1. 32 T+D | July 2014
HUMANCAPITAL
Predictive talent analytics tells us whom to
hire and how we should manage them.
PHOTO: THINKSTOCK

2. July 2014 | T+D 33
podcast
BY STEPHANIE CASTELLANO
Y
ou may not keep up with trade
publications as often as you
should, but if you caught the film
Moneyball in 2011, you learned all about
predictive talent analytics, the latest hot
topic in the human capital industry.
Moneyball is the true story of how
the general manager of the Oakland A’s
baseball team used predictive models to
assess players’ potential performance.
Basing his acquisition decisions on this
data, General Manager Billy Beane put
together a team that then pulled off
the longest winning streak in American
League history.
HR didn’t take long to make the
connection: If baseball teams can use
player statistics to predict performance,
thereby gaining a huge competitive ad-
vantage, why can’t companies do the
same with their employees?

3. 34 T+D | July 2014
When data starts making decisions
Organizations have long been accustomed to
collecting certain human capital–related data.
Trainers track the number of participants
in their courses, the percentage of learners
who passed the exam, and the level of learner
satisfaction with the course. HR profession-
als measure retention, time to hire, and cost
per hire. Senior leaders review the results of
employee engagement surveys. These are all
known as “descriptive” or “historic” metrics—
they describe what has already happened
or what is currently happening within an
organization.
But predictive metrics do descriptive
metrics one better: They describe future out-
comes, serving as powerful decision-making
tools. For example, a company tracks its an-
nual retention rates and sees that retention
has been decreasing during the past few years.
Now, here’s the point of transformation: Fur-
ther analysis reveals that the turnover is
mostly occurring in a department that requires
long working hours. The company decides to
reduce workload for these positions. It then
observes an increase in retention for the de-
partment, which positively affects overall
turnover.
Historic and predictive analyses are two
sides of the same coin, says Alec Levenson,
senior research scientist at the Center for Ef-
fective Organizations at the University of
Southern California. “Conducting multivariate
analysis on existing data contains both histori-
cal and predictive parts. It uses historical data
to predict the future.”
In other words, “A metric tells you that you
have a problem,” says John Sullivan, a leading
HR strategist based in Silicon Valley. “A predic-
tive metric tells you what to do about it.”
What you should know about standards
As predictive talent analytics grows, con-
sultancies have been cropping up to assist
organizations that are struggling with their
data. One of these is the Center for Talent Re-
porting, a nonprofit organization created in
2012 by KnowledgeAdvisors, a for-profit pro-
vider of analytics tools and consulting services.
The Center for Talent Reporting has devel-
oped standards, called the Talent Development
Reporting Principles (TDRp), for defining and
reporting on a variety of human capital pro-
cesses. To its paid members, the organization
offers access to a library of more than 500
measures relating to talent acquisition, learn-
ing and development, capability management,
leadership development, performance man-
agement, and total rewards.
PwC Saratoga is a similar initiative from
PriceWaterhouseCoopers that has established
a metrics and benchmarking database, which
contains “hundreds of global standards for
metric formulas and data elements.”
The Society for Human Resource Manage-
ment, not about to be left behind in its own
field, embarked on a similar attempt to create
a set of standard HR metrics. However, SHRM’s
proposal, which would make data from leading
companies publicly available for benchmark-
ing purposes, was shot down in late 2012 by
the HR Policy Association, a lobbying organiza-
tion that represents 335 of the nation’s largest
corporations. According to an article on Work-
force.com, the HR Policy Association claimed
that the metrics would overburden employ-
ers and expose proprietary information to
competitors.
Proprietary data is one reason that the
movement to standardize human capital
metrics has had a lukewarm reception among
some industry professionals. Although most
organizations are eager to benchmark their
results against their competitors’, many are
hesitant to reveal their unique formulas for
success—especially when it comes to their
talent.
PROPRIETARY DATA IS ONE REASON
THAT THE MOVEMENT TO STANDARDIZE
HUMAN CAPITAL METRICS HAS HAD A
LUKEWARM RECEPTION.

4. July 2014 | T+D 35
Google, for example, is constantly lauded
for its use of predictive analytics to inform
hiring and management decisions. The com-
pany has developed algorithms to predict
performance at all stages of the employee life
cycle, including what makes a great manager
and what makes an employee likely to leave,
but Google keeps these algorithms close to
its chest.
“It’s like proprietary metrics in baseball,”
explains Sullivan. “Each team knows how their
players’ statistics impact their success, so they
don’t share that data with their competitors.”
Other industry experts defend the value
of standard human capital metrics and re-
porting processes. Kevin Oakes, CEO of the
Institute for Corporate Productivity (i4cp) and
a board member of the Center for Talent Re-
porting, concedes that while an organization
will always have metrics that are specific to it,
“TDRp does a great job of providing a foun-
dation for human capital analytics and helps
answer commonly asked questions” about
what data should be collected and how these
measures should be defined.
“TDRp merely provides a common vo-
cabulary,” says Oakes, “along with common
statements, reports, and processes, which al-
low human capital professionals to speak the
same language and improve their ability to
have an impact.”
As organizations prepare to dive into talent
analytics, a jumping-off point may not be such
a bad thing. A comprehensive library of mea-
sures and guidance on how to report on data
findings is probably very welcome to many
organizations. But experts like Levenson and
Sullivan warn that organizations must learn
how to interpret and act upon the data they
are collecting—so instead of merely generat-
ing “endless reports that no one reads,” they
are identifying the most important metrics and
turning them into powerful decision-making
tools.
“The data needed for improving human
capital decision making often does not lend
itself well to standardized reporting,” Leven-
son adds. “Less attention should be paid to
uniform metrics across all employees or orga-
nizations, and much greater attention should
be paid to the metrics that are most important
for a given organization, business unit, func-
tion, process, or role.”
What we can do with data
In an article for The Atlantic titled “They’re
Watching You at Work,” deputy editor Don
Peck examined some of the most future-
forward uses of predictive talent analytics.
For example, Knack, a Silicon Valley start-
up, develops video games that can predict
an employee’s potential as a leader or in-
novator. The games, designed by a team of
neuroscientists, psychologists, and data sci-
entists, generate several megabytes of data
from a player’s performance that give hiring
managers insights into that individual’s level
of creativity, persistence, capacity to learn
from mistakes, ability to prioritize, and social
intelligence.
Gild is a company that uses analytics to
identify promising software engineers. It
scours the web for breadcrumbs of data that
potential employees leave in their trail, in-
cluding software engineers’ coding activity on
open-source platforms, participation on ma-

5. jor social networks and other Internet forums,
and data from past projects.
At the Massachusetts Institute of Technol-
ogy, researchers have developed electronic
“badges” that employees wear throughout the
day that track information about their inter-
actions with co-workers—the frequency and
length of their conversations, their tones and
gestures, how much they talk and listen, and
how often they interrupt. The data are then
analyzed to identify factors
common to successful
teams and effective
leaders.
These tech-
nologies are
considered
“not only as
a boon to a business’s productivity and overall
health, but also as an important new tool that
individual employees can use for self-improve-
ment,” wrote Peck.
Other organizations have taken their data
into their own hands. Farmers Insurance Group
brings together multiple internal and exter-
nal data sources in one proprietary online tool,
where the data is used to build personalized
development plans for each employee. PepsiCo
redesigned its sales jobs and significantly in-
creased employee pay after one analysis, which
led to increased retention and greater capacity
utilization. PriceWaterhouseCoopers reduced
its turnover through a variety of initiatives after
an analysis helped prioritize which levers would
have the biggest impact.
What’s holding us back
If organizations know that predictive talent
analytics is a promising endeavor, then why,
according to research from i4cp, have less
than 7 percent of organizations systematically
undertaken it?
Many experts agree that there are sim-
ply too many metrics from which to choose.
“Too often the cart is put before the horse,”
says Levenson. “Human capital metrics are
selected to populate scorecards without vali-
dating that they are the measures that matter
most for decision making and improving or-
ganizational effectiveness.
“Rather than choose metrics simply for the
sake of having them, more analysis is needed
at the front end to set up and test causal
models of organizational performance,” Lev-
enson adds. “After you test a causal model
and find the factors that matter most for im-
proving performance, the metrics that you
need to monitor and manage toward will be
easy to identify.”
These customized metrics, however, require
analytical skills to build—skills that many or-
ganizations don’t have. Although best-in-class
organizations have dedicated analytics teams
that mine data and design algorithms as part of
their daily work, many HR and training func-
tions struggle with basic data collection and
interpretation.

6. July 2014 | T+D 37
“Our most recent research shows that HR
is the department with the lowest analytical
ability relative to all other departments within
an organization,” Oakes says. However, i4cp
noted a trend in many HR functions of part-
nering internally with departments such as
finance or marketing, which have more statis-
tical expertise.
An organization that breaks down its silos
to share data between functions will estab-
lish, if not stunningly accurate predictive
models, more effective cross-functional com-
munication. Data silos are a common problem
within organizations, severely limiting the da-
ta’s impact.
In a December 2013 T+D article, Elliott Ma-
sie, head of the organizational effectiveness
think tank The Masie Center, wrote: “Imagine
correlating performance reviews with learning
activities and hiring data, either for thou-
sands of employees or drilled down to a single
worker.” He continued, “The relationship be-
tween selection, training, and competency is
very interesting. For example, we often evalu-
ate the impact of a leadership program with
the assumption that we did great things in the
program. In reality … much of it has to do with
how well we select the program participants
from our pool, and how well we select people
to join the organization.”
Finally, another challenge inherent in
predictive talent analytics is that many pro-
fessionals—HR practitioners, senior leaders,
and employees alike—consider it an ethically
loaded practice, rife with privacy, security,
and transparency issues. Organizations must
tread carefully in this new field because col-
lecting and using certain kinds of data could
lead to discrimination lawsuits. There also is
the potential emotional impact on employ-
ees themselves, of constantly being under the
microscope.
Keeping data in its place
In The Atlantic, Peck concluded that, ulti-
mately, these new tools to help get people into
the right jobs and, once there, to help them
succeed, “strike me as developments that are
likely to make people happier.”
Undoubtedly, predictive talent analytics
is a step forward for our society, which al-
ways has relied on human intuition to make
decisions about people’s livelihoods. This has
allowed hidden biases to infiltrate hiring and
management decisions—even today, when
organizations are legally bound to inclusive
policies.
But most experts refuse to entirely toss
aside human judgment in the workplace.
“Nothing in the science of prediction and se-
lection,” wrote Peter Capelli, a professor of
management at the Wharton School, “beats
observing actual performance in an equivalent
role.”
“No matter how much data we collect,” adds
Levenson, “there will always be unexplained
gaps that can only be resolved through direct
observation and interaction—by talking to the
people, their managers, and their work groups.
The best diagnoses always use a combina-
tion of ‘hard’ data and qualitative information,
gathered through direct observation and
stakeholder interviews.”
Stephanie Castellano is writer/editor for ASTD;
scastellano@astd.org.
PREDICTIVE TALENT ANALYTICS IS A STEP FORWARD FOR OUR
SOCIETY, WHICH HAS ALWAYS RELIED ON HUMAN INTUITION TO
MAKE DECISIONS ABOUT PEOPLE’S LIVELIHOODS.

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