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A Comprehensive Framework for Enterprise-Scale Data Validation and Cost Reduction on Cloud Platforms

Author(s) Sai Kishore Chintakindhi
Country United States
Abstract Cloud platforms often face major challenges with data validation at the enterprise level—inefficiencies and soaring costs are the name of the game here. This dissertation jumps right into the problems, outlining a framework designed to boost performance and slash expenses in one go. By mixing together quantitative data like current validation expenses, cloud performance numbers, and various organizational needs, the study uncovers best practices that, quite frankly, make resource use more efficient across different cloud setups. Generally speaking, the analysis shows that when you put this framework into action, you might see validation costs drop by as much as 30%—and that’s while data quality and processing speeds get a healthy lift, especially in healthcare. The impact is pretty clear: organizations not only get a new, structured way to handle data validation but also free up funds that can be reinvested into innovation and better patient care. This idea, that effective data management is deeply tied to improved clinical outcomes and overall efficiency, isn’t just a healthcare story—it spills over into other sectors juggling huge amounts of data. All in all, the research sets the stage for a more strategic, cost-effective approach to data validation on cloud platforms that might just redefine operational practices across industries.
Field Engineering
Published In Volume 6, Issue 1, January 2025
Published On 2025-01-06
Cite This A Comprehensive Framework for Enterprise-Scale Data Validation and Cost Reduction on Cloud Platforms - Sai Kishore Chintakindhi - IJLRP Volume 6, Issue 1, January 2025. DOI 10.70528/IJLRP.v6.i1.1573
DOI https://doi.org/10.70528/IJLRP.v6.i1.1573
Short DOI https://doi.org/g9mvrq
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