Data-Driven Talent Strategy: Bridging the Capability Gap in People Analytics

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DATA-DRIVEN TALENT STRATEGY:
BRIDGING THE
CAPABILITY GAP
IN PEOPLE ANALYTICS

The ability to analyse and interpret talent pool data has
initiated a new era in workforce planning. Increasingly,
businesses are embracing innovative approaches to human
capital in recognition of the competitive impact that
a focused data-driven talent strategy can have. Equipped
with a seemingly limitless flow of candidate and employee
information, as well as a wealth of potential new platforms,
the HR function has become a strategic powerhouse in the
race to compete. The market interest in people analytics,
consequently, is at fever pitch.
According to a recent study carried out by the
Harvard Business Review, nearly three quarters
of CEOs profiled indicated that Human Capital
was a major contributing factor to sustainable
economic value, whilst over half cited data
access and data-driven insights as being of equal
importance.1
Interestingly however, investment
in big data was lower for HR than any other
department. It’s more than a little ironic, then,
that out of the ‘Top 10 Challenges in Getting
Business Value from Big Data’ listed in HBR’s
report, understanding where to focus big data
investments came out joint last – along with
determining what to do with the insights created
from data. The discrepancy between knowing the
value of people analytics and successfully utilising
the data reflects one of the key difficulties that have
emerged in this rapidly evolving field.
Due to widespread talent scarcity and a resultantly
candidate-driven market, the need to bridge the
capability gap in people analytics is evident.
In 2015, Deloitte produced a comprehensive
study of Global Human Capital Trends, which
showed that although three quarters of companies
surveyed viewed people analytics as important
to success, only eight percent described their
organisation as ‘strong’ in this area.2
In-between
the reality and the ideal stands a labyrinth
of interrelated issues: the suitability of data
in performance metrics; problems integrating new
analytics models into existing platforms; overloaded
or clumsy infrastructure; international regulatory
conflicts; the limits of purely quantitative data;
hiring biases – to name only a few. In such an
environment the obvious watchword is simplicity.
And businesses’ desire for a simpler direction
calls for a reassessment of some of the basic
considerations that underpin people analytics.
“The recruiting organisation that
figures out how to extract the value
of data will define the future
of talent acquisition”
BRENDAN BROWNE, SENIOR DIRECTOR OF GLOBAL TALENT ACQUISITION, LINKEDIN
DATA-DRIVEN TALENT STRATEGY: BRIDGING THE CAPABILITY GAP IN PEOPLE ANALYTICS

In the words of David Ogilvy, ‘where people aren’t having
any fun, they seldom produce good work.’ Understanding
how to improve employee engagement levels represents one
of the most critical uses of a data-driven talent strategy.
Sites like LinkedIn, Glassdoor and Twitter
have compounded the pulling power candidates
have by creating a more authentic narrative
of an organisation. In Edelman’s 2016 Trust
Barometer on Employee Engagement, employees
were the single most trusted source to communicate
a business’ financial and operational performance,
the treatment of employees and customers, and
the business’ ability to handle crises. In the same
survey, the highest rated criterion for building
trust in a CEO was communication with
employees.3
A recent study at Stanford University
also demonstrated how 10.24 million employee
emails sent at a US technology firm could
accurately predict levels of both individual
attainment and cultural adaptation.4
Facilitating the conversation between leadership
and employees therefore represents a vital
aspect of effective people analytics. These
correspondences should be the driving force
around which a company shapes its core vision
and values. Any processes which expedite this
kind of internal engagement – such as company
social networks or feedback platforms – are
likely to have a huge influence on productivity,
retention, and effective decision-making.
(Research from another Harvard study has
confirmed a strong positive correlation between
these kinds of evidence-based decisions and
improved financial and operational performance.)5
Google, for example, take an expectedly unique
approach to engagement by differentiating between
employee development and personal wellbeing
in performance reviews - the argument being that
by keeping the two distinct, emphasis remains on
the person behind the role, allowing a more open
and productive environment to flourish.6
Similarly,
in a comprehensive global generational study, PwC
showed that one of the defining characteristics of
millennials is the value they place on a cohesive
and collaborative work environment. To quote
PwC, ‘emotional connection drives retention’.7
The takeaway is this: where employees feel more
comfortable sharing their ideas and opinions, the
information gathered will likely be more actionable.
And where the culture of a business encourages
engagement, cycles of continuous feedback and
improvement are likely to follow.
employee
engagement

As it becomes increasingly more difficult
to differentiate a role to attract the best candidates,
qualitative insight becomes vital to structuring any
employee value proposition. Effective engagement,
therefore, should be in place from the very outset
of an employee’s journey; a community begins
at the welcome sign.
Traditionally, limits to hard data usage in hiring
models centre around the fact that it’s easier
to detect so-called ‘threshold skills’ (things like
a college degree, IQ, specific certifications) than
it is to determine ‘distinguishing skills’ (such
as critical thinking, culture-fit, and creativity).9
And while it may seem clichéd to return
to Google as the arbiter for big data usage,
their success lies chiefly in the insights they
draw out of their vast reservoirs of information.
To get around the ‘threshold skill’ issue, for
example, Google removed unfair biases in their
hiring strategy by considering top-achievers from
any lower-ranked college more favourably than
average achievers from the very best schools –
what Laslo Bock, Google’s Senior VP of People
Operations, calls ‘casting a wider net’. The
consensus from Google HQ? The former group
usually outperform the latter. Another key lesson
to derive from this is that the onus is on the
company to try out new approaches when testing
the validity of data-derived hypotheses. Internally,
this could mean selecting a specific business unit
to trial new systems and methodologies in.
Externally, this might involve setting up a
controlled experiment between different market
subsectors (‘trystorming’, as it has been dubbed
elsewhere).10
At any rate, however, having the
right analytics talent behind these kinds
of implementation strategies is crucial.
recruitment
strategy
Companies that develop successful people analytics
frameworks outperform their competitors in quality
of hires, retention levels, leadership pipelines and several
other key performance metrics.8
Engagement ultimately
allows a business to go beyond basing talent strategies on
quantitative information like salary or mobility figures alone.
DATA-DRIVEN TALENT STRATEGY: BRIDGING THE CAPABILITY GAP IN PEOPLE ANALYTICSDATA-DRIVEN TALENT STRATEGY: BRIDGING THE CAPABILITY GAP IN PEOPLE ANALYTICS

Despite cross-sector interest in the area, high levels
of investment in people analytics is not always viable.
But infrastructure doesn’t have to mean a large-scale
Hadoop cluster and your own private wing of MIT.11
Education, for many HR leaders, can be the
first point of call when it comes to using data
effectively. A recent paper by the University
of North Carolina business school asserted that
a significant part of bridging the big data talent
gap will come from HR professionals educating
themselves and their leadership teams in the
appropriate big data skill sets, including:
• Advanced analytics and predictive analysis
• Complex event processing
• Rule management
• Business Intelligence tools
• Data Integration.
UNC also stress the importance of a computer
science background that can work in congruence
with keen business knowledge.12
Once again,
however, the keystone in the big data bridge
is the analytics team’s ability to create meaningful
insight from their data sets. As people analytics
progressively becomes a board-level topic,
educating internally brings the twofold benefit
of disseminating knowledge and improving
executive awareness of the value of data, thus
prompting decision makers to confront the current
capability gap. ‘Start with the tools you have’
– say Deloitte. Even basic pivot tables that
are pertinent to the business’ wider aims can
be of value to executive management.
HR teams therefore have a responsibility
to understand the interests of their leadership
and ‘data-cater’ accordingly.
What’s more, a company’s people analytics
capacity doesn’t necessarily stand or fall
on the basis of hiring the top talent in the sector
(who are, naturally, in scarce supply – McKinsey
puts the talent gap in deep analytics at up to
190,000 positions in the US alone).13
Whilst the
value of having a solid contingent of analytics
specialists is obvious, other avenues show potential.
Alongside full-scale predictive solutions from
the likes of Oracle, HP and IBM, so called
‘self-service’ analytics systems including Tableau
and Alteryx have proliferated. Other websites
such as Beapplied.com and Textio are also
beginning to emerge, giving HR more control
over the screening biases inherent to the recruiting
process to ensure that the best talent is not unduly
overlooked. Whilst such platforms might offer
part of the solution, however, there is no real
alternative to deep analytics talent – and companies
who understand this will either hire aggressively
from the relevant sectors (the ‘build’ approach)
or contract in analytics talent where scalability
is needed (the ‘buy’ approach).
Globally speaking, research undertaken
at McKinsey has also found that despite
the geographic discrepancy between North
America and Europe’s data infrastructure
versus that of developing economies, the latter
are able to partake in the big data boom
by outsourcing storage and analysis to centres
located in already developed markets. In addition,
the McKinsey report contends, organisations
in developing countries may actually benefit
by ‘leapfrogging’ to the most recent technologies
and bypassing cumbersome legacy systems.
Whilst a significant amount of work remains
in making big data internationally transparent,
there is certainly untapped potential for mutually
beneficial engagement between developed
and developing markets.
infrastructure
“Best practice big data
companies have built
sufficient scale in a core
group of deep analytical
talent, upon which the
rest of their organization
can draw”
MCKINSEY GLOBAL INSTITUTE

DATA-DRIVEN TALENT STRATEGY: BRIDGING THE CAPABILITY GAP IN PEOPLE ANALYTICSDATA-DRIVEN TALENT STRATEGY: BRIDGING THE CAPABILITY GAP IN PEOPLE ANALYTICS
Finally, analytics influencers have a shared
consensus as to where the people analytics function
must sit within the wider HR infrastructure.
‘If analytics is to become a core part of HR’s
delivery’, says David Green (Global Director
of People Analytics at IBM), ‘then it has to report
into and be driven by the CHRO.’14
Mark Berry
(CHRO at CGB Enterprises) claims that by
ceding analytics to a ‘less analytically inclined’
leader, companies risk mismanaging a function
that depends on ‘a specialised set of knowledge,
skills, and abilities’ – which is only likely to further
damage recruitment efforts for any analytics
function in the future.15
Traditionally, HR teams
have not been aggressive enough in demanding
high-quality visualisation, instead settling for
simply mirroring business operations in their use
of data. A good CHRO will be clued up to the
value of analytics and will know what they want
from the available data, which can only strengthen
the case of the function in front of executive
management. Ultimately, without proven capability
from HR, the budget for analytics is unlikely
to increase.
Understanding the value of people analytics
is essential to retaining a competitive edge
in a marketplace that will only become more
data-oriented. Where companies are serious
about getting the best people – and getting the
best out of their people – a data-driven talent
strategy must firmly underline the road ahead.
Part of the challenge at present is convincing
decision-makers to take the next step. But the
capability gap in big data is not so much a leap
of faith as it is a vital crossing point into the future.
In congruence with developing core analytics talent
and mobilising data to improve employee value
propositions, a well aligned HR analytics function
may be the first step to convincing C-suite to
consolidate their efforts towards further investment
in this field. And if the data is anything to go by,
those who do so will gain a considerable advantage.
From pipelining the best talent to helping to re-elect
a president, data is approaching the era where its limits
are defined only by the imaginations of those handling it.16
conclusion

1
“The Big Data Opportunity for HR and Finance.”
The Harvard Business Review / Workday. Web. pp 1-16.
10 May 2016. https://hbr.org/resources/pdfs/comm/
workday/workday_report_oct.pdf.
2
“Global Human Capital Trends 2015: Leading
in The New World of Work.” Deloitte UP. Web.
pp 1-112. 10 May 2016. http://d2mtr37y39tpbu.
cloudfront.net/wp-content/uploads/2015/08/DUP_
GlobalHumanCapitalTrends2015.pdf.
3
“2016 Edelman Trust Barometer - Employee Engagement.”
2016 Edelman Trust Barometer - Employee Engagement.
Edelman Insights / LinkedIn, 4 Apr. 2016. Web. 10 May
2016. http://www.slideshare.net/EdelmanInsights/2016-
edelman-trust-barometer-employee-engagement.
4
Goldberg, Amir, Sameer B. Srivastava, V. Govind
Manian, and Christopher Potts. “Enculturation
Trajectories and Individual Attainment:
An Interactional Language Use Model of Cultural
Dynamics in Organizations.” SSRN Electronic Journal.
Stanford / Berkeley. Web. pp 1-33. http://faculty.
haas.berkeley.edu/srivastava/papers/Enculturation%20
Trajectories.pdf.
5
McAfee, Andrew, and Erik Brynjolfsson. “Big Data:
The Management Revolution.” (2012): 1-10. www.rosebt.
com. Harvard Business Review. Web. 10 May 2016.
http://www.rosebt.com/uploads/8/1/8/1/8181762/big_
data_the_management_revolution.pdf.
6
“Open Sourcing Google’s HR Secrets - Knowledge@
Wharton.” Wharton, 26 Feb. 2016. Web. 10 May 2016.
http://knowledge.wharton.upenn.edu/article/open-
sourcing-googles-hr-secrets/.
7
“PwC’s NextGen: A Global Generational Study” PwC
/ USC / London Business School. Web. pp 1-16 10 May
2016. http://www.pwc.com/gx/en/hr-management-
services/pdf/pwc-nextgen-study-2013.pdf.
8
Kaushik, Niraj. “12 Steps to Building an Effective
Talent Pipeline.” Oracle Blogs. 13 Aug. 2014. Web.
10 May 2016. https://blogs.oracle.com/oraclehcm/12-
steps-to-building-an-effective-talent-pipeline.
9
Goleman, Daniel. “What People Analytics Can’t Capture.”
Harvard Business Review. 07 July 2015. Web. 10 May
2016. https://hbr.org/2015/07/what-people-analytics-
cant-capture.
10
“What Is Trystorming?” Lean Manufacturing and
Six Sigma Definitions. Web. 10 May 2016. http://
leansixsigmadefinition.com/glossary/trystorming/.
11
McAfee, Andrew, and Erik Brynjolfsson. “Big Data:
The Management Revolution.” (2012): 1-10. www.
rosebt.com. Harvard Business Review. Web. 10 May
2016. http://www.rosebt.com/uploads/8/1/8/1/8181762/
big_data_the_management_revolution.pdf.
12
Ahalt, Stan, and Kip Kelly. “The Big Data Talent Gap.”
(2013): 1-15. UNC Kenan-Flagler. Web. 10 May 2016.
https://kenan-flagler.unc.edu/~/media/Files/documents/
executive-development/execdev-big-data-talent-gap.pdf.
13
“Big Data: The Next Frontier for Innovation,
Competition, and Productivity.” McKinsey Global
Institute, June 2011. Web. pp 1-156. 10 May 2016.
http://www.mckinsey.com/business-functions/business-
technology/our-insights/big-data-the-next-frontier-for-
innovation.
14
Green, David. “Key Takeaways from People Analytics
2016.” LinkedIn Pulse, 3 May 2016. Web. 10 May 2016.
https://www.linkedin.com/pulse/key-takeaways-from-
people-analytics-2016-david-green?trk=mp-reader-card.
15
Berry, Mark. “Seven Deadly Sins To Avoid with
HR Analytics Initiatives.” LinkedIn Pulse, 5 Sept.
2015. Web. 10 May 2016. https://www.linkedin.com/
pulse/seven-deadly-sins-avoid-hr-analytics-initiatives-
mark-berry?trk=prof-post.
16
“HP Vertica Analytics Platform Overview.” HP, 2014.
Web. pp 1-2. 10 May 2016. http://www8.hp.com/
h20195/V2/GetPDF.aspx/4AA5-8917ENW.pdf.
references
Bersin, Josh. “The Geeks Arrive In HR: People Analytics Is Here.” Forbes, 1 Feb. 2015. Web. 10 May 2016.
http://www.forbes.com/sites/joshbersin/2015/02/01/geeks-arrive-in-hr-people-analytics-is-here/#181ce5847db3.
Green, David. “The Oscars for People Analytics.” LinkedIn Pulse, 11 April 2016. Web. 10 May 2016.
https://www.linkedin.com/pulse/oscars-people-analytics-david-green?trk=prof-post.
Jobvite Recruiter Nation Survey 2015. Jobvite. Web. 10 May 2016.
http://www.jobvite.com/wp-content/uploads/2015/09/jobvite_recruiter_nation_2015.pdf.
Schwab, Klaus. “The Fourth Industrial Revolution: What It Means, How to Respond.” World Economic Forum, 14 Jan.
2016. Web. 10 May 2016. https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-
how-to-respond/.
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