Part 7/8:
The ultimate metric for data architecture success is the value it delivers. To this end, efforts are directed toward reducing unnecessary data duplication, optimizing data pipelines, and delivering insights faster—thereby improving time-to-market and decision-making agility.
Operational and Organizational Aspects
Implementing these architectures involves defining operating models where domain owners are held accountable for data quality and governance. These owners act as product managers for their respective data, ensuring continuous improvement and adherence to standards.
Moreover, cross-functional collaboration between data engineering teams, business units, and governance bodies is essential to maintain consistency, compliance, and responsiveness to changing business needs.