• Headcount
• Attrition
Talent Analytics
“Talent data for the
business – the
why”
Examples: • Predictors of top
performance
• Drivers of high
performer attrition
talent.datafication
“Talent value
quantification for all
stakeholders”
Examples:
• Personalized
performance tracking –
real time
• TX (talent.experience) =
CX (customer experience)
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Why are we making things so scary?
Myth #1: “I don’t work in talent analytics so why
should I care?”
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Image credit: Discover Magazine
Myth #2: “Big data means analysis paralysis
and more metrics we have to track.”
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Myth #3: “Big data will replace other
decision-making factors.”
“Dig up all the information you can, then go with your instincts. We all
have a certain intuition, and the older we get, the more we trust it. … I
use my intellect to inform my instinct. Then I use my instinct to test
all this data.” (Collin Powell, former U.S. Secretary of State)
Image credit: Junge Karriere
Photographer: Samantha Jones
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Myth #4: “Big data opens the door for increases
in discrimination and privacy infringement.”
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Myth #5: “Everybody welcomes talent analytics
with open arms.”
“An anthropologist might conclude that we are only capable of quantitative talent analysis while drinking beer on our couches. Ultimately, most leaders seem uncomfortable converting subjective judgments into quantitative evaluations.” (Tom Monahan, Chairman and CEO at CEB)
Image credit: Yahoo! Movies
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What Would Data Do (aka WWDD)?
Must Do #1: Design a roadmap based on your
level of talent analytics maturity.
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Must Do #2: Build analytics coalitions,
governance, and capability.
Talent Analytics
Framework
Capability
Governance
Coalition
Guiding Principles
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Must Do #3: Instill a data-guided, self-reflective
mindset.
“A mountain of scholarly literature has shown that the intuitive
way we now judge professional potential is rife with snap
judgments and hidden biases, rooted in our upbringing or in deep
neurological connections that doubtless served us well on the savanna
but would seem to have less bearing on the world of work.” (Don Peck,
The Atlantic: “They Are Watching You at Work”)
Image credit: FastCompany (Photographer: Andrew Whyte)Image credit: Workforce Magazine
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The underdog advantage
“Giants are not what we think they are. The same qualities that appear to give them strength are often the sources of great weakness. And the fact of being an underdog can change people in ways that we often fail to appreciate: it can open doors and create opportunities and educate and enlighten and make possible what might otherwise have seemed unthinkable.” (Malcolm Gladwell)
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Must Do #4: Empower leaders and employees
with analytics tools and education.
Leaders
Craft “crunchy” questions
Prioritize
Develop awareness of
“unconscious bias”
Co-design and educate on
guiding principals
Accelerate reporting
efforts with real-time data
insights via intuitive
dashboards
Keep talent topics top of
mind
1
1 Term coined by Deloitte
Employees
Think of your employees
as talent.preneurs
Empower talent with data
to drive better job fit and
performance
Leverage data to assist
employees in identifying
skill gaps and to access
resources
Make it easy, safe, and
fun to share data (social;
gamification)
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Must Do #5: Create data-informed talent
success and experience profiles.
“Crunchy” Questions:
• What are our key talent
segments, who are the high
performers in each segment,
and what makes them
successful?
• What are our key talent
segments, what is the
demographic make up of
each segment, and what do
they value in an employer
across the talent.experience
lifecycle?
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Case in point: Google
1. Treat your employees’
data with respect
2. Use data to determine
successful attributes –
in individuals and teams
3. Determine which methods
are most predictive in
assessing success
4. Empower managers with
data to enable behavior
change
5. Don’t loose the human
insight
“One of the applications of Big
Data is giving people the facts,
and getting them to understand
that their own decision-making is
not perfect. And that in itself
causes them to change their
behavior.” (Laszlo Bock, The New
York Times: “In Head-Hunting, Big
Data May Not Be Such A Big
Deal”)
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But not every company is like Google…
Predict job
success
Enterprise Solutions Company – launched new
online evaluation with algorithm analyzing answers
along with factual information. Result: attrition
reduced by 20%. Finding: previous experience not
critical success but commuting distance driver of
retention.
Retention profilingHigh Tech Company – developed statistical profiles
for “retention risks” and conducted custom
interventions (mentors, compensation adjustment,
etc.). Result: Reduction in attrition rates by 50%.
Coaching insightsProfessional Services Company – created a real-
time dashboard for leaders with key retention and
engagement drivers; color coded for “red flags” so
leaders can take coaching or other actions.
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So, how do I get started?
Define key stakeholders and prioritize “crunchy” questions.
Determine your talent analytics maturity level.
Create a roadmap and change management plan.
Define needs for capability, coalition, technology, and governance.
Start with a “quick win” or pilot.
Don’t get discouraged and don’t be afraid to ask for help.
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