4 Things Harvard Business Review Got Wrong About People Analytics

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4 Things Harvard Business Review Got Wrong About People Analytics

Recently, Harvard Business Review (HBR) published an article on people analytics titled Are People Analytics Dehumanizing Your Employees? In the article, the authors provide a bleak and one-dimensional view of people analytics that, if not seen in context, will damage the field and dilute the current and potential contribution that people analytics can make to HR.

In this article, we want to debunk the four things we believe HBR got wrong about people analytics. These misconceptions are dangerous to the field. They could set back much of the progress we have made in Human Resource Management over the past decade. Let’s dive in!

Contents
1. People analytics reduces people to their data
2. People analytics equals employee spying and surveillance
3. People analytics is a result of artificial intelligence (AI)
4. People analytics occurs outside of the organizational context
Going forward

1. People analytics reduces people to their data

According to the authors, “workers are increasingly being defined in terms of their data”. In a field that traditionally relies on instinct and gut feeling, we don’t think that’s a bad trend.

Data-driven (or evidence-based) decision-making has revolutionized every discipline it has been applied to. For instance, evidence-based practices have revolutionized how we determine diagnoses in healthcare and treat patients. It has contributed to saving countless lives. In fact, evidence-based prevention services could prevent half of the 2.5 million annual deaths in the United States.

An increase in evidence-based practices in HRM is much needed. From biases in hiring and selection to performance reviews and even commonly used ineffective anti-bias training, HR will benefit from taking a critical look at the impact it makes. The only way to do that is by objectively and responsibly looking at the data.

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In doing this, people analytics has the potential to move many HR disciplines forward. For example, another recent HBR article highlights the opportunities that a metrics-driven Diversity, Equity and Inclusion (DEI) practice brings to the organization.

We also see this approach being incorporated into job design. A recent vacancy for Racial Equity People Analytics at Google illustrates this:

Google People Analytics Vacancy Example

Other contributions include data-driven organizational design, workforce planning, rewards, the employee value proposition, and total wellbeing. In contrast to what the article claims, in each instance, data helps create a more personalized experience.

“This trend of depersonalizing employees isn’t necessarily new. For some time now, HRM has focused less on approaching the employee as a “whole” human being and more on promoting a one-size-fits-all approach to manage employees.” – HBR



Being data-informed helps understand the holistic human experience in an organization. One organization used analytics to monitor trends of COVID-related illness in different regions, allowing the organization to proactively send additional support to employees. Another example includes data being utilized to define personalized benefit experiences and support.

We think the authors of the HBR article should welcome a more evidence-based approach to managing people at work, and we can only hope that HR will further root its decisions in data.

2. People analytics equals employee spying and surveillance

The second and more damaging mistake is that, according to the article, people analytics is involved mainly with employee surveillance. “The creep of surveillance deeper into more and more parts of the workday means that employee privacy is all but being erased, and their work experience is being negatively affected”.

The authors make a common mistake of confusing technological surveillance done by cybersecurity teams with the People Analytics work being completed by teams focused on the workforce. People analytics is not about monitoring but about helping us understand how to best design (hybrid) work in the context of human and organizational needs.

Where organizations have overstepped and used data to “spy” on employees, this is not a reflection on people analytics. Instead, it reflects a distrusting and toxic culture and a lack of management maturity and values. We cannot blame these tools and techniques we have available without looking at who is using them and how.

Most organizations have rigorous internal controls on who accesses and analyzes data and for which purpose. People analytics leaders operate by the grace of the company’s legal team, work councils, and data privacy officers. They are often being held to a much higher standard than some other analytics functions – and rightly so!

We acknowledge that people analytics can negatively impact the organization if used inappropriately and irresponsibly. However, this is true of various other fields, such as organizational development, rewards, and psychometric assessments. We believe that not using people analytics poses a bigger risk to creating fair, unbiased, and informed HR practices, processes, and policies.

HBR People Analytics Misconceptions

3. People analytics is a result of artificial intelligence (AI)

There are many misconceptions about people analytics, HR technology, and artificial intelligence (AI). The article claims that “People analytics comes from organizations’ growing use of artificial intelligence (AI) to promote efficiency”. This may be the biggest misconception in the article.

Some of the biggest gains in people analytics are not from advanced algorithms, technologies, or tools that deploy AI. In fact, AI is often the flashy diversion from the real impact analytics can make.

The most significant value-add of people analytics is creating a culture of decision-making based on the best available evidence aligned with ethical guiding principles and values. AI rarely plays a role in this process.

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The authors also argue that people analytics is a step towards automation, leading to a replacement of employees. There are two reasons why we disagree with this argument. First, people analytics is about making more data-informed decisions that will hopefully benefit both the employee and the organization. We acknowledge that this could, at times, inform decisions pertaining to automation. Still, these decisions would at least have been made from an objectively informed perspective as to whether automation will result in value.

Second, we believe various sciences contribute to the possibility of automation, such as process re-engineering and business analysis. The focus of these and similar fields should be on managing the changes that occur as a result of these processes in a responsible and humane manner. Dealing with the fear of change due to automation is a reality. However, this is not a result of people analytics but of the impact of the Fourth Industrial Revolution on the world of work.

4. People analytics occurs outside of the organizational context

People analytics occurs within a broader organizational philosophy, culture, and value system. This relates to how we treat people, how we engage with our employees, and what behavior we want to drive. 

How, where and when we apply people analytics should be in accordance with the motivations and intentions specified within the broader organizational context and will never occur within a void.

People analytics itself also goes beyond the statistical analysis of data. Rather, it is about a mindset that challenges assumptions, tests hypotheses, and uses sound scientific principles, such as triangulation of data, to inform and guide insights.

An HR professional who is not able to show the data and prove the impact of people policies will be less effective in advocating for employees. They will be unable to effectively advise the business on the best course of action. Similarly, an organization that distrusts its employees will be more likely to apply surveillance technology. This is why the application of people analytics should never be seen in isolation. Instead, it needs to be viewed within the context of the organization.

For example, two teams with similarly high levels of absenteeism can be vastly different. One team may function well but have one or two terminally ill employees, while in another team, poor leadership and high work pressure cause absence to spike. Data is often the starting point. However, it will never tell the whole story until data is triangulated and supported through qualitative interviews, focus groups, and so on. 

Mature people analytics professionals are fully aware of the fact that they need to see the data within the context of other contributing factors.

Going forward

People analytics enables HR departments to make evidence-based decisions and increase their credibility within the business. This means that HR can be more effective and impactful in their initiatives and advice. It should benefit both the individual employee as well as the broader organization.

Until now, people analytics practitioners have been leading this field. It is time for academic institutions, researchers, and journals to take up the baton and work to better conceptualize what people analytics is (and is not). They should show how to measure the impact it has on the HR department and business as a whole and help create standards for good people analytics practices to guide practitioners.

Over the past decade, we have seen people analytics slowly change the way HR operates. We hope HR continues to evolve into a more data-driven and evidence-based discipline where decisions are made based on the best available evidence, and in the best interest of all stakeholders, including employees.

This will lead to a data-driven science that can tangibly contribute to our understanding of human behavior in the workplace. Then, it can help organizations make better decisions for their employees, clients, their bottom line, and the broader society.

To get there, there are still many hurdles to overcome. These include:

  • educating organizations on how to use people analytics responsibly,
  • communicating the role and contribution of people analytics to employees and HR,
  • collaboration between industry and academia to define ethical codes of conduct,
  • and people analytics being seen as part of an integrated people value chain.

As organizations overcome these hurdles, people analytics will not dehumanize employees but rather empower them. After all, what is an organization, if not the people, that work towards a shared future?

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