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What’s It Really Like to Work? Using Big Data to Capture the Human Experience

Our society is obsessed with working harder, smarter, faster, and better. The internet, books, and experts are filled with advice on how to work to be more productive, more creative, more collaborative…the list goes on. Researchers have dedicated entire fields to figuring out how to get employees to work most effectively within their organizations. But with a recent push towards creating more meaningful and impactful careers, we’re quickly realizing that we’ve forgotten to look at one important piece: what is it actually like to work? And how do we make that experience better? Luckily, with new technologies and endless sources of big data, it is easier than ever to examine workers as seeing, feeling, experiencing human beings.

In their 2011 article “Experiencing Work: An Essay on Person-Centric Work Psychology,” Howard Weiss and Deborah Rupp argue that work psychology and human analytics have traditionally focused on outcomes of interest to the organization, such as retention or productivity. In the process, however, the worker as a complex individual has been neglected. Somewhere along the way, we have forgotten the fact that humans are active agents who are living through each of their working experiences. While researchers and businesses are interested in variables like employees’ levels of job satisfaction, this interest has generally only gone as far as it matters for bottom-line organizational outcomes. Workers are assessed as objects with properties, and these properties are related to outcomes of organizational interest. If the employee is satisfied, will they stay? If the employee is unsatisfied, will they work less?

However, in today’s business environment, it is no longer sufficient to view workers as vehicles of traits and properties that serve an organization’s purpose. More than ever before, employees are demanding jobs in “meaningful” workplaces. People want to craft fulfilling careers that can become integral components of their identities. Employees want to use their full skill-sets in their job, and feel appreciated for it. This may explain why the concept of work flow (first coined by psychologist Mihaly Csikszentmihalyi) has taken off in popularity as a sought-after subjective state in which one is fully and contentedly absorbed in their work.

With the push for more meaningful work experiences, a worker-centric approach to understanding employees is imperative. Psychologists and managers have struggled to understand how to capture this, as common tools such as surveys seem to circulate the traditional methods of measuring pre-assumed properties that workers should have. Some researchers have encouraged the use of qualitative interviews to hear individuals’ full stories, but this can be time consuming and labor-intensive.

So what’s the solution? Big data.

At first, this seems counterintuitive. How can we use big data, which seemingly contains endless and often mind-numbing bytes of information, to understand the workers’ subjective experience at work? Isn’t it backwards to use droves of data to understand individuals?

Actually, the massive amount of data available makes it exponentially easier to understand how people are genuinely experiencing their work. Big data can include information in the form of worker details including demographic, compensation, behavioral, performance, and social interaction information. As Jacqueline Ryan and Hailey Herleman explain in Eden King and colleagues’ book Big Data at Work: The Data Science Revolution and Organizational Psychology, these components together provide a comprehensive understanding of individual workers. Rather than asking and measuring employee constructs (e.g. job engagement) in isolation, we now have nearly endless access to a fuller range of information on the whole employee.

Big data can be accessed through multiple sources and then analyzed in numerous ways, some of which may particularly relevant to creating a deeper understanding of human working experiences. King and her colleagues also highlight a few of these tools and sources for big data. Here are some that could especially inform a person-centric approach:

· Sociometric sensors — data collected via cell phone, watches, pedometers, and biometric trackers such as FitBit. Sociometric sensors can collect data regarding activity levels and nonlinguistic social signals such as excitement or interest.

· Social media data — data collected via common social media outlets, such as Facebook or LinkedIn. Data on social media can be collected regarding how often people are sharing, as well as the content that they are sharing, which often provides insight into what people are feeling or experiencing.

· Text analysis — huge amounts of big data exist in the form of text, whether that includes information collected through social media, daily journals, emails, or transcriptions of interviews. While text is a more traditional form of analysis, work psychologists and organizations can use inductive approaches to analyze text and draw conclusions from raw text about what individuals state they are experiencing.

· Sentiment analysis — sentiment analysis is a specific form of analysis that can be conducted on any kind of text or speech that includes individuals’ expressed sentiments. This method examines the content of people’s sentiments and can reflect how they are subjectively experiencing the content they are describing.

· Microexpression analysis — microexpression analysis codes facial action to analyze genuine human emotion and reactions to certain situations. By assessing photographs or videos, this form of analysis can take large pools of data and provide a glimpse into individuals’ experienced emotional states.

· Neuro/psychophysiological tools — these tools can capture individuals’ physical reactions to situations or experiences, such as heart rate, cortisol levels, or brain activation.

With so many methods for collecting and analyzing individuals’ subjective experiences in large quantities, the marriage between a worker-centric approach and big data is clear. It is now easier than ever to consider the worker and their experience of work, with the focus on the individual rather than just on the organization.

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