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We suspect that few leaders appreciate the diversity and volume of data about employees that their organizations are amassing from sources ranging from digital collaboration platforms to workforce wearables and mobile devices. Even throughout the pandemic, the scope and nature of employee data has expanded rapidly to include vaccination status, the results from frequent health checks, virtual meeting behaviors, and work-life survey results.

At most large global firms, people and workforce analytics are mainstream initiatives led by HR leaders. But increasingly, employee data is being used in new ways, beyond HR, to produce lucrative results. For example, one organization analyzed employee activity and building characteristics to generate insights about occupancy in its facilities, which ultimately saved the organization millions of dollars in reduced heating and cooling costs.

At the MIT Center for Information Systems Research (CISR), we recently studied how innovative uses of employee data by organizations can both support and threaten the security of employee dignity.1 Particularly in digital transformation contexts, employee behaviors and knowledge are key to understanding how an organization has historically operated. This understanding can expose opportunities for improvement and lead to unanticipated outcomes when the organization decides to radically alter or eradicate certain work tasks. Such uses of the data can cause tensions and be fraught with complex ethical concerns.

Organizations may be tempted to govern employee data by relying on regulations rooted in personal data privacy and protection, such as the European Union’s General Data Protection Regulation and the Health Insurance Portability and Accountability Act in the U.S. But such laws fall short when it comes to the ethical oversight of employee data use. For one thing, employee data that informs and improves a company’s core operations can be exempt from regulatory constraints. Also, MIT CISR research has shown that a regulatory-based perspective is not broad or deep enough to comprehensively oversee the internal and external use of people data; companies need a capability known as acceptable data use (ADU) that includes legal, regulatory, and ethical oversight practices.2 ADU moves beyond regulation to offer ethical oversight by considering the expectations and desires of the organization and key stakeholders, including employees.

A regulatory-based perspective is not broad or deep enough to comprehensively oversee the internal and external use of people data.

Academic research published this year by two of us (Leidner and Tona) suggests that leaders can effectively manage the ADU-associated ethical requirements of employee data use by focusing on dignity.3 Such a perspective inspires leaders to seek consent, make their goals and findings transparent, and ensure that outcomes are mutually beneficial to the organization and its people.

In fact, we believe that putting dignity at the center of acceptable data use not only improves management and governance of employee data but also allows transformation leaders to reinforce the organization’s value for and commitment to its employees.

Understand the Dimensions of Employee Data

It’s helpful for leaders to first understand the different dimensions of employee data. We refer to the comprehensive set of interrelated employee data as 5-W data — data regarding who, what, where, when, and why:

• Who data describes the employees who are performing work. This has evolved from employee demographics, contact information, health history, and salary and benefits to include social network connections and biometrics from wearables.
• What data describes employee work activities. This can include online behaviors like internet searches and keyboard actions, as well as digitized offline behaviors such as video surveillance, call center audio transcripts, and work logs.
• Where data discloses employee whereabouts. Where data such as physical locale and spatial movement has become increasingly more precise with the introduction of radio-frequency identification (RFID) tags, workforce wearables, mobile tracking within a building, and smart factories.
• When data marks the timing of employee activities and related events or outcomes. This can include simple milestones of a workday or work task, or it can reflect a complex time series of events assembled using a variety of sources, such as usage logs, mobile devices, sensors, and transactions.
• Why data historically has represented employee expertise and logic, such as the documented business rules employees consider when making decisions or their reflections about customer contact experiences. With digitization, when it comes to work automation, organizations manage growing repositories about employees’ reasoning behind their decisions and behaviors. For example, AI training data increasingly includes employee feedback about model outcomes so that employees’ experiences and judgments can improve model performance over time.

What is striking about the diverse array of employee data is the vast potential for organizations to combine it in myriad ways. The combination of 5-W data allows companies to create unprecedented levels of transparency and insight regarding employee work practices and influences. We have seen companies do this to drive new information services, business model changes, and work task reengineering.

Understand the Use of Employee Data

With 5-W data in hand, the organization can use it to know employees — or to show employees. During digital transformation, both activities are important for pursuing transformation goals; knowing and showing help organizations assess, improve, change, and engineer work.

• Knowing employees refers to when an organization uses employee data to understand employee work activities, performance, and behavior and how that information relates to desired outcomes. We have seen examples of this in companies using employee data to automate or reengineer manual processes. When a project team knows what tasks employees are performing and why, it can create explicit business rules for robotic process automation, introduce robotics equipment into the workplace, or train supervised AI models that fuel more efficient processes.
• Showing employees happens when an organization uses employee data to communicate insights to employees or to others inside or outside the organization. A GE project involved using expert compliance evaluators to train an AI system on how to judge whether a contractor met a set of safety requirements based on its submitted documentation.4 The project surfaced evaluation criteria and processes that previously were locked up as tacit knowledge within evaluators — why data — by having the employees identify words and phrases that influenced compliance requirements, classify training documents based on whether requirements were satisfied, and audit the outcomes of model decisions. The project, which displayed human reviews and machine output on a graphical dashboard, and found that human reviewers sometimes were inconsistent in their evaluations and that different evaluators sometimes interpreted text in different ways. GE used that understanding to offer feedback to evaluators and improve their education.

Notably, organizations sometimes share the insights from knowing and showing employees with their customers or external partners. For example, they might share what they know about employee behaviors to partners that offer employee services, such as parking or corporate perks. They might show anonymized employee work practices or outcomes to a benchmark provider that assesses industry-level performance. And they might show employee whereabouts to customers to make service processes more transparent.

When Evaluating Employee Data Use, Center on

Dignity

Organizations stand to benefit greatly from employee data insights, given how they can inform improvements to work practices and customer experience — to the tune of realizing huge cost efficiencies and top-line growth and reaching previously unattainable goals. However, before these big wins can be enjoyed, companies and leaders must ensure that employee data management is carried out responsibly. Establishing a North Star for how the organization values employees and handles employee data use — by treating employees with dignity — is an excellent way to walk the talk.

Dignity broadly refers to the recognition that human beings possess intrinsic value, are endowed with certain rights, and deserve respect.5 Dignity takes three forms: behavioral, meritocratic, and inherent:

• Behavioral dignity: Individuals have resources to achieve a life of well-being.
• Meritocratic dignity: Individuals receive appropriate acknowledgment of their contributions.
• Inherent dignity: Individuals are treated as worthy of respect regardless of status.

These forms of dignity each offer a distinct way to evaluate whether an organization supports employee dignity or threatens it. Armed with this understanding, organizations can reshape practices to ensure that they are supporting rather than threatening the employees’ behavioral, meritocratic, and inherent dignity. (See “Data-Related Activities in Pursuit of Employee Dignity.”)

Organizations support behavioral dignity by providing employees with access to supporting resources and by helping employees achieve their goals and meet organizational expectations. GE did just this when it used why data to provide feedback and retraining to help employees become better compliance evaluators. The company also supported behavioral dignity through its highly transparent and evidence-based evaluation monitoring tool. The visual interface clearly displayed human and machine decisions along with their rationale so that the organization and employees gained a common understanding of the state of compliance review and process gaps that needed to be remediated. As the example illustrates, transparency is key for practices that support behavioral dignity.

Organizations support employees’ meritocratic dignity by recognizing and rewarding them for their contributions to the organization. In settings where employee data is used to help reshape or automate work, for example, employees can end up with harder work tasks (because easier tasks were automated or eliminated). In such cases, we have observed organizations supporting meritocratic dignity by purposefully upskilling and compensating employees to perform new, more fulfilling, and more desired work tasks and by rewarding employees for helping to make an incumbent process more efficient. In effect, the organizations are making sure that both the company and the employees mutually benefit from the effective use of employee data.

Organizations support employees’ inherent dignity by treating them as respected, valued members of the organization. This requires, for one, giving employees control over the use of their data. A helpful practice is managing employee data collection and use permissions in the same manner as customer data collection and use. Often, companies set a high bar for customer data oversight, and the technologies, controls, and perspectives used to manage customer data can be reapplied to ensure that employee data is treated acceptably. So, for example, if customer consent is required before customer data can be used for a new purpose, then an organization would also need employee approval for data use beyond established purposes.

Organizations can recognize employee dignity by (1) providing employees with clear, evidence-based direction that helps them achieve their work, (2) acknowledging and rewarding excellent performance and achievements, and (3) giving employees control over their data contributions. Such actions reflect transparent, valuable exchanges between the employer and employees that grow trust over time.

Make Dignity Core to Employee Data Use

As organizations continue to collect and use 5-W data in new ways, leaders should anticipate ongoing attention to employee dignity as a fruitful part of enterprise data governance processes. Such attention can help organizations treat employees as people at the same time that employees’ work behaviors contribute to progress in automation, task redesign, and future-ready pursuits.

To assess your organization’s state of employee data use, ask these questions:

• What 5-W data is in use? How accurate and complete is our 360-degree employee view?
• Are employees aware of the 5-W data that is being captured, and do they consent to its use? Do they have options regarding which data is gathered?
• Do employees see their own data, and do they benefit from insights derived from their data?
• Do employees understand how and why their data benefits the organization — and how and why their data benefits them both as part of the organization and personally?