Big Data
Big Data refers to datasets that exceed the processing capacity of traditional single-machine database systems and require distributed computing architectures for storage and analysis. These datasets are characterized by three primary dimensions: volume (measured in terabytes to petabytes), variety (combining structured databases with unstructured text, images, video, and sensor streams), and velocity (continuous or near-real-time generation). The fundamental challenge is not merely the size of the data, but the technical and organizational capability required to extract actionable insights from such scale and complexity.
Storage and Processing Architecture
Managing Big Data requires distributed storage systems that partition data across multiple servers and processing frameworks that parallelize computation. Technologies such as Hadoop Distributed File System (HDFS) and cloud storage platforms enable horizontal scaling of infrastructure to handle load.
Governance and Political Dimensions
Beyond technical infrastructure, the management of big data involves complex governance structures that shape data access, control, and usage within the platform society.
- Micheli - Emerging models of data governance identifies four distinct models of data governance emerging from the interaction between big data infrastructures and digital platforms.
- These models highlight the shift from purely technical data management to data politics, where governance strategies determine who controls the infrastructure and benefits from datafication.
- The integration of big data into data infrastructure requires policy frameworks that address the power asymmetries inherent in corporate-dominated data ecosystems. Managing Big Data requires distributed storage systems that partition data across multiple servers and processing frameworks that parallelize computation. Technologies such as Hadoop Distributed File System (HDFS) and cloud storage platforms enable horizontal scaling o
Data Governance and Trustworthy AI
Beyond technical infrastructure, the effective utilization of Big Data requires robust governance structures to ensure data quality, privacy, and ethical compliance, particularly when feeding artificial-intelligence models.
- Organizing for Trustworthiness: Janseen - Data governance Organizing data for trustworthy Artificial Intelligence emphasizes that reliable AI outcomes depend on structured data governance frameworks that manage data-lifecycle-management from ingestion to disposal.
- Algorithmic Governance: Effective Big Data management extends to algorithmic-governance, ensuring that automated decisions derived from large-scale analytics are transparent, accountable, and free from bias.
- Open and Linked Data: The integration of open-data standards facilitates interoperability and trust in Big Data ecosystems, enabling secure information sharing between entities while maintaining regulatory compliance.
- Framework Alignment: Modern Big Data strategies must align with evolving regulatory-compliance and ethical guidelines to prevent misuse of personal data and ensure responsible data-analytics practices.