Roxanne Laczo on shaping talent programs for organizational growth

Meet Roxanne Laczo, who has built and led people analytics teams at both multinational and high-growth organizations. Roxanne is known for her global expertise in shaping talent programs that drive organizational growth. An influential leader, she’s adept at leading cross-functional initiatives with senior stakeholders, including the C-suite.

In this interview, we delved into the role, importance, and methods of people analytics within organizations. We also touched upon data utilization in HR decisions, the significance of a robust statistical foundation, tools, and technologies, and benchmarking people analytics practices against competitors.

I think people analytics has a twofold purpose:

• One, the more common definition of people analytics, is the collection and application of workforce and talent data to help businesses make better decisions.
• What’s missing is this secondary piece around strategic and statistically sound measurement and talent assessment with research-based systems, programs, etc. I think it’s not only using the data that you currently have to help businesses and HR teams make better decisions, but I think it’s also around having a solid foundation of measurement in terms of how you develop programs and how you measure the success of programs.

People often associate people analytics primarily with metrics like ‘time to fill‘ or ‘headcount.’ However, its scope extends much further. A fundamental aspect is ensuring a robust psychometric and statistical foundation.

• Are your interviews standardized?
• Are your interviewers trained to ensure fairness and equity?

While many focus on these basic metrics, there’s a deeper layer to people analytics that’s equally crucial.

This in-depth approach applies across the talent lifecycle, from an individual’s initial contact with the organization as a candidate to their exit as an alumnus. Each phase and every employee-related activity should stand on a firm analytical foundation.

When evaluating employee engagement, decades of research have been dedicated to crafting statistically sound surveys. Books detail how to create effective survey questions, and psychometrics guide the number of questions per section. Confirming that your metrics possess internal validity, reliability, and consistency is pretty essential. Linking these metrics is vital.

For example

One should establish connections to employee engagement when considering talent acquisition and onboarding. It’s worth examining if activities during onboarding impact future engagement. Does an initial meeting with a manager influence an employee’s subsequent experience?

Engagement surveys are crucial as they can preemptively indicate potential issues. For example, understanding why certain employee groups leave can inform your talent acquisition strategy. If a goal is to hire more women in leadership roles, assessing engagement and retention strategies for female employees is imperative. Observing exit rates can further refine this approach.

It’s essential to realize that all these facets are interconnected. Yet, many analytics or HR teams examine them in isolation. A holistic approach to an integrated talent management strategy is crucial, ensuring each component influences and refines the others.

The data sources used in people analytics can vary extensively, depending largely on an organization’s technological maturity and available tools, both internal and external.

Most organizations utilize an HRIS (Human Resource Information System) at the foundational level. For instance, companies I have worked for recently use Workday, but many companies opt for systems like Success Factors. Another cornerstone is the ATS (Applicant Tracking System), which includes platforms like Taleo, Greenhouse, and ICIMS. These are pivotal for comprehensive insights across the employee lifecycle.

Platforms like Workday offer embedded tools for measuring engagement and conducting surveys. However, many businesses employ external vendors for this purpose. Beyond the HRIS and ATS, several organizations rely on employee experience or engagement measurements tracking systems like Qualtrics, Perceptyx, Glint, or Culture Amp. These tools often allow integration or uploading of external data types.

Mature people analytics teams often upload their HRIS data into these systems and, importantly, integrate business outcome data. Sales data, being commonly available, is a notable example. Linking sales data to performance evaluations, quality of hire assessments, or potential evaluations can provide invaluable insights. For example, understanding the performance and retention of top-performing sales associates can guide talent acquisition and retention strategies.

Other significant data sources include financial data or data from platforms like Salesforce. Any metric related to employee evaluation can serve as a foundational data source.

As for how these data sources are utilized, it can vary. They might be used independently or integrated. Several companies opt for cloud-based solutions that amalgamate all the data. However, the approach largely depends on the company’s specific needs, IT systems, and the in-house BI (Business Intelligence) teams — determining what they have, should develop, and might need to procure externally.

I’ve based my model on years of experience and insights from other leaders. While not everyone may agree, leaders should excel in some areas while ensuring their teams cover all of them. Here are my four primary dimensions:

• Content and theory. As an IO psychologist, I firmly believe in the importance of someone with an IO psychology background playing a key role on any people analytics team. While someone from a technical, BI, or consulting background might excel in structuring questions and integrating data, they might lack knowledge about the underlying theories. For instance, understanding how to develop an effective structured interview process or the psychometric property of a survey ensures fairness and equity. Grounding our analytics in theory and research elevates our insights beyond mere numbers.
• Statistics and analytics. While not everyone needs to be a data scientist, a basic understanding of measurement theory is critical. Recognizing a statistically significant difference, segmenting data meaningfully, and employing predictive modeling is essential. Depending on the company’s maturity and infrastructure, there might be a varying appetite for such analytics.
• Data and systems. Many people analytics teams emphasize this dimension but thought leadership is also necessary. This category covers HRIS management, data warehousing, data privacy like GDPR, and data visualization. Knowing and complying with data privacy regulations is pivotal, especially with varied regulations in different countries.
• Consulting and influencing. For more mature companies, people analytics leaders must engage in strategic discussions with business partners and leaders. Understanding business problems, building relationships, and crafting compelling stories are all part of this dimension. Change management, stakeholder management, and story-telling capabilities are crucial.

Interestingly, a potential fifth dimension is emerging: Generative AI. With advancements in AI, companies are adjusting their expectations. Tools like ChatGPT and Midjourney exemplify this shift. The role of AI in people analytics will likely evolve, shaping the landscape.

With AI’s rising prominence, tools like ChatGPT and Midjourney prove invaluable for rapid analytics. The integration of AI is the future. In terms of hard and soft skills, I’ve delineated them through these dimensions.