The future of HR & analytics: A deep dive with Richard Rosenow

Meet Richard Rosenow, the VP of people analytics strategy at One Model and a long-time practitioner and thought leader in the People Analytics space. Richard is actively seeking connections with individuals and companies in the HR analytics and Talent Intelligence space, offering his expertise for guest lectures, speaking engagements, podcasts, conference keynotes, and discussions on the future of People Analytics.

In this interview, we discussed the enduring importance of human involvement in decision-making processes, particularly in fields like data analytics and HR, despite advancements in artificial intelligence and automation. We emphasized that while technology can enhance efficiency, it should not replace the human element, and the future will likely see a return to more human-centered roles in these domains.

I’ve been in the people analytics space for about ten years. I work for a people analytics tech company, and it’s like when a basketball analyst starts working on a basketball team. That’s me. I’m so nerdy about this stuff that, to me, people analytics is my whole world of work. But if I were to speak about the definition of what people analytics is, at the core, to me, it is decision support for decisions around the workforce.

We help people and business leaders. We’ve been making decisions about people for a long time based on our human brains and what we do well as humans, and HR has done a good job there. HR doesn’t get enough credit for that artisan approach they had to take before. But now that we have data, technology, tools, and data professionals that can help us, we can start to make better workforce decisions with data.

It seems like just a funny thing. HR’s children always want to run away, but I think HR itself is a pretty good spot. So, when I think about it, I call out that it’s people analytics. It’s not just analytics. And why is that? It’s that working with people data is very different than working with other data.

Behind every data point is somebody there with family, friends, and livelihoods, and our decisions about work deeply impact people. That’s slightly different from, for instance, marketing or finance. You can automate if somebody goes to your website, send them an ad, or send them a website or email, and that’s acceptable automation.

Also, ethical trouble, “Did I allow that computer to decide for somebody, and it’s not who I want to be as a person or who we want to be as a company?”

People analytics best suits the function where folk are responsible for the workforce and sees the humans in the workforce. And it’s that psychological kind of mindset, that philosophy you have behind it, to say, I have the ethical grounding and the bearing to understand the weight of these decisions that systems could be making. I can’t just come here with a data science sledgehammer and start automating decisions. You’ve got to bring that wrapper of human awareness. That’s why, to me, people analytics sits best within an HR function. But I’ve seen it sit in plenty of other places, and it can be very successful there, too. So, this is not to say it can’t sit in one or the other, or there has to be a hard line. It depends on the structure, the leader, the HR person, and all those different parts.

It’s tough to answer this simply that in every decision somebody makes about people; you could bring people analytics and data to bear to help with that decision. So where should we be hiring, who should we hire, how do we onboard them, how do we bring people in, what candidates are successful, you name it, people analytics could help.

But with that in mind, you could have a data scientist pour over every decision in HR, and you would get nowhere because it would take too much time and cost too much from a resource perspective. Where you see teams become most successful is where they find out, for your business, where your business is experiencing the most pain and then help there. Our people might be leaving in droves; let’s figure out where, how, and why, try to understand what’s going on, and predict what’s happening with the nutrition.

But at a company that’s having a 1% attrition rate annually, they would not be interested in any of that work. And so much of where to apply People analytics is related to the business strategy and what the business cares about on a given day, week, or year.

You might actually need to work on the supply of candidates and understanding talent markets to bring that data to bear. So, to summarize, I think there are countless ways people analytics could be applied. It comes down to what’s essential to your business, where you are struggling, and your questions about your workforce. You’ll have to figure out your company’s needs and then dive in to go after those.

Regarding KPIs, it’s a bit of a nuanced topic. Essentially, effective KPIs are closely linked to specific business outcomes. For instance, if you’re running a retail store, the KPIs related to your workforce should directly impact your store’s performance. This connection between what your workforce does and its impact on the business is at the core of people analytics, and it’s arguably the most crucial aspect of our work.

For example

Imagine you’re in a retail setting and have a wealth of data about your employees, their work schedules, interactions, and job roles. The real value lies in harnessing this data to comprehend and diagnose how those metrics move to learn how to boost your store’s revenue or reduce expenses. You want your workforce metrics to guide you in determining what truly matters.

Now, some well-documented studies have shown that employee satisfaction correlates with customer satisfaction, which, in turn, influences profitability. So, satisfied customers are essential for a profitable store, and your employees should be content to have satisfied customers. It’s a fundamental concept that seems simple, but the true power of people analytics lies in identifying and quantifying these intricate connections.

And to that point, what works for a clothing store might not necessarily apply to a bank with a customer service environment. Different factors may be more critical for each. For instance, in a clothing store, attrition may not be directly related to profitability; perhaps they can continuously hire new staff without affecting profitability, thanks to an exceptional training program. However, the key is understanding what truly drives these outcomes, and that’s what ultimately shapes what KPIs you should select.

This is a particular passion area of mine. I’ve got a blog called The Three Channels of Workforce Information that dive into this topic.

At the foundation, you have your core HR tech systems, such as Workday, Oracle, SAP, Lattice, HiBob, Eddy, and similar HR platforms. These systems seek to cover core HR, applicant tracking tools like Greenhouse or SmartRecruiters for recruitment, compensation tools, performance management tools, and others owned by the HR team, providing a level of control and access.

As teams delve deeper, they explore work tech systems, including accounting software, facilities data, and tools employees use for day-to-day tasks like Salesforce or Jira. Beyond that, there’s collaboration tech, which covers communication tools like Zoom, calendars, Slack, Teams, and emails. These tools establish connections within the organization, and mapping these relationships can reveal insights and help with issues like attrition as turnover spreads through social networks.

Survey tools, including engagement and pulse surveys, are crucial in gathering information our other systems do not capture. Finally, the often-overlooked channel is interpersonal conversations. HRBPs, for instance, possess valuable workforce insights that may not be neatly documented but are essential for analysis. Incorporating this contextual knowledge into your analyses can greatly enhance understanding during discussions.

I would break the challenges down into 3 types of challenges:

• Technical challenges
• Business culture challenges
• Ethics regulations and laws.

So, let’s talk first about ethics regulations and laws. Trusting your employees and developing the trust of your employees is a fundamental aspect of this discussion. Trust is essential; you shouldn’t consider engaging in this work without it. It’s not just about trust in the moral sense; it’s also about the practical consequences.

This is one of the few datasets that could choose to deceive you regarding business data. Errors in analysis are inevitable if employees are unwilling to communicate openly with you. Your ability to carry out your tasks effectively as a people analytics leader hinge on trust. The moment you cross the boundary and become invasive or untrustworthy in the eyes of your organization, people analytics loses its significance.

Therefore, while there’s a moral imperative to approach this work with the right intentions to make lives better for employees, there’s also a stark warning against doing this incorrectly or neglecting ethical considerations. Doing so could lead to the erosion of your impact. Additionally, it’s worth noting that we are witnessing the emergence of various regulations and laws in this field.

As previously discussed, one significant legal framework to consider is the General Data Protection Regulation (GDPR), which extends to employee data in certain contexts. In the United States, regulatory bodies like the Federal Trade Commission (FTC) or the Securities and Exchange Commission (SEC) have recently emphasized that using AI for workforce analysis does not exempt you from current on-the-books regulation. While specific AI regulations may not exist (yet), existing laws addressing bias, diversity, and ethical conduct still apply. In other words, using AI or any advanced tool doesn’t grant you a license for unlawful activities. The United States has a substantial body of laws governing workplace conduct and support for employees, and these regulations play a large part in the realm of people analytics.

The ethics landscape in this field can evolve into industry standards and legal requirements over time. Your organization must be aware of its ethical responsibilities, as these can vary from one company to another. The willingness to collect or share certain data can differ; this relationship with the workforce can also vary by company. Furthermore, the value you deliver to employees in return for sharing data plays a significant role in this dynamic.

From a cultural perspective, there’s an ongoing shift from traditional operating methods to embracing new approaches to work. While some companies have adapted quicker, I’ve noticed a decreasing resistance from HR teams to participate in this transformation. In the early days of my career, there was a sentiment sometimes within HR that HR handled what they needed without needing extensive data. However, in today’s landscape, it’s widely acknowledged that data-driven decision-making is essential in the business world, especially in HR. Organizations rely on data to inform their strategies, and there’s a growing demand for meaningful data insights.

Nevertheless, there remain disparities in data literacy and maturity levels among individuals within these organizations. Some may be well-versed in leveraging data, while others might still feel uncomfortable learning about space. Unless you provide the support that the specific HR team needs, this disparity can create a barrier to effective implementation.

On the technical front, a significant challenge lies in gaining access to clean, organized, and consolidated data. Different HR teams within a company often use a variety of technology platforms, making the task of centralizing this data complex. Recruiting, compensation, learning and development, and employee relations teams have unique systems. Consolidating, cleaning, and organizing data from these disparate sources is a formidable task, and it’s an issue many analytics teams face. Many teams lean more toward data science than data engineering, which can create skill gaps when consolidating and preparing workforce data models.

This is where people analytics platforms come into play, addressing these technical challenges by collecting, processing, and architecting the data. Our goal at One Model is to provide a platform that facilitates people analytics.

Expectations elevated. Given the rapid digitalization in the world, I think the expectations are higher for what we can produce in HR. Expectations for data-driven insights have significantly increased, with demands for real-time access and a consumer-like experience in data interaction.

Growing demands on people analytics teams. Due to these heightened expectations, people analytics teams are now tasked with delivering more timely and insightful data.

Supportive technologies. Fortunately, the landscape has evolved, with several technologies emerging to support people analytics teams and accelerate their efforts.

Time to value. Technologies like One Model play a pivotal role in speeding up the process, allowing organizations to quickly access and utilize their data, creating dashboards and insights that might otherwise take a year or more to develop in-house.

The role of vendors. Organizations collaborating with vendor partners tend to excel in the long run, like how payroll systems have become a domain fully supported by specialized technologies rather than in-house solutions. You would never think to build payroll in-house.

Importance of working with vendors. To meet the fast-paced demands of today’s data-driven environment, it’s increasingly necessary for organizations to work with vendors to expedite their data analytics initiatives.