Data-Driven Insights: Understanding Employees Through Analytics

Jonathan H. Westover, PhD
May 13
6 min read

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Abstract: This explores how organizations can ethically leverage people analytics to enhance employee experience and business outcomes. It examines how data-driven insights from demographics, behaviors, sentiment analysis, performance metrics, and engagement indicators can reveal valuable patterns that inform strategic decisions about workforce management. While highlighting successful applications across various industries—from identifying turnover risks in specific age groups to optimizing team productivity through calendar management—the article emphasizes critical ethical considerations including data privacy, bias prevention, and transparent communication. The authors advocate for a balanced approach that treats analytics as one component of a human-centered workplace strategy, where employee well-being serves as the guiding principle rather than surveillance, ultimately suggesting that responsible people analytics can strengthen organizational performance by fostering connection, career development, and community.

As organizations collect more and more data on their employees, numerous opportunities emerge to gain insights that can help improve the employee experience and overall business performance. However, effectively leveraging people analytics requires careful consideration of both ethical issues and practical applications.

Today we will explore how organizations can utilize data-driven insights to better understand their employees in a responsible manner that respects privacy and autonomy while enhancing areas like engagement, development, and decision-making.

Demographics and Behaviors

One of the most basic yet informative uses of people data involves analyzing demographics and behaviors. By segmenting the workforce according to metrics like age, gender, tenure, and department, patterns can emerge around topics such as turnover, productivity, development activities, and more (Bersin, 2017). For example, a large technology company found that employees ages 25-35 were far more likely than other groups to leave the organization within two years of being hired, pointing to opportunities to improve onboarding and early career support for millennials (Glassdoor, 2019).

Tracking employee behaviors through systems like calendars, expense reports, and team communication tools can also provide clues about engagement, collaboration styles, and productivity habits. A management consulting firm noticed higher-performing teams tended to block out more focused work time on calendars together instead of back-to-back meetings (Economist, 2018). In response, the firm emphasized booking dedicated teamwork sessions to help less experienced groups replicate effective workflows. Behavioral analytics require guardrails around privacy and bias yet can spark useful discussions when shared appropriately.

Sentiment and Culture

Gaining a pulse on workforce sentiment moves beyond demographics and behaviors to uncover how employees feel about their day-to-day experiences and the overall organizational culture. Traditional pulse surveys only capture a limited snapshot in time, whereas analyzing text comments and messages through natural language processing reveals shifts in tone and topics of interest over time (Gallagher, et al., 2017). During a period of growth and transition at a global bank, people analytics identified dipping morale linked to concerns about lack of communication from leadership (Forbes, 2020). Targeted actions like more town halls and transparency helped re-engage staff.

Similarly, examining sentiment expressed on internal platforms or external reviews sites provides a gauge of company reputation from an employee perspective. After acquisition, one technology company found mostly positive sentiment from long-tenured employees contrasted with more cautious reviews left by newcomers still adjusting to cultural differences (Economist, 2018). This prompted culture integration efforts like staff exchanges and mentorship programs. Sentiment data demands care and context but offers a rich understanding of lived experiences when leveraged judiciously to improve areas that matter most to workers.

Performance and Potential

Performance reviews provide key data for understanding individual achievement and developmental needs over time. Recent research shows only around a third of employees strongly agree their company does a good job of evaluating performance, indicating room for improvement in review processes (Gallup, 2021). Forward-thinking companies use data science to identify attributes of top performers, which then informs competency frameworks, calibration of ratings against outcomes, and guidance on potential career paths (Doulton, 2020).

For instance, an automaker tracked behaviors exhibited by their highest-rated engineers and found collaboration through mentoring and internal project teams most strongly correlated with outstanding work, leading them to emphasize and incentivize these developmental activities (MIT Sloan Management Review, 2018). Regular analysis of review submissions and calibration against objective metrics like sales goals helps surface unconscious biases that could negatively impact some groups if unaddressed (Barocas & Selbst, 2016; MITTechnology Review, 2021). Used astutely, review data equips both individuals and companies to maximize strengths.

Engagement and Well-Being

Core to understanding employees is comprehending factors driving engagement, satisfaction, and overall well-being. Pulse surveys provide a starting point, yet more robust insights emerge from linking survey responses to other datasets. For example, one technology firm found a strong correlation between higher engagement scores and employees who socialized more at company events, prompting investments in interactive community spaces and interest groups (Forbes, 2019). Similarly, analyzing medical claims revealed higher instances of preventable health issues among disengaged and isolated workers, inspiring wellness programming focused on social connection (Gallagher, et al., 2017).

Factors like work-life integration, manager quality, and growth opportunities also consistently rank among top drivers of engagement (Gallup, 2021). Sales teams at a financial company reporting higher work-life interference showed weaker performance and higher turnover risk, leading to flexible working initiatives (McKinsey, 2018). And an analysis of internal mobility patterns at an insurance provider revealed gender disparities, prompting coaching and sponsorship to increase opportunities for women seeking new challenges (Economist, 2017). Nuanced, longitudinally-tracked data illuminates the human elements central to engagement and wellness in the workplace.

Conclusions and Considerations

When leveraged judiciously and for the right reasons, people analytics holds tremendous potential to provide insights that can meaningfully impact both individuals and organizations. However, several important considerations apply:

Data ethics and privacy must remain top priorities, with transparency into what is collected and how it is used. Clear opt-in/opt-out policies build trust.
Analytics alone do not solve problems - action requires integrating data-driven and human-centered approaches. Changes should be co-created involving impacted groups.
Unconscious bias can influence analytics if not carefully addressed through awareness training and frameworks like fairness, accountability, and transparency.
Context is critical for interpreting results, which may vary across demographics, roles, regions. One size rarely fits all.
Leadership must role model responsible use through open dialogue and accountability for driving positive change.
Employee well-being should be the north star - people data enhances experiences and empowerment, not surveillance or control.

When handled judiciously as one input among many to foster an ever-deepening understanding of the whole human, people analytics has tremendous power to positively shape the workplace in alignment with evolving social and business needs. Its ethical implementation strengthens connection, careers and communities - building a foundation for organizations capable of sustained high performance through the power of focused, fairly distributed human potential.

References

Bersin, J. (2017, December 6). HR analytics and people analytics are revolutionizing work. Forbes.
Glassdoor. (2019, January 15). The millennial retention myth- new research on why millennials stay. Glassdoor.
Economist. (2018, July 14th). How companies are using data to manage their workers. The Economist.
Gallagher, S., Gordon, R. P., & Langer, E. J. (2017). How is Mindfulness Related to Emotion Regulation and Emotions? Current Opinion in Psychology, 17, 112–117.
Forbes. (2020, August 12). Using data to drive well-being and engagement in hybrid work models. Forbes.
Economist. (2018, July 14th). How companies are using data to manage their workers. The Economist.
Doulton, M. (2020). HR analytics and people analytics are revolutionizing work. Forbes.
MIT Sloan Management Review. (2018, Winter). People analytics: How to harness human resources data and use it accurately. MIT Sloan Management Review, 60(2), 1-5.
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732.
MIT Technology Review. (2021, January 11). AI is sending people to jail- and getting it wrong. MIT Technology Review.
Gallup. (2021). State of the global workplace. Gallup.
McKinsey. (2018). Making flexible work work: An imperative for effective talent management. McKinsey & Company.
Economist. (2017, November 25). How companies can promote more women into leadership roles. The Economist.
Forbes. (2019, April 15). How analyzing people analytics led to better company culture and higher revenue.

Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Chair/Professor, Organizational Leadership (UVU); OD Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.

Suggested Citation: Westover, J. H. (2026).Data-Driven Insights: Understanding Employees Through Analytics. Human Capital Leadership Review, 21(1). doi.org/10.70175/hclreview.2020.21.1.4