Self-Discovery: Ethical People Analytics Skills
How to use this table:
- Read the description of each skill and make your best estimate of your current level of ability based on the following scale:
- Aware: You are brand new to this skill. You may know about it conceptually, but have not yet completed the task yourself.
- Learner: You’re just to learn this skill or still rely on detailed instructions and guides to complete this task.
- Skilled: You have some ability in this area, but you still need assistance to tackle certain parts or advanced aspects.
- Expert: You confidently handle challenges in this topic area, do not need support, and can clearly and simply explain concepts to others.
- Strategist: You are applying this technique in new ways and creating new ways of doing this, you can mentor/guide intermediates and advanced individuals in this skill.
- Optional: Determine if there are any skills for which you would like to have a different future skill level and indicate your desired level. Keep in mind, that your desired skill levels may change over time and may change as you consider different possible career options. You will likely come back to this table and change your desired levels many times.
Note. Don’t worry too much about where you rate yourself, there’s no true score or measurement for something like this. The scale provided is only meant to provide a general sense of guidance and to serve as a thought-provoking exercise.
Ethical People Analytics Skills
| Skill | Description | Current Level (Aware/Learner/ Skilled/Expert/Strategist) | Desired Level (Aware/Learner/ Skilled/Expert/Strategist) |
| Ethical Framework Development | Develops and applies a personal ethical framework to guide decision-making in people analytics. Defines core values related to data privacy, fairness, transparency, and accountability, and uses these values to navigate complex ethical dilemmas. | ||
| Impact Assessment | Critically evaluates the potential consequences of people analytics work. Considers both intended and unintended consequences, and proactively addresses potential negative impacts. | ||
| Transparency and Communication | Communicates clearly and honestly about data collection practices, data usage, analytical methods, and the limitations of people analytics findings. Operates openly about how data is being used and ensures that stakeholders understand the information being presented. | ||
| Data Privacy | Understands and implements practices to protect employee data from unauthorized access, use, or disclosure. Applies knowledge of relevant regulations (e.g., GDPR, CCPA, AI act), ethical considerations, and best practices for data handling, storage, anonymization, and security. | ||
| Informed Consent | Obtains explicit and informed permission from employees before collecting or using their data. Clearly communicates about data usage, ensures individuals understand what they are consenting to, and provides easy mechanisms for withdrawing consent. | ||
| Legitimate Use Case Identification | Defines and justifies clear, valid, and ethical reasons for collecting and using employee data. Aligns data use with business needs, considers the impact on employees, and documents the rationale behind data activities. | ||
| Ethical Data Collection | Collects data responsibly and ethically, considering the potential impact on employees. Minimizes data collection to only what is necessary, operates transparently about data collection practices, and ensures data is stored securely. | ||
| Ethical Data Analysis and Interpretation | Analyzes data ethically, using appropriate statistical methods. Recognizes and addresses potential biases in data and algorithms, interprets data responsibly, avoids “data dredging,” and protects individual privacy during analysis. | ||
| Ethical Reporting and Sharing | Communicates people analytics findings accurately, transparently, and ethically. Avoids sensationalism or exaggeration, clarifies limitations, shares findings only with those who have a legitimate need to know, protects individual privacy in reports, and presents data clearly and understandably. | ||
| Research Ethics | Designs and conducts people analytics research ethically, minimizing risks to participants. Ensures confidentiality and maintains objectivity. | ||
| Bias Mitigation | Identifies and addresses potential biases in data, algorithms, technology, and decision-making. Applies critical thinking, data analysis skills, knowledge of algorithmic bias, and an understanding of how human biases can influence outcomes. |
Bonus Self-Discovery
Identify Your Core Values: What principles are most important to you? Think beyond the workplace and consider your personal values. Honesty, integrity, fairness, compassion, responsibility, respect, and transparency are common starting points. Which of these resonates most strongly with you? Are there others you would add?
Identify Your Biases: We all possess biases, both conscious and unconscious, that can significantly influence our perceptions and judgments. Bias is simply a tendency to feel or be inclined toward or against something. As humans, we process the world through approximately 200 different biases. Not all of these biases are bad, many of them help us be effective, safe, and smart people. But some can lead to harm. And they can creep into our work in people analytics, negatively affecting everything from data collection and analysis to interpretation and reporting. It’s crucial to acknowledge this reality and actively work to identify your own biases. Understanding your tendencies is the first step toward mitigating their impact. So you will want to consider what biases you may have related to demographics (race, gender, age), performance (halo effect, confirmation bias), and other factors relevant to people analytics (e.g., affinity bias, anchoring bias).
Begin by educating yourself on the various types of cognitive biases. The Decision Lab (thedecisionlab.com/biases) provides a comprehensive list of definitions. Take note of any biases that you feel could impact your work. Some common biases that tend to derail people analytics include confirmation bias (favoring information that confirms existing beliefs), self-serving bias (attributing successes to oneself and failures to external factors), anchoring bias (over-reliance on the first piece of information received), and selection bias (drawing conclusions from a non-representative sample). Equally important is becoming aware of any biases you might hold toward specific groups of people. A quick and insightful way to explore these is by completing various Implicit Association Tests (IAT) through Project Implicit (implicit.harvard.edu). Reflect on the results and consider how these unconscious biases might subtly influence your work in people analytics. This self-awareness is essential for ethical practice.
After identifying the biases that may require closer attention, document the specific actions and efforts you can implement to mitigate their influence. This might include strategies for recognizing biases before they affect your decisions, identifying them when they do slip through, and minimizing their impact in the future. Developing concrete mitigation plans will help you translate self-awareness into ethical action.