Workforce Analytics:A “Big Data” approach to

Talent Management & RecruitingBy 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.

OriginsBig 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 Abbanat is 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.

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 relevant in 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.

ProcessAs 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 that workforce 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 DataCompanies Trade In Hunch-Based Hiring for Computer Modeling

–The Wall Street Journal

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

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.

ImplementationSo how are organizations using data analytics for talent recruitment and 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

ApplicationsDespite 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 insight to 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.

ChallengesGiven 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.

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.

ConclusionsAs 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.