Future of people analytics: Interview with Stephanie Murphy, a top 10 data analytics professional

Meet Stephanie Murphy, a people analytics leader and management professor at Texas McCombs School of Business. Stephanie has been internationally recognized for her work in employee listening, research, assessments, and analytics, and she has been featured in several publications, webinars, and podcasts. Stephanie has experience teaching MBA courses such as people analytics and diversity and inclusion.

In this interview, we discuss people analytics and its role in organizations, its applications in talent acquisition, retention, and employee engagement, key metrics and data sources used, challenges and ethical concerns, required skills and expertise, and its future role in shaping organizational decisions. We also touched upon the tools and technologies commonly used in people analytics and addressed the ability of people analytics to forecast future workforce needs.

People analytics, to me, simply uses people data to derive insights and make decisions that ultimately improve organizations. It quantifies human characteristics and behaviors in the workplace and looks for patterns and trends. It includes anyone in an organization collecting and analyzing data to look for insights about their talent. These include culture, leadership, engagement/retention, selection/recruitment, learning & development, performance management, promotion & succession planning, job design & analysis, com & ben (compensation and benefits), and DEI (diversity, equity, and inclusion).

For the most part, people analytics professionals belong in HR for many reasons, but namely data governance and data privacy purposes. But these individuals can be in many different functions across HR such as talent acquisition, employee listening, comp and benefits, performance, talent development, HR technology, diversity & inclusion, or sometimes in a separate function specifically for HR analytics. I don’t think there’s a wrong or right model for where PA experts should sit within HR; it depends on what works best for the organization and what model fits the culture. There are pros and cons to decentralized models (PA dispersed in different HR functions) and centralized models (PA in its separate function). For instance, decentralized models enable deep domain knowledge for PA professionals, which leads to more actionable and useful insights; however, it can lead to inconsistent data being shared across functions and missed opportunities to see cross-functional insights. The opposite occurs in centralized models; they facilitate information sharing but can lead to a lack of specialization.

The primary role of PA in organizations is to provide data-driven insights that lead to actionable insights that drive strategic decisions related to talent and contribute to the company’s overall success.

People analytics can help in these spaces by assessing the current state, looking for trends and patterns to determine what’s working and what’s not, and assessing the impact of actions/interventions taken based on the data.

For example
People analytics can help in talent acquisition by identifying effective recruitment strategies, predicting candidates’ success rates, and finding ways to improve the hiring and onboarding processes. For retention, it can help by identifying what may be contributing to turnover and what keeps team members happy/valued/engaged. For employee engagement, people analytics can help identify what factors drive engagement and where there are high and low pockets of engagement across the organization.

This depends on the goals/vision of the organization, but some common KPIs include, turnover rates, employee survey scores, performance ratings, time-to-fill job openings, DEI metrics, learning and development usage and metrics.

This also depends on the structure and vision of the people analytics team, but some common data sources include:

• Demographics (e.g., location, job family, tenure diversity) – typically in the HR system
• Listening tools (e.g., surveys, focus groups, interviews, crowdsourcing) – require data collection
• Development tools (e.g., assessments) – homegrown or vendor-built
• Direct performance metrics (e.g., sales attainment, reviews, customer ratings, succession plans, merit increases)
• Learning usage (e.g., courses, programs)
• Social impact/D&I participation (e.g., employee resource groups, volunteering or giving, mentoring)
• Indirect performance data (e.g., emails, meetings, badge entries)
• Other (e.g., health trackers, AI, external benchmarks).

Yes, people analytics teams use various tools. We use a data analytics platform (e.g., Tableau, Power BI, Domo), an HR management system (e.g., SAP SuccessFactors, Oracle Fusion, Workday), several analytics and data science tools (e.g., R, Python, SPSS), And a survey tool (e.g., Medallia, Perceptyx, Qualtrics).

Some common challenges and concerns are data privacy and security (e.g., following new laws and governing access to PII), avoiding bias in AI and algorithms, transparency in when data is being collected and how data is being used, and navigating ethical boundaries related to passive data (e.g., employee surveillance; when do things become too “big brother?”).

Like many things, as you’re hearing me say, this varies depending on the technology and tools available and the team structure. Teams are usually either people-centered or technology-centered — you need both, but teams will lean towards one another — psychology vs. data science, human behavior vs. data, and numbers.

For people-centered teams, I’d say the #1 skill is data storytelling. Being able to tell a story with the data includes knowing how to use data visualization to display the data/insights in layman’s terms and communication skills to tell the story and consult on actions. But this may not be so important for a PA expert who isn’t customer-facing and is more technology-centered, in which the #1 skill is knowledge of the technologies, tools, and data analytics software.

Despite a spike in layoffs this year and last, I think people analytics will continue to grow in importance. As technology advances and we can collect more and more people’s data, we will need PA experts to find insights, humanize the numbers, and consult to bring the data into action. When we hit unprecedented times, we can no longer rely on our experiences from the past, the tried and true, or our intuition. We have to rely on data and science to make decisions. Data-driven decision-making will only become increasingly the norm across all aspects of organizations, including HR.