Data and Power:
From Factory Registers to Digital Control

The General Register contains a lot of information about child laborers in factories. Who has the power to record and interpret this information? Do the young laborers have the ability to control how their data is recorded? The underlying power inequalities within these records sparked our interest and led to several research questions:

1. What is the purpose of data collection?

2. Who controls the data, and how are power structures maintained or reinforced through this data?

3. How does data capitalism affect broader groups?

Labor laws serve as the basis for the state to supervise the labor system and protect the basic rights of both parties to employment. The Factory and Workshop Act 1901, which is related to this study, requires the recording of basic personal information of child and adolescent workers, work qualifications provided by schools, and accidents. For a large portion of recorded history, data has been gathered and kept, with governments managing through information records (Kitchin, 2014). However, this also reveals the dependence on data in the labor governance process. Due to the imperfection of the overall data recording system of society, the data records at that time were generally distorted, and regulators often found it difficult to implement effective supervision (Kirby, 2017).

In the digital age, information collection is no longer limited to paper registration but is completed through digital technologies such as algorithms and sensors. Compared with the manual records based on paper records in the past, the data recorded by current machines seems to be more "neutral" and "objective". However, this "neutrality" often masks the problems of algorithmic bias and transparency in data use (O’Neil, 2017; Weerts et al., 2024). There may be differences between the original purpose of data recording and the final use of the data. For example, according to the Working Time Regulations 1998, the original purpose of recording employees' working hours is to avoid direct exploitation of employees, such as overtime violations, but at the same time, these data may become implicit standards for companies to evaluate employees (Taylor, 2004; Braverman, 1998).

The above alienation of data reflects that in the process of labor governance, data not only exists as a management tool but also gradually evolves into a part of power operation (Sadowski, 2019). Especially after the rise of the platform economy, the scope of data collection has far exceeded the original necessity of work management. Taking platforms such as Uber and Amazon as examples, data such as workers' order acceptance rate, cancellation rate, geographic location, and even emotional state are systematically collected to formulate implicit assessment standards or affect labor conditions (Rosenblat and Stark, 2016). At the same time, workers have extremely limited control over their own data. They lack the right to know and the complaint mechanism for data collection, use and evaluation mechanisms (Collins and Atkinson, 2023). This imbalance further exacerbates the power asymmetry between workers and employers in the information age. And also reminds us that digital data records do not necessarily lead to fairer labour governance but may reshape the labour discipline structure in a more hidden and systematic way.

In the contemporary digital environment, this disciplinary power derived from data control is exerted not only on workers but also on a wider range of consumer groups in different forms. Cluley and Brown (2015) pointed out that data-based marketing activities are no longer just a technical means of exerting influence, but have transformed into a force that actively controls individuals and groups through data classification, prediction and guidance of behaviour. Platform companies and third-party data brokers (such as CoreLogic and Epsilon), as data controllers, continue to collect, aggregate, analyze and resell personal data (Glasgow, 2018). Large commercial databases can "ingest" data from multiple sources (such as purchase history and website cookies) and calculate and archive thousands of data points for each individual (Federal Trade Commission, 2014). Based on this data, the platform can build a highly detailed digital portrait of individual consumers, systematize their behavior patterns, consumption values and risk tendencies, manage them accordingly, and incorporate them into the credit assessment system. Similar to the implicit discipline faced by workers, this process is usually not open and transparent to the public. What consumers ultimately perceive is just the “credit score” presented on the web page, and in order to avoid practical inconveniences such as service restrictions and price increases due to lower scores, individuals have to consciously adjust their own behavior to conform to the standards and expectations behind the data system, thereby invisibly accepting the power of data discipline.