When 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.
The new fundamentals
Build trust and seek mutual benefits. Success will require building a relationship of trust between workers and their organization, government, or third party to use data in mutually beneficial ways. We are seeing signs that organizations are beginning to make decisions that are mutually beneficial. When asked in the Deloitte 2023 Global Human Capital Trends survey to identify the top benefits from their organization’s approach to using worker data, the top response was increased worker engagement and well-being of the workforce. While organizations might want to control their worker data, many are ultimately using that data for their workers’ benefit—as well as the organization’s. For example, as the major impacts of the pandemic subsided, some organizations used worker interaction data to conduct network analysis and inform development of hybrid work practices.5 Additionally, several startups are leveraging artificial intelligence (AI) to identify patterns based on worker interactions and communications and advise organizations on burnout risks.6
Data can also help workers improve their performance, as we discuss in more detail in our “Powering human impact with technology” trend. A global technology company is using cognitive AI tools to analyze worker data generated by their global sales teams to determine why some sellers do better than others and to make recommendations to improve the knowledge and win rates of their sales force.7 By identifying patterns of behavior missed by humans, AI can recommend the next best course of action or even quick learning modules that improve the success of their sellers.
Embrace workers’ desire to control their data. As workers recognize the value of their personal data, they are expecting more control of that data and influence over how it’s being used. According to the Deloitte 2023 Global Human Capital Trends survey, a significant majority of organizations (61%) describe their existing data ownership structure as either shared or worker-owned. This represents a significant departure from the traditional model of full control by the organization. Organizations must accept this new reality instead of clinging to their old model of controlling all the worker data that they collect.
Use expanded worker data to create more, and mutual, value. Fortunately, expanded availability of worker data opens up new opportunities to create business value for everyone involved. Learnings can be taken from the trajectory of customer data, where, as expanded customer data became available, organizations developed sophisticated strategies and analytics to generate deep and valuable business insights from that data. Currently, leaders who responded to the Deloitte 2023 Global Human Capital Trends survey say the worker data that is most valuable to their organization is basic productivity data (i.e., data to make people work harder and/or smarter). However, over the next 2–4 years, more advanced data is expected to become valuable, including behavior data, personality data, and data about professional relationships and interactions. Along the way, rising worker agency will prompt organizations to hone their methods and balance business-oriented insights with insights that benefit the workforce.
From a technology and operations perspective, organizations must build on their current focus on establishing clean, accurate data to find ways to derive new insights by asking different questions, such as “How do we assess risk and reward and decide what data to gather?” and “Are we using our data to its fullest potential to serve our business and our workers alike?”
To achieve any degree of worker trust and control over data, organizations need to design the overarching data architecture, including workforce data policies that are transparent, accessible, and ethical (i.e., biometric data). Also, they need to recognize and reward teams (i.e., leveraging blockchain) for using data in ways that enhance individual and team performance. This helps reinforce data analysis as a core competency.
Current experiments: What leading organizations are exploring
- Schlumberger is using AI to help manufacturing workers improve performance and reduce worker fatigue.8 Its Center for Reliability & Efficiency in Denton, Texas, collects, aggregates, and anonymizes video data and then uses AI to look for patterns. The data is never used to monitor how individuals work; however, any worker can privately opt in to see their own performance data. The data creates valuable benefits for workers and the organization—for instance, the company has used it to give workers more frequent but shorter breaks to combat productivity-sapping fatigue.
- Telstra, Australia’s largest telecommunications company, has given its workers the ability to edit their own career data.9 The company maintains an internal site called MyCareer, which is used to store career and skills data, and enable the organization to more effectively match talent to work. Workers are able to manage their own data and are given the ability to challenge any incorrect or incomplete inputs.
- ABN AMRO is on a journey to bring together all HR and enterprise data to create an integrated IT and data landscape, which will enable machine learning and nudges for employees on career-related topics like development or open roles.10 The Netherlands-based global bank is also rewriting its privacy statement to ensure more transparency for employees. A dashboard that includes data on employee experience, diversity, workforce, talent acquisition, and learning is accessible to all employees; this dashboard is also used by business leaders to make decisions regarding their workforce.
The path forward