How People Analytics Can Help You Change Process Culture

572019 How People Analytics Can Help You Change Process Culture An

Transformational change within organizations, especially in the digital age, often involves complex, integrated initiatives that depend heavily on the involvement and behavior of people. As companies strive to adapt to technological advancements and market disruptions, understanding and leveraging the power of people analytics can play a crucial role in guiding successful transformation efforts. People analytics, defined as the use of data about human behavior, relationships, and traits to inform business decisions, offers a data-driven approach to understanding organizational dynamics and facilitating change at various levels.

This paper explores how people analytics can be used to support process, cultural, and strategic transformation within organizations. Drawing on examples and insights from companies engaging with Microsoft’s Workplace Analytics division, the discussion emphasizes the value of data in identifying operational efficiencies, fostering cultural change, and enabling strategic agility.

Introduction: The Role of People Analytics in Organizational Transformation

In today’s fast-paced and competitive environment, organizations face a multitude of challenges that necessitate transformation. Whether it is digitizing processes, evolving corporate culture, or repositioning strategy, success hinges on effectively managing people, as they are the most complex and dynamic component of any organization (Bersin, 2017). Traditional top-down approaches often fall short due to their limited capacity to capture ground-level insights and foster employee engagement. Consequently, organizations are increasingly turning to people analytics to inform decision-making, monitor change progress, and personalize interventions.

Using People Analytics to Drive Process Transformation

One of the primary applications of people analytics is optimizing core business processes. As enterprises digitize and automate, the data collected about employee activities and network interactions can uncover efficiencies and best practices that are not apparent through conventional methods.

For example, a global consumer packaged goods (CPG) company employed people analytics to analyze the efficiency of its financial processes across multiple countries. By basing insights on data rather than anecdotal evidence, the team discovered that one country outperformed others by 16%, completing the same tasks with significantly fewer resources. Specifically, this country required 71 fewer person-hours monthly and involved 40 fewer individuals, which highlighted their operational excellence. These insights prompted the transformation office to engage local finance leaders as partners in broader process improvements, fostering a collaborative, bottom-up approach (Nielsen & McCullough, 2018).

This example underscores the importance of employing data to identify 'bright spots' within complex organizational systems. Top-down initiatives often overlook these grassroots efficiencies. The role of people analytics is thus instrumental in democratizing knowledge, promoting transparency, and replicating best practices across divisions.

Fostering Cultural Change through Data Storytelling

Beyond optimizing processes, people analytics is vital in shaping organizational culture, particularly when cultural transformation is a priority. Culture change often involves altering deeply embedded behaviors and mindsets, which can be challenging without concrete evidence to spark dialogue and reflection. Data-driven storytelling can effectively bridge this gap by translating raw data into compelling narratives that influence attitudes and behaviors.

A practical illustration involved an engineering company's efforts to develop managerial capabilities aligned with inclusivity. Analysis revealed that managers who engaged in at least 16 minutes of one-on-one meetings per week with direct reports boasted 30% higher engagement scores, compared to those spending only nine minutes. Presenting this data as a simple benchmark helped managers recognize their role in fostering an inclusive environment, transforming abstract ideals into achievable behavioral goals (Nielsen & McCullough, 2018).

By making behavioral science tangible and relatable, data storytelling can shift conversations from passive acknowledgment to active engagement. Such approaches facilitate cultural shifts by making behaviors measurable and feedback tangible.

Supporting Strategic Transformation and Managing Change Fatigue

Strategic initiatives driven by external market forces require careful execution to ensure sustainability. People analytics can provide real-time dashboards to monitor workforce capacity, resource utilization, and stress signals such as burnout or change fatigue. This proactive monitoring enables leadership to adapt strategies, allocate resources, and manage change effectively.

An example involves a financial services firm where a dashboard tracked employee activity levels across teams, revealing insights about over-utilized or under-engaged groups. Leaders could then tailor interventions, such as adjusting workload or onboarding support, to prevent burnout and sustain performance (Nielsen & McCullough, 2018). Such data-driven insights enhance agility, enabling organizations to respond swiftly to emerging challenges and maintain momentum during transformation processes.

Challenges and Best Practices in Leveraging People Analytics

Despite its potential, implementing people analytics is not without challenges. Data privacy concerns, cultural resistance, and technical complexities can hinder progress. It is essential to adopt ethical data practices, ensure stakeholder buy-in, and develop organizational capabilities in data literacy. Furthermore, integrating qualitative insights with quantitative data enhances contextual understanding and more effectively informs change initiatives (Levenson, 2018).

Successful organizations embed analytics into their culture by fostering transparency, establishing clear objectives, and encouraging experimentation. Starting with pilot projects that demonstrate tangible benefits can build confidence and momentum for broader adoption. Additionally, involving employees in data collection and interpretation fosters trust and ownership (Davenport et al., 2020).

Conclusion: The Future of People Analytics in Transformation

As organizations navigate a landscape marked by rapid technological change and evolving consumer expectations, people analytics emerges as a critical tool in managing transformation. By providing data-driven insights into processes, behaviors, and culture, it enables organizations to foster agility, engagement, and continuous improvement. The integration of analytical techniques with human-centered practices promises to unlock new levels of organizational performance and resilience. Ultimately, the successful application of people analytics hinges on a strategic commitment to data quality, ethical practices, and cultivating a data-literate culture that values evidence-based decision-making.

References

• Bersin, J. (2017). The Rise of People Analytics. Deloitte Review, 21, 84–91.
• Davenport, T. H., Harris, J., & Shapiro, J. (2020). Competing on Talent Analytics. Harvard Business Review.
• Levenson, A. (2018). Using Data to Drive HR Decisions. People + Strategy, 41(3), 56–59.
• Nielsen, C., & McCullough, N. (2018). How People Analytics Can Help You Change Process, Culture, and Strategy. Harvard Business Review.
• Patel, P. C., & Coughlan, A. T. (2020). Information-driven HR: An Organizational Perspective. Journal of Business Research, 118, 439–452.
• Rasmussen, R., & Ulrich, D. (2015). Learning from Practice: Achieving Strategic Impact Through HR Analytics. Business Horizons, 58(5), 545–554.
• Scott, P., & Michael, J. (2019). Ethical Considerations in People Analytics. Journal of Business Ethics, 155, 113–125.
• Wallace, J. C., & Deery, M. (2021). The Future of HR Analytics: Trends and Challenges. Human Resource Management Review, 31(2), 100738.
• Zhang, L., & Li, H. (2020). Data-Driven Decision Making in HR: Opportunities and Challenges. International Journal of Human Resource Management, 31(4), 532–554.
• Wright, P. M., & McMahan, G. (2011). Exploring Human Capital and Talent Management. Human Resource Management Review, 21(2), 93–99.