Navigating the data dilemma: Can organizations build trust while using workforce data to improve performance?

There is a wealth of newly available and largely untapped data generated by the workforce in the course of their everyday work. This can help organizations improve their business with greater agility, innovation, and customer satisfaction—and at the same time, help workers be happier, safer, more employable with relevant skills, and enjoy a fairer, more inclusive experience at work, increasing trust between the two entities.

But organizations that rush to adopt these new tools risk alienating their workers and undermining the very productivity they are attempting to optimize.And perhaps more critically, they may miss out on opportunities to use work and workforce data to help create organizational impact beyond the individual worker and potentially build trust across the board.

As people and machines increasingly interact, they leave an ever-expanding digital trail of work that can be mined to create value. These data trails can then be analyzed by new tools such as algorithms that judge the quality of a software developer’s code or writer’s article, the emotional tone of a call center employee interacting with a customer, worker behaviors that shed light on an organization’s culture and sense of equity, the physical safety of workers in the field, or how people are interacting with one another.

Whether applying analytics, machine learning, or human judgement, sense-making is what allows organizations to convert data into insights, actions, and decisions that have the power to improve everything from innovation to agility to worker performance and well-being.

How can organizations chart a course toward responsible use of work and workforce data and technology that creates trust and value for their workforce, while navigating trade-offs between risk and opportunity? There are two important keys to unlock this new value across an organization: transparent data-collection practices that build trust through consent and a data strategy that prioritizes value creation for employees and workers.

The trust factor: Work and workforce data collection doesn’t have to be a source of conflict

Harnessing new data on work and the workforce and turning it into insights that can improve an organization’s performance can be both a promise and a challenge. The rewards can be significant, but so can the risks. Done right—in a transparent, responsible way that benefits both workers and organizations—it can increase fairness and trust. But done wrong—where biased algorithms lead to poor decisions and workers fear rights violations—it can damage fairness and trust, with impacts to an organization’s brand, reputation, and financial performance.

Deloitte defines trust as “the outcome of high competence and the right intent.” But the relationship between trust and data-collection initiatives can be nuanced. Trust is often fragile and easily lost. Missteps can be costly, even for organizations where there is already a strong trust that exists between the worker and the employer. For organizations that are already facing a culture of mistrust, that hill can be an even steeper climb.

When respondents to Deloitte’s 2023 Global Human Capital Trends survey were asked to identify top barriers to realizing value from worker data, 27% of respondents cited culture, making it the most common barrier. However, “culture” may be a broad proxy for misaligned values or disagreements over if, how, or when worker data should be used.²

Indeed, Deloitte’s position is that any type of employee data collection should be done with the consent of employees and in a transparent way. This becomes particularly relevant as AI increasingly enables us to take advantage of this data.

One step toward overcoming the trust barrier is for organizations to implement a framework for responsible data collection, use, and management that prioritizes the core principles of transparency, worker empowerment, outcomes-based measurement, and shared value.

Default to transparency first

Being transparent with workers about what an organization is collecting and why can help mitigate the risk of potential backlash and elevate trust. Gartner found that only 30% of employees surveyed were comfortable with their employer monitoring their email. But in the same study, when an employer shared what they would be monitoring and explained why, more than 50% of workers reported being comfortable with it.³

Organizations should also be transparent about their data-security and governance rules—how (and how long) the data is being stored, whether the data will be shared in individual or aggregated form, and who (internally and externally) will have access to the data. In fact, safeguarding privacy was identified as a top priority among workers surveyed about pressing issues for their generations to solve.⁴ But only 28% of workers strongly agreed that their leadership understands the implications and responsibility of protecting data confidentiality and ensuring security.⁵ Limiting data storage time, deleting incidental data, and using advanced technologies can help keep data secure. Some new technologies can even provide insight without acquiring or transferring the data itself.⁶

Regional, local, and global regulations around data privacy will likely continue to guide how organizations collect and use data and the internal policies they develop to manage it. For example, an organization with employees distributed globally may be able to gather employee data in some countries but not others due to regional or local privacy laws. The organization may need to choose a path: maintaining different policies for different locations or creating a blanket policy that adheres to the most restrictive regulations.

Give workers ownership of their data

The simple act of giving workers the opportunity to choose to share their data, and choose what data to share, can be a critical step toward building trust. Opt-in policies can be ideal, although there may be cases, such as digital security monitoring, where opt-in is not feasible. But when opting in makes sense, information should be straightforward and easy to understand (not buried in legalese).

Can your workers access the data being collected about them? By providing a platform to see the data collected on them as individuals, as well as the aggregate data collected on them as part of teams or groups, organizations can provide greater transparency and build trust by giving employees an opportunity to ensure their data is correct and challenge it if they feel it isn't. A platform can also help workers control access to their data, decide if it can be used for purposes other than intended, and receive data analysis drawn from their data.

It’s important to help workers control how their individual, personal data is shared across an organization. For example, while workers may be open to sharing their individual skills data with the entire organization, they may not be so open to sharing their individual data about their performance or emotional states. Aggregating and anonymizing data before sharing can help, as well as involving workers in creating the organization’s overall data-privacy policies.

Measure the right things

With an exponentially growing volume of data available, focusing an organization’s efforts on a deep understanding of what data should be collected—not just what can be collected—and linking those initiatives to specific organizational goals and outcomes, can allow the organization to tap into important sources of value that might otherwise be left on the table.

Organizations should ensure that the data they collect reflects the metrics they are seeking to capture accurately and reliably. Case in point: Ameta-studyconcluded that it is impossible to judge emotion by simply looking at a person’s face, using technology like facial recognition.7 Likewise, productivity likely cannot be accurately evaluated simply by measuring one’s activity. Productivity, instead, should be measured with specific outcomes. Be careful of becoming more enticed by the data and numbers than the actual goals. Always ask: Just because it can be measured, does it really need to be, and if so, why?

Share responsibility, share value

Studies show that workers are willing to share data—but there are some conditions. According to a study published in Harvard Business Review, 90% of employees surveyed are willing to let their employers collect and use data about them and their work, but only if it benefits the employee in some way.⁸ A more recent Deloitte study on skills-based organizations confirmed that the vast majority of workers surveyed are willing to share data on everything from their skills, interests and passions, preferences, and performance on informal work in projects or internal gigs not directly related to their job, but many say it would depend on whether their employer offered them benefits in return (figure 1).⁹

Instead of designing initiatives that collect and use worker data as a top-down exercise, consider involving workers from the start in cocreating the data collection practices themselves. This could include involving them in choosing what metrics will be useful and relevant in improving their experience at work and collaboratively deciding how the data can be used to inform action by AI or human judgement.

Who benefits from data collection? Start with your workers if you want to realize organizationwide impact.

When an organization uses the data they collect about their workforce to benefit everyone—individual workers, teams and groups, the organization, and society as a whole—they are creating shared value. The value created at each level can flow between them, reinforcing and amplifying the value created at other levels. By designing data-collection efforts with worker benefits in mind from the start, organizations can create new value for workers while realizing performance impacts across the organization (figure 3).

Consider worker happiness as an example. In addition to the individual benefits of being happier at work, such as improved wellness and performance, worker happiness could also improve teamwork and social encounters at the group level.¹⁰ It has been linked to improved engagement, productivity, and culture, and reduces attrition risks at the enterprise level.Japan-based technology firm Hitachi experimented with improving the happiness levels of its employees using wearables and an accompanying  mobile app that offered employees suggestions for increasing feelings of happiness.¹¹ During testing, the psychological capital of workers rose by 33% and profits increased by 10%. Sales per hour increased 34% at call centers, and retail sales increased by 15%, demonstrating how creating value at the employee level had far-reaching impacts on the business.¹²

How does an organization know what data it should be collecting and measuring to create value for its employees?When workers feel like their data is being used to judge them, and it leads to a potential dismissal or other penalty, distrust and other overall negative consequences can result. In general, data should be used to help workers learn, grow, make their jobs easier, find meaning or happiness at work, and realize their potential. Consider these opportunities for creating shared value when developing a data strategy with workers in mind.

Provide opportunities to improve job performance

• When used with consent, employee-communication data and internal social-network analysis can help identify the activities, roles, actions, and workers that create the most value in your organization. Identify “rockstar” performers and use AI to help others learn from them.
• Use audio or video analytics (e.g., of a sales or call center employee, or a retail clerk) or work and collaboration data to identify behaviors that help drive results and use this data to create algorithmic coaching personalized to the employee. Personalized insights can be used to help improve skills such as communication, focus, self-awareness, and time management.

Personalize learning and development

• Track how well people are learning through virtual reality/augmented reality (VR/AR) that captures reactions in real time or through neurotechnology wearables that use AI to deliver adaptive learning tailored to the individual.
• Use wearables like AR goggles to overlay learning on top of physical reality as people move (e.g., providing directions on how to place objects in fulfillment centers).
• Use information collected about what workers are working on to recommend relevant, just-in-time learning opportunities and suggest others with whom they may want to connect.
• Measure the impact of learning and track behavior change from social discussions, metaverse interactions, videos watched, articles read, use of performance support tools, and calls with mentors.

Build employee leadership skills

• Use internal social-network analysis to help identify the presence of cross-functional leadership teams and the strength and type of a leader’s connections across the enterprise.
• Identify inclusivity by measuring an employee’s degree of listening and communication. Use video and audio analytics to infer leadership qualities like a learning mindset.
• Help growing leaders stay focused on strategic priorities by using work applications to assess time spent on various activities, comparing it to their actual priorities and goals.

Improve career mobility

• Use data on transferable or adjacent skills, interests, and worker activity to suggest which skills employees can develop to be more marketable and employable as organizations evolve. This data can also be used to match them to new opportunities, projects, learning, or roles.
• Help employees identify valuable skills and adjacent skills from project and work histories (including volunteering, military service, or other lived experiences), digital work products (e.g., code or support tickets), work applications (e.g., project systems), and text analysis (e.g., performance feedback, collaboration sites, etc.)
• Analyze external data from job and project postings, social profiles, skilled vendor industry benchmarks, and more to predictively see future skills needed and skills migrations. Help workers connect these trends to their existing skill sets to suggest learning and work experiences that can help them prepare for the future.

Enhance employee wellness

• For those who opt in, wearables, sensors in the environment, or video analytics can track body movements to reveal patterns of physical wellness.
• Detect patterns of stress, attention, and other mental states when workers opt in to using wearable neurotechnologies like headphones and AR headsets designed to measure mental state. Transparently tracking the amount of time spent on work (including after hours) through work applications can help detect potential burnout. Audio, video, and wearable data can help identify other signs of stress, as well as opportunities to help workers improve mental health.
• Data from employee communications, voice and video data, location data, or embedded sensors in the workplace can reveal relationship patterns, interactions, and socializing styles. That data can then be used to make suggestions on improving interactions and relationships with others, as well as suggesting mentors, coaches, or other colleagues an individual worker might want to connect with. When implementing these kinds of efforts, it is critical to ensure that these tools are not biased against neurodiverse individuals and those with disabilities and are implemented with consent of workers.

Support safe working conditions

• Improve safety by connecting location or biometric data from wearables to smart devices in the physical environment that enable workspaces and processes toadapt to the worker (e.g., having robots or machinery move based on a worker’s movements).
• Useneurotechnology wearables to put cognitive ergonomics¹⁶ into practice, measuring the cognitive load of workers in physical work environments and detecting and alerting overload, which can produce safety hazards, errors, and health issues.
• Wearables, smart sensors on devices or in the environment, or video analytics can track and alert workers to improper physical movements (like posture) that could lead to injuries. Use this data to feed simulation tools that can predict injuries and lead to new safety policies.

Organizations that are able to successfully tap into work and workforce data without alienating their employees will likely be those seeking to create a new relationship with workers based on trust and prioritizing new value opportunities for their workforce. It is possible to reconcile worker-privacy concerns with organizational needs for data to improve performance—but it could require a transparent data strategy that gives workers ownership of their data and builds organizational trust. Combined with a focus on understanding what data shouldbe collected—not just what canbe collected—and linking those initiatives to specific organizational goals and outcomes, organizations will better be able to make the most of important sources of value that might otherwise be left on the table.

Learn more in our full report, Beyond productivity: The journey to the quantified organization.