What are some pitfalls to avoid when using predictive analytics in HR?

1 Data quality

One of the most critical factors for predictive analytics is the quality of the data that is used to train and test the models. Poor data quality can lead to inaccurate, biased, or misleading results. Therefore, it is important to ensure that the data is relevant, reliable, representative, and complete. You should also check for any errors, outliers, or inconsistencies in the data and address them before running the analysis. Additionally, you should be aware of the limitations and assumptions of the data and how they may affect the predictions.

2 Ethical and legal issues

Another pitfall to avoid when using predictive analytics in HR is the ethical and legal implications of the results. Predictive analytics can potentially create or reinforce discrimination, privacy, or fairness issues, especially if the models are based on sensitive or protected attributes such as race, gender, age, or disability. You should be careful to comply with the relevant laws and regulations regarding data protection, consent, and anti-discrimination. You should also consider the impact of the predictions on the employees and candidates and how they may perceive or challenge them. Moreover, you should be transparent and accountable for the methods and outcomes of the predictive analytics and explain them clearly and respectfully.

3 Model validation

A third pitfall to avoid when using predictive analytics in HR is the lack of model validation. Model validation is the process of evaluating how well the predictive model performs on new or unseen data and how generalizable and robust it is. Without proper model validation, you may end up with overfitting, underfitting, or spurious correlations that compromise the accuracy and reliability of the predictions. Therefore, you should use appropriate methods and metrics to validate the model, such as cross-validation, hold-out testing, or error analysis. You should also compare the model with alternative or baseline models and assess its added value and significance.

4 Human judgment

A fourth pitfall to avoid when using predictive analytics in HR is the disregard of human judgment. Predictive analytics is not a substitute for human judgment, but a tool to support and enhance it. Predictive analytics can provide insights and recommendations, but it cannot capture the full complexity and context of human behavior and decisions. Therefore, you should not rely solely on the predictions, but also use your own critical thinking, intuition, and experience to evaluate and interpret them. You should also involve the relevant stakeholders and experts in the process and solicit their feedback and input.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?