Littal Shemer Haim

People Analytics, HR Data Strategy, Organizational Research – Consultant, Mentor, Speaker, Influencer

Should People Analytics Practices Change to Face Post-Covid19?

There are four types of interdependent questions to address: How employees feel, what do they need to get the work done, how productive they are, and how they maintain their connections. To answer those questions, we can leverage any of the three levels of People Analytics.
Photography by Littal Shemer Haim ©
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Should People Analytics Practices Change to Face Post-Covid19 Challenges? No, and Yes.

The Covid19 crisis accelerated the digital transformation of work, mainly through the crucial need to disconnect the workforce from the workplace. Although the discussion about the role of HR leaders in successful digital transformation was pervasive even two or three years before the pandemic, it became urgent as organizations quickly adapted to working from home. As we approach the post-pandemic times, it appears that things are not going to go back as they used to be before Covid19. A new chapter in this discussion starts now. Organizations prepare to combine remote work with a physical presence in the workplace in the post-pandemic times, AKA “the new normal” or “hybrid work model.”

The hybrid work model has many advantages. It enables to save costs and positively impacts the bottom line of the business. Therefore, managements support it. However, it also has its challenges. I was privileged to participate in an experts’ panel at the Innov8Work conference and share my perspective about People Analytics in times of post-pandemic new normal. The main question raised was what data companies should collect to design, maintain and improve their hybrid work model. For me, such a question represents a fundamental but widespread misunderstanding among HR professionals. 

HR data strategy did not change during the pandemic

I perceived the question about data needed to handle the new normal as a misunderstanding because, in People Analytics, we never start with data collection. Every People Analytics project we conduct, and every People Analytics solution we decide to buy, always begins with business questions that eventually lead to actionable insights. It was true before the pandemic and will remain forever valid. However, we pose new business questions relevant to the post-pandemic times and transform them into analytics.

The steps to defining business challenges as analytics are straightforward. We start by identifying a key concern, goal, or issue for the business. We then create a hypothesis as to how the workforce performance or behavior impacts that key concern. We continue by defining what needs to be measured to test that hypothesis. Only then we source the data from any departments that hold it.

Let’s take an example to demonstrate how to articulate a business question and transform it to analytics that serves the design of the hybrid work model. Suppose a business which experiences a decline in customer-related problem-solving during Covid19. The customers who reach fewer resolutions express less satisfaction. Customer success agents who worked separately from their team during the pandemic lacked the support of their team members, and therefore their performance was significantly low.

As the pandemic gets closer to its end, there are different attitudes among the agents regarding the work model. Some agents prefer to return to the office, others prefer working remotely, yet many agents hope to work flexibly and find the best mix of office and home working days. The company needs to find the best combination that, on the one hand, contributes to employee preferences but, on the other hand, enables to increase the interaction between agents in a way their performance pick back as pre-pandemic KPIs.

In general, there are four types of questions to address: How employees feel, what do they need to get the work done, how productive they are, and how they maintain their connection with different parts of the organization. Obviously, these four questions are interdependent.

The Analytics methods did not change either

To answer those four questions, we can leverage any of the three levels of People Analytics: operational reporting, data mining, and scientific methods. Let’s explore some optional research projects for the hypothetical customer success agents.

Most HR departments already leverage their operational reporting to support their organization during the pandemic and beyond. They can provide the management with a snapshot of current workforce data. The customer success agents, for instance, are diverse in many ways, from demographics to tenure, performance, absenteeism, and attrition, and their attitudes and preferences regarding work models, as organizational surveys may reveal. These data sources are typically presented on dashboards. However, even if you leverage pre-existing data and visualize it on a dashboard, it is only helpful for temporal reporting and insufficient to support strategic decisions.

Usually, to guide strategic decisions, you need to collect data or generate insights by integrating data from different sources and use data mining, i.e., statistical models and machine learning techniques, to spot significant patterns in the available data. Such analysis may provide you answers beyond what’s happening and contribute insights into why it is happening. We can explain and predict outcomes such as the performance of our customer success agents by inputs such as burnout, connection patterns with others, time resource allocations, workplace resource allocation, and more. However, such correlations are certainly not causation. For example, employee engagement measures can boost employee performance, but poor performance due to insufficient resources can lower engagement.    

Fortunately, organizations can use People Analytics to determine causation. They do so in scientific methods, e.g., experiments and controlled trials. In those methods, you don’t start with pre-existing data but rather with a business outcome and then postulate the impact of various HR processes on that outcome. For instance, in our hypothetical customer success agents, we can hypothesize that a specific onboarding program may contribute to a faster ramping-up to fully contributing new employees. We can then test this new onboarding program against the traditional one in a sort of A/B testing. The same approach is suitable to test mixtures of hybrid work models.

What did change? The analytic mindset of executives and HR

People Analytics is not merely a function or a role within the HR department. It is a mindset that each HR professional should have and leverage to impact both the business and the people. This analytical mindset, which becomes more common within HR teams, essentially evolved during the pandemic. HR professionals focus now more on using people data derived from HR processes to impact the business. Specifically, the research approach of HR moved from focusing on human resource processes, which they have traditionally measured, to the focus on business performance indicators on which they want to impact.

Processes conducted by HR during the entire employee lifecycle, e.g., recruitment, onboarding, learning, feedback, recognition, reward, promotion, mobility, are impacting the workforce outputs. The pandemic forced HR to change many of those processes, and now they must change them again to be applicable for the hybrid work models. The association between HR practices and the business outcomes must be re-established or adjust.

The entire HR-tech industry cover HR processes along the employee lifecycle, making it easier to collect and analyze data from each process. Products that were previously considered as HR operations are now a vital part of the People Analytics ecosystem. HR professionals understand that People Analytics is not about surveys or employee reviews, but rather the integration of the entire data universe related to the employee journey in the organization.

The evolving use of tech to support that journey for both employees and managers, i.e., HR data’s consumerism and democratization, makes Ethics a vital part of the HR role. HR leaders start to exercise their voice in ensuring ethical use of talent data and establish new practices in collaboration with other teams, e.g., legal and IT. The ethical use of employee data is no longer perceived as solely belonging to the HR—tech vendor side.

To conclude, HR data strategy did not change during the pandemic, and analytics methods did not change either. We still follow the fundamentals of business questions and actionable insights, and we still classify People Analytics projects as operational reporting, data mining, or scientific methods. However, the analytic mindset of HR changed and brought them to leverage technology and to associate their process with the business. I believe that it guarantees a seat at the decision-making table.

Littal Shemer Haim

Littal Shemer Haim

Littal Shemer Haim brings Data Science into HR activities, to guide organizations to base decision-making about people on data. Her vast experience in applied research, keen usage of statistical modeling, constant exposure to new technologies, and genuine interest in people’s lives, all led her to focus nowadays on HR Data Strategy, People Analytics, and Organizational Research.

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