Where’s the Value In Talent Analytics?

There’s a problem in the world of big data, and size isn’t the issue. More data is not adding up to better decisions.
A 2012 Oracle study shows the volume of data has increased an average of 86 percent in the past two years, and 60 percent of executives rate their companies unprepared to leverage the data and cite significant gaps in people, processes and tools.

For those in talent management, this problem is compounded by the nature of the investment decisions they manage. Research firm the Aberdeen Group found that in 2012, 49 percent of organizations aimed to address business leaders’ inability to leverage HR and workforce data to make better operational decisions. Research from the Corporate Executive Board, or CEB, found that business executives will require, on average, a 23 percent improvement in senior leader performance to achieve performance goals across the next 12 months. (Editor’s note: The writer is an employee of CEB).

The new work environment, however, is driving significant and universal changes for senior leaders. Nearly three-quarters of senior leaders report they face more flux in their roles and responsibilities compared with three years ago. Such high exposure to new situations that are different in magnitude, kind and predictability leads to an average drop of 16 percent in leader performance — that’s a substantial performance penalty at a time when executives are calling for aggressive performance improvements.

This presents a challenge. Talent leaders are being asked to provide evidence that investments made today will pay off, but the return on investment will not be seen for years to come. Further, talent managers have said their biggest challenge with big data is where to focus the investments in the HR function, according to the 2013 study “The Emerging Big Returns on Big Data” from Tata Consultancy Services. If talent managers struggle with where to focus their investments on big data and need to provide evidence on those investments paying off, where should they start?

How to Drive Big Insight
Organizations are increasing their investments in leadership development, both in absolute terms and as a proportion of their overall leadership and development spending. CEB research finds that 68 percent of organizations increased spending on senior leader development in 2011 and 2012, and the average organization is forecasting an increase in spending on leadership development of 5 percent in 2013. Further, nearly two-thirds of organizations surveyed reported senior leader development represents a greater proportion of the leadership and development budget than it did three years ago.

These reasons are why the conversation is shifting from big data to big insight. In the talent analytics space, insights need to address fundamental questions, such as, “Does the organization have the people who will take a key business unit, or the organization, from where it is today to where it needs to be tomorrow?” And, “What do business leaders need to know to be confident that people investments made today will pay off tomorrow?”
A big insight tells an organization whether an issue is true, and if it is, what actions need to be taken to resolve it and improve organizational performance. It is the value of the insight, not just the size of the data, that makes it big.

Four key principles can help to deploy talent analytics that deliver big insights. The first principle is relevance. Analytics must address a critical business issue to deliver value. When asked if talent analytics addresses the right issues, only 17 percent of business executives in the CEB study agreed. Only 14 percent of business executives agreed that talent analytics used today are aligned with the right business issues. It may seem obvious, but best practices in talent analytics demand relevance.

The second principle is impact. Since the returns from talent investments are long-term, a key value in effective analytics lies in managing the odds today in favor of a positive future return from those investments. For example, consider a low return from an investment in high potential programs, which will be discussed later.
Maturity models for analytics address the need to move up from describing a current state to predicting where talent leaders want to be tomorrow. But there is a step beyond that — talent analytics needs to present results that set a prescription for action, which is the third principle.

To have relevance, impact and establish an agenda for action, the most fundamental principle is to develop the right perspective. Perspective relies on whether talent leaders are asking the right questions and whether the right data is being used to answer those questions. First, take a look at relevance, and build off an insight from W. Edwards Deming, the father of total quality management: “If you do not know how to ask the right question, you discover nothing.”

A 2012 research study from human resources association WorldatWork shows that 60 percent of companies globally cite a problem attracting high-potential employees. But according to research from CEB, in 2012, more than 70 percent of companies had an established high potential program or were in the process of building one. On average it takes about 29 months to develop a high-potential, midlevel manager into a “ready now” senior leader, according to a 2013 study from Aberdeen Group. Further, employees who go “above and beyond” plan to stay at their employer, indicating that talent leaders need to focus retention efforts on their most productive workers.

Framing Big Insights
When breaking this problem down, three questions emerge that need to be answered.

First, what do people need to rise to a more senior position? Some abstract notion of potential is not going to tell a talent manager whether an employee has the appetite and ambition to reach the senior position, which high potential programs exist to supply.

Second, will those people be effective when they get to that next level? In today’s rapidly changing economic and social environment, this question needs to be answered in the long-term based on where the organization sees itself in the future, and in the short-term as the organization approaches that vision. Essentially, this question acknowledges the fit between the individual’s talents, the role he or she may be effective in, as well as what is needed to improve the fit between that person and role.

Third, will an employee nominated as a high-potential candidate be with the company when he or she gets to that next role? A report from leadership consultancy the Center for Creative Leadership found in 2010, between 14 and 33 percent of high-potential candidates reported they were looking outside their organization for employment. Addressing this question is critical to understand whether the future manager and leader talent will be there when an organization needs it.

When it comes to the data aspect of relevance, tenure and experience are low predictors of career success, and performance in a role today is a better, but not complete, answer — it has an accuracy rate of about 29 percent, according to CEB. Essentially, performance today speaks to whether someone is realizing his or her potential in a current role. It doesn’t necessarily capture potential for more senior and critical roles.

Potential for the future requires clarity about the knowledge, skills and abilities that more senior and critical roles require. Those criteria need to be compared to data on the performance potential a candidate offers for future roles, knowledge of the investment required to meet any potential performance gaps and whether the employee has the aspiration for a more senior role. Yet, while those data are among the most critical sources of insights, They are also the ones seen as the most insufficient.

The amount of data available may not be the most critical value changer in an investment in talent analytics. Value lies in framing that investment by focusing on relevance, impact and action. It is also important to ask the right questions with accurate data in hand to get the best perspective on interventions that could change the odds to generate the business outcomes business leaders are looking for.

Eugene Burke is chief science and analytics officer at the Corporate Executive Board, a member-based advisory company. He can be reached at editor@talentmgt.com.

Data Insights at Work at Time Warner Cable
Janet Manzullo, group vice president of talent acquisition at Time Warner Cable, said she relies on analytics to assess and maximize value her department delivers to the business. Selection programs are evaluated against employee performance metrics the business identifies as most relevant, such as number of sales or percentage of issues resolved in one call. Talent acquisition data such as test scores are analyzed against relevant metrics to weigh the impact of the program. Knowing the impact leads to a variety of actions, from enhancing program acceptance to directing further program improvement.

For example, Time Warner’s talent analytics showed an average customer service agent who scores in the top quartile on the pre-hire assessment of sales focus generates as much as 71 percent more sales revenue than the average agent who scores in the bottom quartile. High scorers on customer focus also prevent 19 percent more of the inconvenient and costly technician visits to customers’ homes and businesses by more frequently resolving issues over the phone. This leads to lower costs, higher margins and better customer experience. Adjustments suggested by the same analyses are being implemented to further increase business value.
— Eugene Burke

How Big Data Can Transform HR
By David Bernstein
Josh Bersin of Bersin by Deloitte declared 2013 to be the year of big data in HR. However, research by the Center for Effective Organizations revealed a significant gap between where HR is and where it would like to be in the realm of big data. While the HR practitioners surveyed felt HR should have a primary role (4.4 out of 5) in big data efforts, they said they actually have an occasional role (2.9).

HR should have a primary role in big data efforts because descriptive and predictive analytics can transform the function by empowering talent leaders to be more evidence-based, to leverage foresight and be more strategic.
Evidence: Big data frees organizations from relying solely on gut instinct, assumption or tradition. Gas and electric power company Black Hills Corp. discovered after an acquisition that about one-fourth of its workforce would soon be eligible for retirement. The company needed solid guidance on how to proceed so it could meet a government mandate to remain adequately staffed. Black Hills used analytics to discover how many workers were likely to leave or retire during the next five years, how job functions were expected to change and time to productivity for new workers, among other things. This enabled the company to create a five-year workforce plan that defined talent needs and to subsequently transform recruiting and training strategies and processes.

Foresight: Rather than basing decisions on what happened months or even years ago, big data lets organizations see what is happening now and how the present is likely to impact the future. IBM combined data from its internal global services billing and HR databases to look for patterns that could not have been detected otherwise. Using the insights derived from its big data consultants, the company was able to forecast talent shortfalls in critical areas. Because they were able to see ahead, talent leaders knew they would have time to train some of the talent needed to fill projected gaps and to hire others. The company could also see where it had talent overages and adjusted accordingly.

Strategy: The 2012 study “Rethinking Human Resources in a Changing World” by the Economist Intelligence Unit and professional services company KPMG revealed that HR perceives its biggest challenge to be seizing the opportunity to transform itself into a strategic player. Another 2012 report by ManpowerGroup and Right Management, “A Pulse on Talent Management in the Year Ahead,” stated: “In the human age, the shift in strategic focus from capital to talent is putting business performance results at the door of HR leaders. Success will be measured by their ability to work with the business leadership to … leverage talent as a competitive advantage.”
Airline catering company Gate Gourmet used its talent acquisition process to be more strategic. The company analyzed many large data sets for its roughly 1,000 employees at Chicago’s O’Hare Airport. The company had a turnover rate of 50 percent and wanted to find out why. By searching for patterns within the data in its internal systems — applicant tracking systems, HR information systems, performance review systems, point of sale data, sales performance and compensation — and data from external systems — compensation, demographics, recruiting informatics, traffic, transportation and social media — the company learned the turnover rate was closely connected to factors such as how far away an employee lived from the job site and accessibility to public transportation. With these insights, the company adjusted its hiring strategies, achieved “fully staffed” status for the first time and was able to lower unwanted turnover to 27 percent.

David Bernstein is the vice president of eQuest’s big data for HR division. He can be reached at editor@talentmgt.com.