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所在平台: Udemy |
课程主页: https://www.udemy.com/course/knowledge-graph-benefit-case/
课程评论：没有评论
**课程名称：** 知识图谱效益案例 **课程概述：** 本课程旨在探讨知识图谱在现代企业决策和数字化转型中的关键作用。传统企业依赖流程和样本审查来做出决策，但随着近85%的数据为非结构化数据，这种方式已不足以满足实时决策和监管要求。知识图谱技术提供了一种新的解决方案，利用图数据库映射数据间的关系，将原本分散、非结构化的信息转化为可驱动实时决策的资产。 课程内容将重点关注： * **知识图谱的定义：** 阐述知识图谱是什么，以及其核心概念。 * **知识图谱的应用场景：** 深入理解知识图谱在应对非结构化数据、自动化审查、实时决策、合规性、外包决策以及改善客户服务等方面的具体用例。 * **知识图谱的效益分析：** 学习如何定义和量化知识图谱解决方案带来的商业效益。 * **复杂性与效益的权衡：** 分析实施知识图谱所需的相对复杂性，并将其与预期的收益进行对比。 * **图分析体验策略：** 最终，学员将掌握如何制定有效的图分析体验策略，以最大化知识图谱的价值。 通过本课程的学习，您将能够清晰地描述知识图谱，准确地阐述其解决方案的用例，并有效地定义其商业效益，从而为企业在数字化时代实现更智能、更高效的决策奠定基础。
In the past, Organizations relied heavily on process reviews and sample reviews to drive their conclusion. Today, nearly 85% of the data they review is unstructured. Traditional IT function automates 15% but leaves the 85% to an army of humans. Digitization is seeking decision-making in real-time.Regulators are seeking review of 100% transactions. Samples are no longer sufficient Outsourcing is involving external organizations to decision-making. Policies and procedures must be digitized. Customer engagements are aggressively looking for fast turn-around on quality decision making utilizing all available structured and non-structured data. They want support for proactive online decisions to improve customer service.. The concept of using Graph databases to map relationships digitally started seeing popular usage in business around 2015. With increased compute power, in-memory computing, multi-processing and agreed-upon standards moved the concept from academics to real-world uses in business and enterprise computing. After completing this course, youu will be able to * Describe what is Knowledge Graph* Understand and articulate the use case for knowledge graph solution* Define benefit case of knowledge graph solution* Define relative complexity and benefits of knowledge graph solution andFinally, You will learn how to strategize a Graph analytics experience
