1) The document discusses how workforce analytics uses big data approaches to improve talent management and recruiting. It outlines a 5-step process for implementing workforce analytics: clarifying the problem, determining metrics, gathering data, analyzing the data, and presenting results visually. 2) Most companies are still only reporting workforce analytics data, while few are able to forecast or simulate results. Examples are given of how some companies have used workforce analytics to optimize retention, promotions, and talent acquisition strategies. 3) The meeting discussed how workforce analytics can help move companies from decisions based on hunches to data-driven models, showing clearer links between talent expenditures and organizational performance.

1. Workforce Analytics:
A “Big Data” approach to
Talent Management & Recruiting
By Robert Abbanat
May 2016
On March 3rd, 2016, the Talent Transformation Forum of the American
Chamber of Commerce Shanghai hosted a council meeting comprised of
about 20 senior business leaders. The purpose of the meeting was to discuss
the application of ‘big data’ analytics to the process of strategic ‘people’
decisions. The meeting was facilitated by two workforce analytics experts:
Dion Groeneweg, Partner at Mercer; and Nick Sutcliffe from the Conference
Board. The following paper summarizes the topics discussed and proffers an
analysis and summary of conclusions reached.
Origins
Big data has made a big name for itself in marketing, and now appears to be gaining
traction in the realm of talent development. But how can the analysis of big data be
applied to the recruitment and management of talent and why does it matter? How are
those at the forefront of this trend leveraging it to their organization’s advantage? These
were some of the key questions that our group set out to address. Interestingly, we
began with a look at how big data analytics first gained traction and success in the
marketing function.
Not long ago, marketing budgets were regularly challenged, and often constrained, for
lack of evidence that marketing expenditures were delivering any value to the company.
To strengthen their position, marketing professionals began using data to show
correlations between various marketing activities and growth in sales and profit. The
result has positioned marketing data analytics as a central pillar in strategic decision
making. More recently big data has become big business in the internet age, with
billions being spent on tracking, predicting and marketing to consumers based on troves
of data that are collected through mobile devices.
The success of big data in marketing has
inspired HR professionals to find a parallel
solution to a similar problem. The inability
to show a clear ROI has long been a barrier
Rob Abbanatis CEO of Ivy League English and
Chairman of the Talent Transformation Forum at
the American Chamber of Commerce Shanghai. He
can be reached at rabbanat@ile-china.com.

2. for increased spending on training and development, especially during economic
downturns. Following the lead of their marketing counterparts, HR professionals are
increasing both the scope and sophistication of big data analytics to support their
organization’s ‘people strategy.’ The objective is to move organizations away from
decisions based on hunches towards models that can be measured for results, thus
showing clearer links between talent development expenditures and organizational
performance.
Senior decision makers and strategists are looking
for more predictive, metrics-based models for
building teams capable of flourishing in a rapidly
changing global business environment. The nascent
success of data analytics among HR professionals,
combined with a broader movement towards
metrics-based decisions, portends an answer. This is
particularly relevantin China where the slowing
economy is forcing business leaders to shift their focus from top-line growth and market
acquisition to a sustainable model based on profitability. Key concerns include better
organizational performance, better talent development, better talent retention
and expanded organizational control. Our consulting expert reinforced this noting
that his China-based customers are all looking for help to increase productivity.
Whereas HR was previously concerned with workforce planning, in this context, the
application of big data to talent development has adopted the moniker of workforce
analytics.
Process
As the room full of seasoned business leaders began discussing and debating the topic,
one thing became quickly apparent: many of the participants had relatively little
knowledge and experience in the application of data analytics to talent management.
This underscored thatworkforce analytics is a nascent discipline that has much room
for improvement and adoption. Fortunately our experts were able to outline a process
for implementing workforce analytics using the five steps below.
General Workforce Analytics Implementation Process
1. Problem
•Clarify the
problem
you are
trying to
solve
2. Metrics
•Determine
the metrics
used to
analyze the
problem
3. Data
•Gather the
data for the
metrics
chosen
4. Analysis
•Analyze the
data
5. Story
•Use the
data to tell
a story,
preferably
visual
Meet the New Boss: Big Data
Companies Trade In Hunch-Based Hiring
for Computer Modeling
–The Wall Street Journal

3. 1. Clarify the Problem: The first and perhaps most important step is for management
to decide what problem they seek to resolve through workforce analytics. In many
cases, this may require a shift from thinking in terms of “HR metrics” to “Talent
metrics,” as the focus should be on improving organizational performance. One
example offered is the value of addressing “time to productivity” rather than “time to
hire.” Where time to hire has been a common element of HR metrics, leaders should
recognize that the time to productivity—i.e. the length of time it takes to fill a
position and for the new candidate to reach a specific level of performance—is not
only a more important metric, but one that can be addressed with workforce
analytics.
2. Determine the Metrics: Once the problem is identified, the metrics which define the
problem must be chosen. In most cases, no more than 5 or 6 metrics will be
sufficient. Any more will likely make the analysis more difficult and less impactful. It
may be wise to get input from multiple functional departments which can not only
help to clarify the problem, but can also help to clarify the right metrics and collect
the data.
3. Gather the Data: After the metrics have been selected, the next step is to gather the
data. One of the unusual aspects of workforce analytics is that the data tends to be
highly structured, such as payroll data, time to hire, performance reviews, etc. This
contrasts with the unstructured text, sensor data, audio, video, click streams and log
data typically found in marketing.
Where HR is concerned there is, in many cases, plenty of data already available.
Most companies already collect data on everything from diversity to attendance to
scoring on performance reviews. Some of the more sophisticated metrics include
expanded span of control, organizational performance and talent retention. New
technologies are emerging that have the ability to even track mood, focus and
emotion during work hours. The fact that there is a plethora of data available
highlights the need to selectively choose the metrics that address the problem to be
solved.
4. Analyze the Data: With the data in hand, the next step is to perform the analysis.
One of the first concerns raised to this point was whether or not the organization
needs data scientists for effective analysis. Our group generally felt workforce
analytics can and should be used to make decisions that are ‘directionally correct’
rather than ‘precisely wrong.’ Where the issues being addressed—improved
organizational performance, greater talent retention, etc.—are often measured over
longer periods of time, this logic is consistent. As such, data scientists aren’t
necessary.
One recommendation however was to keep workforce analytics away from the
reporting, accounting and finance teams as their approach may be too narrow and

4. thus reduce the overall effectiveness of the exercise. Again, it may be wise to enlist
the support of multiple departments to gain the clearest view of what the data is
saying.
Another factor to consider is how to benchmark the data by comparing it against
external data sources. While there is lots of external data that can be mined, and a
comparison can be instructive, it must be considered in context and may not directly
relate to your organization’s internal strategy.
5. Tell the Story: The ultimate result of the entire process should be a visual story that
illustrates the problem and suggests potential solution(s). One of the key takeaways
from the successful application of data analytics in marketing was the impact that a
well-conceived graphical representation of the analysis has on the decision-making
process. It’s an application of the age-old adage that “a picture is worth a thousand
words” when trying to get the CEO’s attention and influence a decision.
Implementation
So how are organizations using data analytics for talent recruitmentand development
today? According to our implementation experts, workforce analytics is still more of an
art than a science. If we look at spectrum of implementation as outlined in the graph
below, only about 2% of global respondents have implemented workforce analytics to
the level where they are able to forecast and simulate results. Fully 50% of companies
are at the reporting stage only while 20% of companies are segmenting the data and
benchmarking; Just 10% are looking at correlations and causations.
Workforce Analytics: Measurement Continuum
Source: The Workforce Analytics Institute

5. Applications
Despite its youth, workforce analytics has traction even among the group of just 20
senior leaders at our Council meeting. One of our participants, a senior HR professional
at one of the world’s premier technology and consulting organizations, told of her
company’s use of data analytics to optimize employee retention and promotion. With
troves of data that had been collected over decades, they strategies for acquiring
technical talent in emerging markets. This differed from their approach in developed
markets where talent was more readily available. The result was an increase of global
talent in support of local markets, and a shift from rewarding high-potentials to
rewarding high performers.
Another participant who leads the local training efforts for one of the world’s most
successful FMCG brands told of his organizations application of data analytics to
optimize talent acquisition. The challenge they are trying to address is that local talent
tends to have a better understanding of the local market but often lacks the knowledge
of best practices that global experience brings. Conversely, global talent knows how to
implement the company’s well-honed global practices, but lacks insightto compete
within the local market. The company’s solution was to analyze ratios for global vs. local
as well as internal vs. external recruitment to identify the optimal blend. The result has
been an increase in the company’s long-term profitability while maintaining growth.
In a parallel example, another participant indicated that his former employer, a Fortune
100 manufacturing company, uses workforce analytics to strike a balance by looking at
the percentage of staff that is focused on short-term vs. long-term growth. Similarly,
they also analyzed the impact of having most of the key decision makers located outside
China on the company’s growth and performance within China.
Challenges
Given that workforce analytics is just emerging, there are still a host of challenges to be
addressed. Chief among them is the need to properly set expectations regarding the
relative timeframes to see results. For most businesses, decisions are often driven by
the need to show quarterly results. The transformation of talent, which is often the
objective of training and development, can take many quarters or even years. This
leaves a gap in the process of decision making to step back and look at the results over
a longer period of time.
Those responsible for talent development must also consider the selection of
appropriate training methods, which are also rapidly emerging. Executive education,
MBAs and EMBAs have long been a popular stepping stones for upwardly mobile
professionals. To attract top talent, many organizations offer tuition grants and
subsidies for these programs. However, popular consensus among the HR professionals
in our group is that these programs are not good for the organization because they lead
to excessive salaries that aren’t justified by productivity and higher attrition.

6. Another concern is the encroachment of workforce analytics on privacy. While some
planners may be keen to utilize whatever data is available, some worry that technology
continues to expand the types and methods of data collected. Consider for example the
use of advanced facial recognition tools, implemented through an increasing number of
workplace cameras, to track employee’s facial expressions and make predictions
regarding their emotional state. While this data could be used to address a wide range
of problems ranging from peak productive hours to an employee’s satisfaction with
various aspects of her job, or even a pattern of moods that could be connected to
external factors, it also starts to harken Big Brother.
Conclusions
As the world becomes increasingly globalized, and a larger share of economic growth
comes from developing economies, the ability for business leaders to anticipate which
skills their organizations will require, and where, becomes a key competitive factor.
Beyond planning, they also need to be able to make decisions to maximize the
performance and retention of talent. This may be particularly true in China, where the
economic landscape shifts extremely fast.
The quickening pace will no doubt increase the pressure to make more accurate
decisions with fewer errors. As such, we should expect the application of workforce
analytics to pick up steam. For those taking a leading role in this process, it may
behoove us to examine where our organizations lie on the Workforce Analytics
Measurement Continuum, and what barriers are preventing us from moving closer
towards the ability to forecast through simulation.
As for what comes next, one of our experts suggested that wearable devices will herald
a new era of workforce analytics as companies gain the ability to track with astonishing
detail and precision the performance of our human capital. As this will no-doubt raise
privacy concerns, it underscores the need for organizations to behave responsibly and
place the highest value on their employees’ trust.