Workforce Analytics: The Art and Science of Navigating People Problems
Workforce analytics is often perceived as an extension of traditional data analytics—a science aimed at solving problems with precision, generating reliable outcomes, and delivering measurable ROI. Yet, this perception overlooks a fundamental truth: HR issues are inherently different from the typical case examples found in data analytics. While some HR problems are predictable, many aspects are not, which makes workforce analytics a blend of art and science.
The Unique Challenges of Workforce Analytics
Typical data analytics case studies revolve around predictable problems—those that, once solved, produce consistent and replicable results. For example, optimizing supply chain logistics or improving manufacturing efficiency generates clear and reliable outcomes. These problems are measurable and tied directly to financial returns.
HR issues, on the other hand, often operate in a less predictable space. While some challenges—such as automating payroll or conducting salary benchmarking—are straightforward, many HR problems, like improving employee engagement or reducing turnover, are far more nuanced. These issues are influenced by diverse human behaviours, individual contexts, and cultural dynamics, making their outcomes harder to forecast.
The Overlooked Step: Problem Classification
One of the most critical yet underappreciated steps in workforce analytics is problem classification. Understanding the nature of the problem—whether it is predictable or unpredictable—can help HR professionals:
Prioritize limited resources
Set realistic expectations
Develop tailored approaches to problem-solving
The Cynefin framework, a decision-making tool, is particularly helpful here. It classifies problems into domains of predictability and unpredictability, allowing HR to better navigate the complexities of workforce challenges.
Breaking Down Complex Problems with Strategic Workforce Planning (SWP)
Strategic workforce planning (SWP) is a prime example that illustrates the value of problem classification. As most HR professionals know, SWP is a complex process with many moving parts. Taken as a whole, it might be classified as an unpredictable problem, making it appear daunting and insurmountable.
Aspect | Predictable Problems | Unpredictable Problems |
Definition | Problems with clear, reliable solutions and measurable outcomes. | Problems influenced by human behaviour, requiring flexible and iterative approaches. |
Examples | Automating payroll, salary benchmarking, headcount projections. | Employee engagement, reducing turnover, enhancing workplace culture. |
Approach | Use data to produce specific, actionable answers. | Use data to explore hypotheses and guide experiments. |
Outcome Expectation | Reliable, repeatable results. | Probabilistic results requiring human interpretation. |
Role of Data | Provides definitive answers and clear actions. | Supports hypothesis testing and iterative adjustments. |
Role of Human Judgement | Minimal once the problem is solved. | Essential for interpreting results and guiding actions. |
However, when the individual steps of SWP are classified using the Cynefin framework, the problem becomes more manageable. For example:
Simple Problem: Gathering headcount and productivity data is straightforward, as the data should be readily available within the organization.
Complicated Problem: Building a workforce benchmarking and projection model is a more complex task but one that has a predictable numerical solution.
Unpredictable Problem: Executing the SWP plan often falls into the unpredictable category. For instance, if the plan requires upskilling or reskilling employees for new work processes, there are many potential obstacles. While organizations can mandate training, they cannot guarantee that employees will learn or apply new skills effectively.
By breaking down SWP into these distinct components, HR professionals can tackle it systematically. Addressing the simpler and more predictable aspects first builds a foundation, while the more unpredictable elements can be approached with iterative experiments and adjustments.
The Role of Data in People Problems
Data remains a critical tool in addressing HR challenges, but its role varies depending on the predictability of the problem.
In the Predictable Space
For predictable problems, data analytics can deliver clear, actionable answers. For example, projecting the number of teachers needed in the next five years based on student enrollment trends is a straightforward application of data. The outcomes are reliable and can be used to drive precise actions.
In the Unpredictable Space
Unpredictable people problems, however, require a different approach. Here, data serves as a tool for exploration rather than a source of definitive answers. For example, improving employee engagement involves testing various hypotheses to determine what might work. The outcomes are probabilistic and require iterative experimentation.
In this context, data insights guide HR in running small-scale experiments to probe potential solutions. Human judgment, informed by data, becomes essential. It’s not about finding the “right” answer but identifying a course of action that’s likely to have a positive impact and adjusting based on what works in practice.
Managing Expectations in Workforce Analytics
One of the key reasons organizations sometimes feel disappointed with workforce analytics is the assumption that all people issues are predictable. This misalignment of expectations leads to frustration when data-driven solutions don’t deliver immediate or definitive outcomes.
By building capabilities to classify people problems and setting clear expectations about the approach and timeline for outcomes, organizations can significantly enhance their workforce analytics efforts. Understanding that predictable problems yield clear answers while unpredictable problems require experimentation and adaptation makes all the difference.
Conclusion
Workforce analytics is not just about crunching numbers; it’s about understanding the art of working with people. While data analytics provides powerful tools, the human element remains critical. By recognizing the unique challenges of HR issues, leveraging frameworks like Cynefin for problem classification, and adopting a tailored approach to data use, organizations can unlock the true potential of workforce analytics.
Ultimately, workforce analytics is a journey of continuous learning, where the art of managing people and the science of data work hand in hand to drive better outcomes.
💡 Interested in learning how to apply workforce analytics in your organization? FYT Consulting offers expert-led workshops to help HR professionals classify problems, set realistic expectations, and use data effectively to tackle complex challenges.
📩 Contact us at FYT Consulting to find out how we can help you unlock the full potential of your workforce analytics initiatives.
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