Data Management
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Growing significance of augmented data management

Data volumes are growing rapidly, increasingly generated through automated means, and AI is now accelerating this ongoing trend. Additionally, data sources such as scientific instruments, large sensor networks, and the IoT (Internet of Things) devices in general are becoming more prevalent. Managing these data volumes, extracting information, generating insights, and preserving data value is a challenging task.

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Augmented data management, a form of AI-based automation, is evolving and radically reducing the manual tasks of data management teams, such as building data orchestration pipelines, assessing data quality, and running repetitive data integration workflows.

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To properly harness the growing use of AI for data management, data quality is essential. Therefore, AI is recognised as an important pillar for more content-aware and data quality-aware data management solutions.

Maturity

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More diversified access to large AI models Increase in co-evolution between hardware and AI Collaboration between humans and AI Democratisation of XR content creation using generative AI Public investments in XR continue to increase Standardisation of data space architectures for secure and trusted data sharing Growing significance of augmented data management Trusted digital recognitions are emerging Digital Trust Frameworks: from hierarchical to distributed trust Emerging dualuse of AI in cybersecurity More accessible privacy-enhancing technologies to share data Rising pressure to prepare for post-quantum cryptography Emergence of measures for secure Internet of Things The growing need for security protocols to work in the cloud Growing investments in the European cloud infrastructure & sovereignty More AI-driven hardware in data centres to manage workloads in the cloud Increasing need for heterogeneous and hybrid computing platforms Trade-offs in accuracy with approximate computing Fixed wireless access Fibre optic networks: strategic infrastructure for the knowledge economy Enhancing quantum computing robustness and potential Quantum sensing integration to other domains

Drivers

Impact

education

Education

  • AI-enabled data management can bring huge advantages to education, developing more personalised learning content, supporting teachers in the assessment process and supporting education by automating administrative tasks.
Research

Research

  • Next to how AI is transforming how research is being done, augmented data management can automate many repetitive data management tasks in enriching metadata, quality checking, and developing and enabling more content-aware data management solutions for researchers while preserving the value of the data.
Operations

Operations

  • Augmented data management only works effectively on quality data. To prevent augmented data management systems from being trained with low-quality data, data managers need to be semantically skilled (on metadata, semantic vocabularies, and semantic thesauri) to ensure that high-quality data is used.
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