The technological advances in data analysis are presenting new opportunities for HR professionals to understand what causes what and to project what might happen in the future based on what has happened and is happening. But taking advantage of what technology offers is sometimes easier said than done. Most HR practitioners lack PhDs in quantitative methods. There is a limit to the number of courses in any curriculum leading to a BA, and even an MS, in a field related to HR and there is a lot to learn about the principles underlying sound workforce management, so it is unlikely HR practitioners are going to know enough about data science to take full advantage of what technology offers. Conversely, data scientists tend to focus their formal education as well, with limited exposure to behavioral science topics. So there generally is a limited amount of overlap in the knowledge possessed by HR professionals and data scientists.
One of the requirements for people from different disciplines to be able to work together is that they share language and knowledge. If one party speaks Chinese and the other English, there are tools for easy language translation available today. It is much more difficult to translate the technical language data scientists speak into a form that HR professionals speak. This has been an issue for a very long time when academics do research and practitioners attempt to understand and apply research findings. The scientific method is rigid in its requirements and compromising on those just to make research results accessible to practitioners is a breach of the rules (but there is no rule against trying to translate findings at least partially). One of the potential solutions to the lack of common ground is to have people with some knowledge of both fields do a translation of what research has found in the pop literature, where practitioners tend to get their information. But the translation often results in authors “cherry picking” research findings that support the point an author is trying to make, consciously or not. In some cases, the translator has a surface level of competence in interpreting research, and this can result in attempting to generalize findings beyond where they would apply.
A popular book attempted to support the claim that extrinsic rewards diminish the ability to experience intrinsic rewards. “Evidence” included a controlled lab study that found that people threw tennis balls at targets longer if they did not receive the very small rewards on offer. Even though the study seemed to meet the “internal validity” requirements for a valid study (it was well designed to determine what would happen under those specific conditions), it attempted to apply the lab results to employees working for many years, often doing things viewed as undesirable, to support themselves. The “external validity” requirements for a study to be generalized to a different context were definitely not met. To make matters worse numerous field studies done in contexts similar to real world contexts (thereby meeting the external validity test) have refuted the lab study findings. The bottom line is that whoever uses research needs to have the ability to determine if it applies to the situation being evaluated. And not knowing that research needs to meet both validity tests can mislead someone attempting to apply research findings where they do not apply. The growing adoption of evidence-based management in decision-making is a positive trend. But the evidence used must be sound and must apply to the matter at hand. Workforce analytics can be of enormous value to an HR practitioner. But there are two requirements. First, the analysis must be done in a manner consistent with the principles of the scientific method. Second, lab tests must meet the external validity test just discussed if their results are to be applied to the field.
One of the most common applications of workforce analytics is the attempt to predict who might be prone to voluntarily leaving the organization. Top performers in critical occupations will always be in short supply and will be both expensive and difficult to replace. So being aware of issues that may lead to turnover before they reach a critical level can enable the practitioner to adopt preventative strategies. When someone resigns they have often left (mentally) already and recovery strategies are much more difficult to pull off than preventative measures. But how does an HR practitioner get inside someone’s head and discover if they are edging towards the exit? By using data and finding causal relationships between factors leading to termination steps can be taken to focus on what might be most effective to address. Employee satisfaction certainly will influence whether someone is receptive to a call from a recruiter or an internet posting of an opportunity. So will employee engagement. And using technology enables organizations to get a reading on where their employees are on the satisfaction and engagement scales. Some models for predicting potential turnover also include characteristics of who have left in the past, either in the organization being studied or in other similar organizations (i.e., length of service, age, etc.). The use of big data, machine learning, and AI can be used to create prediction models.
Data scientists are apt to believe these models are strong predictors of what will happen. They create algorithms and apply them. But two potential limitations are often ignored:
- Data is from the past and the present and if the future is going to be different the predictive power may be diminished
- Data scientists are working with numbers, without consideration of human factors that may be better predictors.
If an employee is subjected to real or perceived mistreatment by a manager on Monday, a notice of departure may be tendered that week, even though that employee had not possessed the “prone to leave” factors, at least until that event. There are other signs of trouble brewing that may have been visible to an HR professional but not included in the prediction model. Behavior, events in an employee’s personal life, feedback from peers, subordinates, and managers… all of these indicators of an employee’s mindset may be recognizable by the HR professional. So in order for predictions to be as good as they can be it is prudent to augment the data analytics with the human stuff, either by building factors into the predictive model or by an interpretation of quantitative results using qualitative measures.
Recently I was asked whether a client organization should assign a data scientist to HR. I am in favor of collaboration between people from the two disciplines and believe that credible evidence can come in the form of “hard” evidence (numbers) and “soft” evidence (professional knowledge and judgment). But co-locating people with different perspectives does not necessarily result in the pooling of knowledge and consideration of both points of view. So should HR professionals add “speaks analytics” to their competency model? Yes, to some degree. Should data scientists add “respects the impact of human behavior” to their competency model? Yes, to some degree. Who has to walk the furthest out on the bridge between them to achieve mutual understanding will depend on the issues being dealt with and the parties involved. As a faculty member for DePaul U.’s MSHR program, I try to ensure students understand the need to respect both the quantitative and qualitative perspectives Although I do not know I hope the faculty developing data scientists are equally respectful of the “other way.” If nothing else, balanced approaches lessen the fear of being replaced by robots.