Cross-Domain Data Warehousing: Comparative Analysis of Implementation Strategies Across Life Sciences, Banking and Telecommunications

Data warehousing approaches differ substantially between businesses and industry verticals because of different regulatory regimes, data profiles as well as operational costs and benefits. Law- Life Sciences Compliance-driven architectures, including tight validation and audit functionality to ensure compliance with the FDA's 21 CFR Part 11 regulations regarding e-signatures and auditable logs, as well as GxP segregation. Banking is all about processing secure transactions quickly, or not so quickly, if they’re the kind of financial analytics required by AML and Basel III through Sarbanes-Oxley compliance requiring immutable logs; sub-second query times to look suspicious as soon as a check swipes on an ATM. Double, where tens of billions of CDRs are processed daily using distributed frameworks Telecommunications deals with high-velocity network data through streaming architectures tailored for performance monitoring (through34 & 40) Telecom answering the call: How telecom companies can guide their Big Data strategy and directly tied to industries such as telecommunications. Universal models such as dimensional modelling, master data management, and metadata governance are industry independent with domain-specific variations dictating success of implementation. Technology stacks capture operational needs where Life Sciences is biased towards hybrid cloud implementation, Banking towards low-latency engines and Telecommunication applications towards cloud-native streaming systems. Cross-domain learning discloses transferable activities such as streaming techniques that inform fraud detection, governance rigor that fortifies compliance programs, and agile methodologies that adjust to regulated environments. The implementation issues such as validation schedule to scalability demands demand specific solutions to deal with regulatory limitations in stages, a hybrid model between real-time and batch processing, automated data quality models, and multi-functional teamwork to keep the requirements of the business.