Datatelah

Datatelah is a contemporary concept in data management and analytics that refers to an integrated approach combining data engineering, analytics, and narrative explanation of results. It envisions a modular framework in which data provenance, transformation, and interpretation are treated as interconnected layers.

The term is used mainly in speculative, academic, or industry discussions about improving reproducibility and transparency in data projects. It emphasizes end-to-end traceability of data from source to insight and the ability to present findings as coherent stories for diverse audiences.

Core components typically include data ingestion and lineage tracking, metadata management, transformation pipelines, analytics modules, and a storytelling or reporting layer that translates computed results into human-friendly narratives, dashboards, or documents. Governance, access control, and data quality checks are integral.

Applications span enterprise data platforms, research reproducibility initiatives, and data-literacy programs, where teams must explain complex analyses to non-technical stakeholders. Proponents argue datatelah can reduce ambiguity, improve collaboration, and provide audit trails, while also highlighting the trade-offs between flexibility and standardization.

Limitations and challenges include the lack of formal standards, potential overhead for maintaining lineage across varied tools, and risks of oversimplification in storytelling. As a relatively new concept, its adoption varies by organization and remains an area of ongoing development.