In the AI era, organizations are eager to harness innovation and create value through high-quality, relevant data. Gartner, however, projects that 80% of data governance initiatives will fail by 2027. This statistic underscores the urgent need for robust data platforms and governance frameworks. A successful data strategy outlines best practices and establishes a clear vision for data architecture, ensuring data remains accessible, secure, and ready for advanced analytics and AI.
By investing in modern data platforms, fostering a data-centric culture, and implementing strong governance, organizations can fully leverage data and AI to drive transformative outcomes belarus rcs data and stay competitive in an increasingly data-driven landscape.
Defining a Data Platform
A data platform is an integrated system that manages the entire data lifecycle from ingestion to analysis, serving as a unified foundation for collection, storage, processing, and access. Evolving beyond traditional warehouses, modern data platforms now support real-time analytics and machine learning (ML) to meet growing demands for flexibility.
A modern data platform architecture consists of four core layers: infrastructure, data management, processing, and access. The infrastructure layer includes storage, compute, network, and security, forming the platform’s physical foundation. Data management oversees metadata, data quality, master data, and lifecycle processes. The processing layer supports both batch and real-time analytics, while the access layer provides APIs, query tools, and self-service access, enabling secure, scalable, and efficient data retrieval.