返回 ITIL 4理论与实践整体知识体系中文版发布文件汇总


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知识管理实践的目的是在组织上维护和改进有效，高效和方便地使用信息和知识。


知识管理实践是一种将信息和组织知识资本转化为面向员工和服务消费者的持久价值的方式。实践旨在在合适的时机向合适的人提供合适的信息，从而使构建成为不断发展的环境，其中：


●        吸收性容量不断改进
●        人们渴望学习新知识，不学习旧知识，并获得并分享他们的体验和洞察力
●        决策能力得到改善
●        存在自适应变更文化
●        性能或绩效改进，支持组织策略
●        组织始终使用数据驱动和洞察力驱动的方法。


通过为知识资产管理建立集成且系统的流程，构建高互操作性知识环境并使人们能够开发和共享知识，可以实现这一点。这包括了解和使用现代技术，数据/ information / 知识管理方法，根据组织愿景和需求进行培训和指导的方法。


知识管理实践为ITIL 服务价值流的每个组件做出了贡献。实践包含以下前提：
●        知识在价值流的背景中进行处理和使用。该实践已集成到价值流中，并确保有效，及时地提供信息，以满足利益相关者的期望。
●        该实践应该着重于发现和提供高质量信息（在定义的范围中可用，准确，可靠，相关，完整，及时且合规）。


2.2        术语和概念
        
有几个概念对于在组织中建立有效的知识管理实践非常重要。这些概念是根据科学研究和实用的管理体验开发的。对于那些希望从他们可以访问的信息资产中增加价值的组织，建议使用这些概念。


2.2.1        吸收性容量


学习能力是一个人或组织的重要方面。对于组织，它由组织的吸收性容量启用和限制。吸收性容量代表组织能够识别价值的新信息，将其嵌入到现有知识系统中，并将其应用于实现业务的结果。为了创新和适应变更，组织应不断开发吸收性容量。从中吸收新知识



在组织之外并将其集成到知识系统中是复杂的，应该同时在各个级别（外部，组织，团队和个人）进行。它还应该考虑服务管理四维模型1.


2.2.2        数据和知识管理


为了表示数据，信息，知识和智慧之间的关系，通常使用数据，信息，知识，智慧金字塔（DIKW），也称为知识金字塔。但是，每个级别在管理中的划分都没有明确定义。组织选择如何命名相关的活动，以及是否应将数据管理视为知识或信息管理的一部分，还是应将重点放在原始数据的管理上的单独实体。另外，智慧通常被模糊地描述，并且不包含在结构化流程的描述中。在本指南中，没有讨论“智慧”一词，除了暗示组织应致力于将知识用于价值共创。


组织应定义并同意知识管理实践的定义和分类法，以获得有效的结果。这些定义可能会根据所涉及的数据的类型和行业而有所不同。


可以从组织的外部以及内部获取重要的决策知识。这可能包括来自社交和企业媒体中文章和帖子的信息；监视和网络摄像头，录音和物联网（IoT）设备的数据。该数据大部分是非结构化的


2.大数据管理系统已经出现，可以与大量原始且通常为非结构化的数据一起使用，并对其进行分析以预测洞察力。Big 数据分析（BDA）为知识管理带来了挑战和机遇


3.大数据通常由三个词（即大数据的3V）定义：音量，速度和变化。但是，还有更多可用的V，例如价值，准确性，有效性等等。前三个V对了解组织是否正在处理大数据或更传统的数据形式至关重要。


Vs之一是多样性。这是原始数据的来源范围和格式，以及影响和知识管理实践最多的准则，因为它代表了大数据带来的挑战


The purpose of the knowledge management practice is to maintain and improve the effective, efficient, and convenient use of information and knowledge across the organization.


The knowledge management practice is a way of transforming information and organizational intellectual capital into persistent value for employees and service consumers. This practice aims to provide the right information to the right people at the right moment to build an evolutionary environment where:


●        absorptive capacity is continually improved
●        people are eager to learn new knowledge, unlearn old knowledge, and gain and share their experience and insights
●        decision-making capabilities are improved
●        an adaptive change culture exists
●        performance improves, supporting the organizational strategy
●        data-driven and insight-driven approaches are used throughout the organization.


This is achieved by establishing integrated and systematic processes for knowledge asset management, building a high interoperability knowledge environment and empowering people to develop and share knowledge. This includes knowing and using modern technologies, data/information/knowledge management methods, approaches for training and mentoring according to the organizational vision and needs.


The knowledge management practice contributes to every component of the ITIL service value stream. This practice incorporates the following premises:


●        Knowledge is processed and used in the context of value streams. This practice is integrated into value streams and ensures that information is provided effectively and on time to meet the stakeholders’ expectations.
●        This practice should focus on discovering and providing high-quality information (available, accurate, reliable, relevant, complete, timely, and compliant in a defined scope).


2.2        TERMS AND CONCEPTS        

There are several concepts that are important for establishing an effective knowledge management practice in an organization. These concepts have been developed from scientific studies and practical management experience. The concepts are recommended for organizations aiming to increase value from the information assets that they have access to.


2.2.1        Absorptive capacity
The ability to learn is an important aspect of a person or an organization. In the case of an organization, it is enabled and limited by the organization’s absorptive capacity. Absorptive capacity stands for an organization’s ability to recognize the value of new information, to embed it into an existing knowledge system, and to apply it to the achievement of business outcomes. In order to be innovative and adaptive to change organizations should continually develop absorptive capacity. Absorption of new knowledge from



outside of an organization and integrating it into the knowledge system is complex and should occur simultaneously on various levels (external, organizational, teams, and individual). It should also consider the four dimensions of service management1.


2.2.2        Data and knowledge management
To represent the relationship between data, information, knowledge, and wisdom, the data, information, knowledge, wisdom pyramid (DIKW), also known as the knowledge pyramid, is usually used. However, the divisions in the management of each of the levels are not clearly defined. The organization chooses how to name the related activities, and whether data management should be considered a part of knowledge or information management or a separate entity focused on the management of raw data. In addition, wisdom is usually vaguely described and not included in the description of structured processes. In this guide the term wisdom is not discussed, apart from implying that organizations should aim to use knowledge for value co- creation.


Organizations should define and agree the definitions and taxonomy for the knowledge management practice to gain effective outcomes. These definitions may vary depending on the type of data involved and the industry.


Valuable knowledge for decision-making may be obtained from outside of an organization, as well as from inside. This may include information from articles and posts in social and corporate media; data from surveillance and web cameras, audio recordings, and Internet of Things (IoT) devices. Much of this data is unstructured


2.Big data management systems have emerged to work with the huge volumes of raw and often unstructured data, and to analyse it for predictive insights. Big data analytics (BDA) introduces both challenges and opportunities for knowledge management


3.Big data is often defined by three words, known as the 3Vs of big data: volume, velocity, and variety. However, there are more Vs available, for example value, veracity, validity, and so on. The first 3 Vs are essential to understanding whether an organization is dealing with big data or more traditional forms of data.


One of the Vs is variety; which is the range of sources and the formats of the raw data, and the criteria that may impact the knowledge management practice the most as it represents the challenge that big data brings。

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