Denodo Technologies > 实例探究 > Deploying Data Virtualization at an Enterprise Scale – A Journey towards an Agile, Data-Driven Infrastructure

Deploying Data Virtualization at an Enterprise Scale – A Journey towards an Agile, Data-Driven Infrastructure

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公司规模
1,000+
国家
  • Worldwide
产品
  • Denodo Platform
技术栈
  • Data Virtualization
实施规模
  • Enterprise-wide Deployment
影响指标
  • Cost Savings
  • Productivity Improvements
  • Revenue Growth
技术
  • 平台即服务 (PaaS) - 数据管理平台
适用行业
  • 半导体
适用功能
  • 商业运营
服务
  • 数据科学服务
关于客户
One of the largest semiconductor companies in the world, “The Company” boasts more than 100,000 employees worldwide, including more than 6,000 IT professionals, and The Company operates 61 large data centers around the world. The Company manufactures a wide variety of semiconductors for personal computers, handheld devices, and wearables, and with the proliferation of mobile devices, The Company is redefining the next generation of semiconductor products.
挑战
The Company, being one of the largest multinational companies, with offices, data centers, and fabrication facilities all over the world, developed a heterogeneous ecosystem of tools and technologies over time, giving rise to a complex, distributed data ecosystem. As SaaS applications became mainstream, SaaS adoption within The Company skyrocketed; data oriented nomenclature grew inconsistent across business units, and the office of the CIO was under tremendous pressure to deliver business-friendly, consistent information with the lowest total cost of ownership (TCO), as well as products and services with enterprise grade security and privacy, and the fastest time-to-market (TTM). The physical EDW fell short of its promise. The Company also wanted fast, post-M&A data integration, distribute new selfservice entitlements to downstream acquisition applications, and distribute directory identities to the acquisition directory. The Company also wanted to architect its enterprise data access layer for a single point of entry for HR and supplier data consumption, seamless support for data source migration, and scalable interaction among on-premises and cloud data sources. As its IT culture was not historically suited for reusable information, The Company experienced and egregious misuse of resource time and effort. As challenges became overwhelming, The Company searched for an agile data access solution.
解决方案
The Company started its journey with data virtualization as the data access layer. Initially The Company rolled out the Denodo Platform for five of its business units and achieved success with the horizontal, IT-driven approach of establishing a logical data warehouse architecture. The Company created a logical data access layer, based on data virtualization, in which its data security, data privacy, and data governance rules were centralized, and The Company also established semantic uniformity so that cross functional teams could collaborate more effectively. The logical data layer could easily integrate The Company’s HR and Supply Chain departments’ cloud and SaaS applications, filling gaps in its services portfolio. As a next phase, The Company rolled out data virtualization as its enterprise wide data access layer, accessed by more than 400 developers and thousands of business users on a regular basis. Through the data virtualization layer, The Company also standardized API registered services, lightweight data governance, and the enterprise data model.
运营影响
  • With data virtualization in place, The Company accelerated the time of its average services deployments from 180 hours to 8 hours.
  • Some departments reported that service disruption was prevented during migration, saving millions of dollars.
  • Time-to-market (TTM) for data delivery to business users has been reduced by 90%
  • The Company is able to generate tens of millions of dollars of additional revenue by significantly cutting down on TTM.
  • Post-merger data integration has been greatly accelerated.
数量效益
  • Average services deployments time reduced from 180 hours to 8 hours
  • Prevented service disruption during migration, saving millions of dollars
  • Reduced time-to-market (TTM) for data delivery to business users by 90%
  • Generated tens of millions of dollars of additional revenue by significantly cutting down on TTM
  • Greatly accelerated post-merger data integration

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