Picture this: A senior leadership team is reviewing store results and, by all accounts, two particular store profiles look roughly the same. Customer demographics: similar. Market conditions: similar. Yet, one location is seeing major issues with revenue growth, customer retention and quality control. One executive talks about local advertising challenges. Another mentions supply chain and distribution delays. The third talks about turnover at the location creating continuity issues. Who’s right?
In this scenario, further analysis of workforce data revealed that there were indeed major differences in workforce turnover. Additionally, key sources of the difference related to overall market employment levels and declining availability of target talent for front-line customer-facing roles. This added pressure made seemingly similar markets from a business perspective perform quite differently from a talent perspective.
While this example is telling, it is not practical nor effective to manage these questions in this reactive way. Rather, we are seeing organizations building internal teams that are adept at managing and delivering on workforce insights in much the same way that we address customer analytics. That said, we’ve also observed some common challenges as organizations work to accelerate their understanding of these key workforce pain-points and opportunities, which highlight the need for:
• Thoughtful strategy and sequencing: It might seem obvious, but more often than we’d expect, we see organizations try to tackle too many questions early in their journey, which can slow down, or even derail, their workforce analytics efforts. A simple “stress test” of priorities is to ask: Would this get a leader’s attention either to support their ambition (to drive their success) or fear (to get in the way of their success)? If the analytic questions that you are answering not only satisfies one (or both) of these criteria and can be analyzed based on readily available data and easily explained methods, organizations are significantly more likely to get leadership support.
"The goal is to PASS the investigative ball down the field to make progress in the arena of workforce analytics"
• Dedicated staffing: We see many organizations move people from other parts of operations into workforce analytic roles as “stretch assignments.” While there is great potential in this approach, careful consideration needs to be given for this model to be effective, including core analytics experience, HR understanding, leadership support, influencing skills and, most of all, time. If this work is defined as a task in a broader role, progress tends to be marginal at best. This outcome isn’t a reflection of the individual but rather the organizational commitment overall. It should be noted here too that some organizations have great aspirations for analytics—that “everyone should be an analytics expert.” Albeit admirable, if you looked at many, if not most, of the job descriptions in these organizations, you may not see the term analytics even present, much less prioritized in the recruiting process. Often, centralized analytics teams are more effective because they typically have the critical mass in terms of skills, data access and leadership focus to gain needed momentum.
• Actionable analytics: Analysis paralysis is a phenomenon that is so common it has a name. In the area of workforce analytics, we often see organizations that want to bring together several data sources and studies—not to mention an insatiable desire for external benchmarks—to inform key talent-related questions. When talking to these organizations, we sometimes learn that they struggle to provide even more basic insights related to talent on a consistent and well-understood basis—what is the size and shape of our workforce? What is our turnover? How do people progress in their careers here? In these situations, we need to start where our end-audience is and provide more foundational research with fewer pieces of information to get them to trust our data and methodology before scaling to more complex analytics. On the positive side here, if these simpler methods have not been deployed in the past, there is often low-hanging fruit to be gained through analysis of the existing variance in workforce practice to add value near term.
• Process and governance: We all know the importance of process and governance to the successful implementation of a new technology and the management of proprietary data. In the field of workforce analytics, many experts are in these roles because they thrive on mining through information to find the nugget of gold. While this process may be lucrative if successful, there can be more failures than successes. Consciously managing what and how data are analyzed is a constant balance of art and science, which requires strong project and time management skills—that are often not well evaluated when considering candidates for these roles.
• Technology enablement: It is daunting enough to think about changing HRMS in an organization, much less how we manage all types of HR data from core workforce demographics to learning and development, performance management, succession planning, and the like in an integrated way. Some proffer the ERP as the solution to comprehensive data management. Certainly, it is appealing to consolidate data with a single vendor for both input and output purposes. The challenge that we typically face relates to a common dilemma in technology—the bigger the system’s purview, the more likely that auxiliary modules will lag and cost more than market leaders. This trade-off has important consequences on what can be accomplished in the analytics arena and can lead to other home-grown “solutions” to work around the core issue of system capability, which is fraught with other issues. Equally likely these days is to see organizations adopt a Business Intelligence (BI) platform to support data management across functional disciplines. This model has seen efficacy in other areas of business management but has been shown to create some unique challenges in the area of HR, where retroactive events are common and may even be legislatively required, not to mention the core calculation logic that may not be easily replicated in these data management engines. Today, there are systems that are designed to address these unique needs and organizations should ensure that their technology decisions are based on their underlying requirements and known trade-offs.
• Change management and education: In about one-third of the cases where we are brought into organizations because something “is wrong” with their analytics, we find that the core mechanics are sound but the challenge is either in the method selected itself and/or the way it is delivered to its targeted audience. Not only do we face the typical change management challenges of potentially bringing information to light that may be counter-intuitive or “counter-desirable,” we are asking for leaders to accept evidence about human behavior that, while generally accepted in customer marketing, is still newer territory in the area of HR.
Progress can and should be made in the area of workforce analytics, when these general guidelines are considered. If all of the above success factors seem too daunting, we will leave you with this short framework to follow the 80/20 principles for success:
• Have a (P)oint of view: A common phrase in analytics is to “see what the data tells us.” We recommend the opposite, if you want to make quick progress—have an opinion of what’s important and focus analytic efforts to prove or disprove hypotheses that already exist in the organization.
• Focus on (A)ction: While it may seem obvious, organizations don’t always focus on questions for which immediate action can be taken and change can be achieved.
• (S)tart somewhere: We’ve seen organizations spend months trying to set an agenda and review available data sources, etc. Taking the litmus test of bigger-bet priorities for business leaders, where directionally reliable data are available, should provide a short list of immediate opportunities to get started.
• Embed (S)torytelling: Don’t expect an audience to “get it,” as they didn’t spendall the time and effort that the analyst did to put the findings together. We need to lead our target audience through the story line to gain trust in the data, prove or disprove their initial hypotheses and show the evidence-based story line that drives a better workforce, and ideally business, outcome.
The goal is to PASS the investigative ball down the field to make progress in the arena of workforce analytics!