At a time when artificial intelligence is deeply permeating policy, operations and public services, data quality is the backbone of reliable and legitimate digital decision-making. AI systems learn, reason and predict based on data - but when that data is incomplete, outdated or biased, not only are outcomes compromised, but also the legitimacy and ethical integrity of the technology. Errors in data sets can lead to discriminatory algorithms, incorrect analysis and reputational damage, with major social and legal consequences.

In this file "Data Quality & Compliance," we explore how organizations can structurally ensure data quality in an era of AI regulation, digitization and increasing public scrutiny.

We cover such topics as:

• The core principles of data quality: accuracy, completeness, timeliness, consistency and traceability.

• The relationship between data quality, bias, explainability and reliability of AI systems.

• Governance, validation and auditing as tools to ensure data quality in the AI life cycle.

• The legal frameworks and responsibilities under the AVG, the upcoming AI Act and sectoral standards for data responsibility.

• The role of data controllers, data stewards and compliance officers in monitoring data flows and risk management.

• Practical lessons from incidents in which organizations faced legal or ethical consequences of poor data quality.

In addition, this dossier focuses on current issues such as the use of synthetic and generated data, the integrity of training sets, and the growing dependence on external data suppliers. Thus, we bring the connection between technology, governance and trust into sharp focus.