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SSON:数字化数据-创建跨企业无缝数据流的新方式(英文版)(10页).pdf

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1、Digitizing DataA New Approach to Creating Seamless Data Streams across the Enterprise Sponsored byTable of ContentsIntroduction3WhyisDigitizedDataImportant?4Solutions4EnterprisesandCMR5PersistentDataChallenges6CASE STUDY:Digitizing and Processing 170K+LoanApplicationsDuring COVIDfora90%AccuracyRate7

2、DigitizingDatawithCMR,RPAandOCR8Conclusion92DIGITIZING DATA:A NEW APPROACH TO CREATING SEAMLESS DATA STREAMS ACROSS THE ENTERPRISE 2022 SSON IntroductionPast responses to crises such as the one in 2008 have involved a shift from low cost labor to higher value work,but nothing short of digitization w

3、ill secure anti-fragility in the present.With human labor susceptible,businesses need a digital workforce for resilience,and survival.Technologies for data capture such as robotic process automation(RPA)and intelligent automation(IA)uncover patterns that saved one global payment company$140 million.

4、Given digitizations laud,the near future will see that more than under a third of organizations,a figure drawn by a McKinsey survey,implement intelligent technologies,and weather the storm.Unstructured data including images,handwriting,signatures,and mobile content pose a challenge to capturing and

5、digitizing data.Cognitive Machine Reading(CMR)and Machine Learning(ML)can be a solution to this problem.They outpace Optical Character Recognition(OCR),which captures only structured data.CMR classifies data across dozens of languages and leads to end-to-end process automation.Garagiola defines end-

6、to-end process automation as“using a machine or even a collection of machines or decision trees to execute those human steps for you.Leaving you as a human to perform more value-added activities versus perhaps some of those processes that are less than worthy of a human performing them or time sucke

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本文主要讨论了数字化数据的重要性以及如何通过认知机器阅读(CMR)、机器人流程自动化(RPA)和光学字符识别(OCR)等技术来创建无缝的数据流。文章指出,自动化过程需要更少的资源,可以提高质量,加快处理速度,并减少对客户的响应时间。然而,自动化只有在对数据进行结构化、数字化后才能有效。 文章中提到,未结构化的数据,包括图像、手写、签名和移动内容,对捕获和数字化数据构成挑战。CMR和机器学习(ML)可以作为解决方案。CMR可以成功摄入各种数据格式,从表格、复选框到手写、草书、图像和签名,从而实现连续和无中断的端到端过程自动化。 文章还提到了一个案例研究,一家美国最大的金融机构在COVID-19期间使用XtractEdge平台和其计算机视觉能力来加速贷款审批,实现了大约90%的数据准确性。 总的来说,文章强调了数字化数据的重要性,并展示了如何通过CMR、RPA和OCR等技术来实现这一目标。
企业如何利用CMR实现端到端流程自动化? 为什么说OCR在处理非结构化数据方面存在局限性? 企业如何通过智能文档处理提高客户体验?
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