Workforce analytics has come a long way from its origins as a discipline practiced by a small group of highly-educated theoreticians. Today, almost—if not—all larger organizations are working to build or enhance their ability to analyze, report, and (most important) act on workforce insights. This activity has also piqued the interest of many mid-sized organizations, although there are unique challenges in establishing this discipline in these organizations, as they often don’t have the same scale of financial or human resources to support these efforts as readily as their larger brethren.

In fact, there is so much talk about workforce analytics in the market today that some have decided that any type of data dissemination or reporting constitutes workforce analysis. While there may be a case to be made for this interpretation, workforce analytics is intended to deliver incrementally more than consolidated data and tracking metrics.

Workforce analytics should go beyond telling us what a number is and how it compares over time or across segments. While this work certainly has value (or “merit”, some might say), analysis in the mathematical definition under Webster’s dictionary is: “proof of a mathematical proposition by assuming the result and deducing a valid statement by a series of reversible steps.” Analysis is and should be answering the question of why we find something to be true or not true—as in, a result.

Given the potential debate about what constitutes analytics, it is interesting that, while there is so much consternation about the naming convention of teams within organizations focused on this topic, most of the deliberation is on the first term for this area of analytic focus. Is it:

• People
• Talent
• Workforce
• HR
• Labor
• Manpower (still commonly used in select markets around the world)
• Humankind (yes, we’ve seen it!)

It is almost a fait accompli as to the second term: analytics. Yet, if you were to look at the activities and investment of time undertaken by many teams today, one might observe that significant—if not the majority of—time is actually spent in areas that would not qualify under this mathematical definition. When adding up the amount of time spent on database development, metrics and report generation, meeting activities and socialization, what percentage of time is actually left for true analysis? If you had a sales team spending less than 50% of their time on direct selling activities, would leaders be satisfied? Yet, for many workforce analytics (we’ll use this term for simplicity here) functions today, reaching a 50% ratio of direct analytical activity would be considered elusive.

Given the frenzy of interest in this topic and the inherent challenges of the work tasked to these groups, we wanted to explore ways to separate those who are talking about workforce analytics from those who are making meaningful progress in advancing an evidence-based approach to HR within their organizations. For example, the hype of workforce analytics talks about progression of capability from basic reporting through predictive and “prescriptive” statistics (we can discuss this distinction in another post). The substance of workforce analytics relates to evaluating "why" something delivers value to the organization. While the methodology is certainly foundational, success requires diligent and deliberate attention to the end audience and how to engage them with meaningful, targeted evidence that proves or disproves a result. Sometimes simpler methods can accomplish more, if managed effectively and consistently with business needs and readiness. That said, there are certain hallmarks that are typically observed in more mature workforce analytics functions--those that have substance have:

• Clearly defined objectives and guiding principles for what the team will accomplish, which is focused on deductive analysis
• Analytic priorities that support business performance through evidence-based understanding of what drives HR or people results
• Active use of multiple analytic methods "fit for purpose" from descriptive to predictive statistics
• Established software to support ongoing and ad hoc analytics, spanning multiple methods
• Integrated database with global historical data, covering all dimensions of the candidate and employee lifecycle with built-in quality controls (ideally, these data are likewise integrated with business and operational data)
• A meaningful percentage of dedicated resources to HR analytics (separate and apart from data management and reporting), although the team may or may not reside in HR
• Assigned resources to address change management and education relating to workforce analytics and driving actionable results
• Bifurcated HR reporting and analytics teams with consistently defined protocols for data entry, management, sharing and interpretation

In essence, the goal of these groups is to provide Measurable insights that focus on Effectively and Realistically engaging leaders and Integrating with their efforts to identify Targeted actions based on evidence to drive better results. While we covered some of the more common signs to distinguish hype from substance here, we welcome your perspective on what you’ve seen in the marketplace, so please feel free to share your thoughts with us at info@meritanalyticsgroup.com .