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2 years ago
Wise Practitioner – Predictive Workforce Analytics Interview Series: Haig Nalbantian at Mercer

 

In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, The Pay Equity Revolution:  How Haig_NalbantianAdvanced Analytics are Helping to Close the Gender Pay Gap in Organizations, we interviewed Haig Nalbantian, Senior Partner, Co-leader Mercer Workforce Sciences Institute at Mercer. View the Q-and-A below to see how Haig Nalbantian has incorporated predictive analytics into the workforce of Mercer. Also, glimpse what’s in store for the new PAW Workforce conference, May 14-18, 2017.

Q: How is a specific line of business / business unit using your predictive analytics method to inform decisions? 

A: We’ve been conducting pay equity modeling and assessments either alone or as part of a broader workforce analysis since the early 90s. In the past five years or so, this area of work has grown enormously. More and more of our clients – in the US and, increasingly in Europe as well – are conducting annual pay audits to proactively address pay equity issues for women and minorities. In working with us, they choose to rely on comprehensive predictive models of base pay and total compensation that account for the multiple individual, group and market factors that drive pay in organizations. In this way, they not only isolate the effects of specific demographics themselves, thereby assessing if and to what extent there are unexplained pay disparities associated with gender or race, but also get a deeper insight about explained differences – that is, of the root causes of persistent differences that show up in raw (unadjusted) comparisons of pay levels.

While those concerned with legal challenges regarding pay equity commonly use statistical controls to explain pay differences and reduce estimates of the size of pay disparities, the more strategically-minded leaders in this domain use these same controls to better understand why pay disparities exist and what can be done systematically to reduce or eliminate them in a sustainable way. I am pleased to see more organizations moving away from a predominately legal or compliance view of pay equity to a more expansive strategic view that seeks to address systemic sources of gender and racial disparities in pay. Mercer’s When Women Thrive research has shown that aggressive evaluation and management of pay equity is a leading indicator of greater success in other aspects of employment equity. Specifically, those organizations which have specialized, independent teams using statistical methods to assess and ensure pay equity as part of the annual compensation process are significantly more likely to do better in securing a more diverse workforce and leadership team. Focus on pay equity and you are likely to end up with better diversity outcomes overall.

Many of our clients do, in fact, rely on our predictive modeling approach to pay equity, commissioning us, on an annual basis, to estimate statistical models of pay determination to assess if and to what extent pay disparities exist and make adjustments where bona fide pay gaps are found. They typically do this work as part of the annual compensation review.

Q: If HR were 100% ready and the data were available, what would your boldest approach to pay equity deliver?

A:  In the best of all worlds, organizations will evaluate and address pay equity in the broader context of what the organization actually rewards. Our team has undertaken analyses of the drivers of pay across literally hundreds of organizations in the US and abroad for almost twenty five years now. We find the drivers of pay vary significantly across and even within organizations. They also vary over time as changing business models and objectives and changing labor market dynamics force organizations to adapt their rewards to help drive corresponding changes in their workforce. Effective pay equity practices must account for such changes and help ensure that pay equity actions align with evolving reward strategies. So, for example, if a new business strategy places a premium on certain new roles, it is important, from a gender pay equity perspective, not only to know that women in those roles are paid on par with comparable men, but that women are getting the opportunity to access these new and valued roles.

If these new roles command higher pay, disproportionate representation of men would end up increasing the raw pay gap and likely diminishing the prospects of women to be successful in the organization. A successful pay equity process will keep tab of underlying changes in what is being valued by the organization to ensure women, minorities and other groups of interest are not systematically disadvantaged by market- or internally-driven shifts in the valuation of skills, knowledge, capabilities, experience, behaviors and roles.

Properly designed, a pay equity assessment is folded into the annual compensation review; it becomes an opportunity to assess the strategic alignment of rewards with business goals. Most our clients pursue this approach. A pay equity review is not a one-time study; it is an ongoing process of rewards review, one that is of significant strategic importance to the organization.

Q: Do you think “black box” workforce predictive methods will become widely embraced in the pay equity domain?

A: “Black box solutions” are for functional tacticians at best, not practitioners of strategic workforce management. Strategic workforce management requires understanding and effectively communicating the story within the data. By design, black box solutions bypass the story, substituting claims of “predictive validity” instead. Time may prove me wrong, but I have yet to see a compelling human capital storyline emerge from statistical relationships or algorithmically-generated predictions alone. Explanatory analytics – understanding what’s behind relationships detected in the data – is, in my view, central to building and articulating a story that can engage leaders and compel action. Since I view pay equity as fundamental to reward strategy, I am reluctant to embrace the use of automated data analytics as the basis of pay equity assessments. If pay equity is part and parcel of rewards alignment, there is no substitute for careful modeling and interpretation of the drivers of rewards.

Q: Is there a risk of making the pay equity process too complex?

A: Our domain of workforce analytics always carries the risk of being overwhelmed by complexity of approach or analytical techniques. This has never deterred our team, however, from pursuing a more sophisticated technical solution if we are sure that solution will lead to more accurate conclusions and better results. The proof ultimately is in the results achieved. As I mentioned in my interview last year, sports analytics has definitely added complexity to the statistics tracked and followed by front office professionals, field managers and coaches, players, player representatives and sports journalists, but they have gained speedy adoption in the industry. Few of these stakeholders really grasp the technical dimensions of sports analytics. Nonetheless, they are pervasively used – because they work, because they lead to better decisions and more targeted investments. Staying away from sophisticated analytics on grounds of complexity is a cop out, one that is becoming increasingly untenable in the HR field.

The analytics used for pay equity are not all that complex. Most HR leaders have a basic understanding of multivariate regression analysis. Even if they don’t, they can readily understand that measuring pay disparities and determining their sources requires accounting for other non-demographic factors that also influence pay levels. That’s what good modeling will accomplish. More complex is the way in which the methodology is practically applied and how the results are translated into action.

So, for example; if pay strategies and pay determination are different across business units, functions, geographies, occupations and job families, do you need to model each of these separately? What determines the degree of segmentation used? Technical requirements, such as minimum required population sizes for statistical modeling, may trade off against practical business considerations. There is no pure science to inform such decisions. Similarly, once you identify pay disparities or, for instance, employees who are “under-paid” relative to peers – i.e. “outliers” – how do you close the gaps? Do you address outliers only in groups where demographic disparities have been detected? Should you make adjustments for women and non-whites only? Implementation questions such as these are generally more “complex” and challenging to navigate than are issues around methodology. Seldom do we get drawn into detailed conversations about statistical techniques. On the other hand, we do have extensive discussions about implementation issues and the “philosophy” behind pay actions.

In sum, complexity is not a major barrier for workforce analysts in the pay equity area. A richer explanation of such issues is found in Stefan Gaertner, Greenfield, G and Levine, B. “Pay Equity: New Pressures, New Challenges,” Human Resource Executive Online. April 12, 2016.

Q:  What is one specific way in which predictive analytics is driving workforce decisions?

A: Pay equity is perhaps the area where we see the most tangible results from our predictive modeling work. First of all, clients don’t ask us to do this work if they are not prepared to act on the results. Organizations understand that you don’t sit on pay disparities if you find them. You have to take reasonable action to remedy bona fide pay inequities once found.

Due diligence is always required in implementing pay actions. No statistical model can alone determine if there are pay disparities, certainly not at an individual level. First of all, there is always the potential for error in the raw data on which such models are estimated. Further, there is statistical error in the estimation of the models themselves. Not all relevant factors influencing pay may be captured in the organizations archival workforce (HRIS) data. And some jobs or career levels may be so thinly populated that it is impossible to make accurate statistical comparisons that account for differences in job or role. At a certain point, judgement comes into play.

Once individual outliers are identified, you need to carefully review them to sort out those cases where there are good technical or business explanations for the pay differences observed and those differences related to gender or race that remain unexplained. The modeling helps narrow the field for such hands-on review, but it does not bypass this need entirely. As in most areas of workforce analytics, science and art come together to render the best solution.

Still, there is no question that the analytics delivered here are hugely impactful. When you do this work, you know you are going to have an immediate effect on the client organization and the employees whose pay is at issue. Doing such consequential work is very satisfying. But it carries a huge responsibility. Because you will deliver point estimates of pay differences that may translate into actual payouts to individuals, you cannot rely on large sample sizes to overcome any data error. Precision in working the data you have is critical. Those who do this work have to be on their toes. Always!

Q: How does business culture need to evolve to realize the full promise of predictive workforce analytics such a pay equity modeling?

A: I think I largely answered this question in my response to the first question above where I reference Mercer’s When Women Thrive study. That study showed that pay equity is basically the tip of the spear in organizations’ efforts to secure gender diversity in their leadership and workforce generally. If you don’t get the pay side right, it is unlikely you’ll be doing well on the representation, promotion, retention, hiring or performance sides either. Rewards are consequential. They signal what is valued in an organization. If you don’t signal you value women, minorities or other groups of interest, you are unlikely to secure them as a vital, engaged, representative and effective part of your workforce. So start with pay equity.

But don’t stop there. If I am clear about anything in our field, it is that effective human capital management requires a systems view. The dynamics process that produces your workforce – we call it your “internal labor market”- consists of multiple moving parts that interact with each other continuously to affect the mix of talent embodied in your workforce. What happens on the reward side influences what happens on the retention side, the development side, the performance side; and vice versa. The best analytics will de-mystify this process, help you understand what drives it and, thereby, help you shape your internal labor market to meet the needs of your business. Workforce diversity and pay equity should be seen in this light. In the end, they are all about the business.

Organizations that do in fact recognize their workforce as an asset need to know what’s happening to that asset and the return they’re getting on that asset. Taking a systems view helps deliver and better process this information. Workforce analytics teams can help foster this view in the way they analyze data and communicate results. This approach enhances the power of their work. It also helps engage leadership in a way traditional HR often failed to do. Such engagement makes all the difference in making the resulting strategies successful.

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Don’t miss Haig’s conference presentation, The Pay Equity Revolution:  How Advanced Analytics are Helping to Close the Gender Pay Gap in Organizations, at PAW Workforce, on Wednesday, May 17, 2017, from 2:15 to 3:00 pm. Click here to register for attendance.  Use Code PATIMES for 15% off current prices (excludes workshops).

By: Greta Roberts, CEO, Talent Analytics, Corp. @gretaroberts and Conference Chair of Predictive Analytics World for Workforce

One thought on “Wise Practitioner – Predictive Workforce Analytics Interview Series: Haig Nalbantian at Mercer

  1. Pingback: It’s the culture, of course | Philip Whiteley's Blog

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