knowledge--management

lundi 26 septembre 2016

Importance of Performance Measurement for a successful KM Implementation.

Culture may be defined as a set of beliefs and values that provide identity and defines the day-to-day operations within an organization. This will include organization's purpose, vision, criteria of performance, the authoritative locations, decision-making orientations, leadership styles, compliance, evaluation, motivation et al.

A knowledge friendly culture is a key and critical component to successful knowledge management implementation. The organizational view and facilitation for both learning and innovation including how the employees are encouraged to build the knowledge base, in ways that enhances value addition for all its stakeholders is very important.

Organization Culture, which is a key element of managing organizational change and renewal is the biggest challenge for knowledge management implementation. It is this internal culture which can decide on the make or break of effective knowledge transfer, sharing and management. The employee behavior gets moulded to the organizational culture.

An open culture built around integrating individual skills, experiences and competencies into the organization's knowledge will be more successful. A culture of confidence and trust is required to enable and encourage the application and development of knowledge within an organization.

Mon site Web 13:19

Importance of Benchmarking for a successful Knowledge Management Implementation

Acquisition and Creation of knowledge takes places from various sources such as Individual level, Group level, and Organizational level. Sharing of knowledge among stakeholders ensures in capturing, collating and creating specific, reliable, useful, up-to-date and timely knowledge.

Organizations are today striving for improving their bottom line and therefore realizes the importance of involvement of customers and suppliers as sources of product and service innovation. Strategic partnerships with customers are viewed as long-term proposition.

Emergence of Communities of Practice has shown that individual and common goals and interests are taken into account to provide a natural focal point for organizing and promoting knowledge in a particular area. This helps to provide solutions to organizational problems, as well as to provide insight on new or innovative product and services.

Hence Benchmarking is seen as an important aspect with respect to Knowledge Management. It helps in understanding where the organization features in comparison with other organization's in the industry with respect to knowledge, competency and capability which helps in the growth of the organization.

Mon site Web 13:16

Knowledge Management Component Architecture

The Knowledge Management Component Architecture consists of knowledge portals, knowledge components, and the knowledge repository.

A Knowledge Portal is a starting point web site where members of a knowledge community begin to enter, find, and access knowledge using the various knowledge artifacts. The knowledge portal may be designed to focus upon the type of work expected to be done by the knowledge user. Knowledge portal profile modes so far determined are:

knowledge subject matter access,
collaboration,
community description and,
a combination of the above.

At times, the knowledge user may wish to focus on knowledge relevant to a project being worked on within the context of the knowledge community, or he or she may wish to take an enterprise knowledge view.

A knowledge component is a self-contained, reusable object that can be used independently or assembled with other components to satisfy knowledge management requirements. There is the generic set of architecture issues relevant to all components. Knowledge components have to interface with the knowledge portal, with the knowledge repository, and with other knowledge components. A knowledge component may need to be customized to handle knowledge of events specific to a given knowledge community. In a like fashion, component behavior may need to be customized to satisfy the special needs of the specific knowledge community.

The Knowledge Repository consists of servers where knowledge indices and, often knowledge artifacts (documents, presentations, databases, charts, graphs, plans, audio files, and/or video files) are made accessible. Some searching may cross knowledge servers.

Global Virtual Knowledge Repositories are inter-connectable Knowledge Repositories, globally distributed, that look to be a single entity to portals and knowledge components. One search searches all.

Mon site Web 13:15

DIKW model

This is an adaptation from Russell L. Ackoff, "From Data to Wisdom," Journal of Applied Systems Analysis

The DIKW model assumes the following chain of action:

Data comes in the form of raw observations and measurements.
Information is created by analyzing relationships and connections between the data. It is capable of answering simple "who/what/where/when/why" style questions. Information is a message, there is an (implied) audience and a purpose.
Knowledge is created by using the information for action. Knowledge answers the question "how". Knowledge is a local practice or relationship that works.
Wisdom is created through use of knowledge, through the communication of knowledge users, and through reflection. Wisdom answers the questions "why" and "when" as they relate to actions. Wisdom deals with the future, as it takes implications and lagged effects into account

Data has commonly been seen as simple facts that can be structured to become information. Information, in turn, becomes knowledge when it is interpreted, put into context, or when meaning is added to it. There are several variations of this widely adopted theme. The common idea is that data is something less than information, and information is less than knowledge. Moreover, it is assumed that we first need to have data before information can be created, and only when we have information, can knowledge emerge.

Data are assumed to be simple isolated facts. When such facts are put into a context and combined within a structure, information emerges. When information is given meaning by interpreting it, information becomes knowledge. At this point, facts exist within a mental structure that consciousness can process; for example, to predict future consequences, or to make inferences. As the human mind uses this knowledge to choose between alternatives, behavior becomes intelligent. Finally, when values and commitment guide intelligent behavior, behavior may be said to be based on wisdom.

Data
Specific local properties
1: factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation (the data is plentiful and easily available.)
2: information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must be processed to be meaningful.

Information
Specific local properties
(1): knowledge obtained from investigation, study, or instruction
(2) : intelligence, news
(3) : facts, data.

Knowledge
Specific local properties
(1) the range of one's information.

Wisdom
Specific local definition
(1) accumulated philosophic or scientific learning: knowledge. (2) wise attitude or course of action.

According to these definitions, data is the basic unit of information, which in turn is the basic unit of knowledge, which itself is the basic unit of wisdom. So, there are four levels in the understanding and decision-making hierarchy. The whole purpose in collecting data, information, and knowledge is to be able to make wise decisions. However, if the data sources are flawed, then in most cases the resulting decisions will also be flawed.

Mon site Web 13:14

Elements of Knowledge Management

It is important that organizations transform their structure and processes to become learning organizations leveraging Knowledge Management. Some insights into the Knowledge Management elements, as provided by Marquardt (1996):

Collaboration: People should be able to use the social media, the internet and internal systems and encouraged to collate data and share with their peers and teams.
Expertise and Access to Experts: Job rotations, cross- functional projects etc. facilitates transferring of knowledge across boundaries and also in enhancing the knowledge and expertise.
Communities of Practice: This forum helps in posting issues, solving problems and discussing key topics. People who share common interest in an area of competence and are willing to share their experiences come together to form a community of practice. Apart from meeting formally, collaboration tools, such as message boards, chats, web boards, discussion forums etc. can be used for this purpose.
Real-time information: The key concept of knowledge management is to provide information to the right people, in the right format and in the right period of time. Providing systems that people can readily access for information helps in this.
Knowledge of organization depth and scope: Having volumes of data is not useful unless it is coded and stored in a manner that makes sense to the people and which is easy to retrieve. Determining what to capture, how to capture, formatting to help people to analyse and make decisions is important.
Personalization and navigation of the system and interface: For people who are not computer savvy, it is difficult to understand the importance of collating data, entering it in a centralized system. Having a system which is easy to use, retrieve data and also enter data helps in leveraging knowledge to the maximum.
Difference between instruction and information: Data which has a meaning is information. Organizations encourage sharing information using collaboration, mentoring and socialization to inform people, which can be done through meetings, at workstations or as issues happen. Instruction is information that is taught as part of on-the-job training, class room training or web-based training.

Mon site Web 13:14

Evolution Of Knowledge Innovation (KI)

Organizations are struggling today to differentiate themselves from relentless competitors with saturation in markets and new innovations being introduced all the time. The ability to differentiate depends on the “intelligent use” of knowledge assets for innovation. As a result, many organisations have been trying new techniques based on unique production processes, rare and distinct skills, creativity, and now on management initiatives such as supply chain management and customer relationship management (Gold et al, 2001).

The intensified competition has raised the bar of expectations with respect to the knowledge-based computer systems, such as expert systems or decision support systems being used as a KM tool. Earlier these tools were centered on stand-alone systems but very soon the realization that human beings should be taking the central role in knowledge management rather than creating thinking machines created the need for separating information management and knowledge management.

The use of IT-based KM tools to equip organisations with the requisite competencies needed for innovation; and not replace individuals by “thinking machines’, led to three kinds of physical IT systems that are needed for KM practices to be effective, namely: capture tools (e.g. intelligence databases), communication tools (e.g. distributed networks), and collaboration tools (e.g interactive web pages).

Today many organizations are yielding considerable benefits in innovation-related and product development functions.

Mon site Web 13:13

Knowledge Centered Principles for Innovation Management

If Knowledge Management practices are to be incorporated into Innovation Management processes as a competitive tool for supporting Knowledge Infrastructure, organisations should embrace their roles based on an evolving set of knowledge-centered principles. Through a distillation process of contemporary literature, six knowledge-centered principles relating to managing

Knowledge Infrastructure seem to distinguish themselves from the other conventional management approaches as summarized below (Harkema and Browaeys, 2002; Davis and Botkin, 1999; Miller and Morris, 1999; Skyrme and Amidon, 1997, Davenport, 1993).

1. Understanding Innovation Value System (not Value Chain) – A value chain is linear and static, while a value system is non-linear, dynamic and represents interdependent relationships that need to be understood, considered and developed for Knowledge Infrastructure;

2. Formulating Collaborative Knowledge Strategy (not Competitive Information Strategy) – The latter strategy creates win-lose scenarios due to competition for the same information pie, while former strategy encourages win-win situations through symbiotic relationships by sharing and growing the knowledge pie;

3. Developing Strategic Knowledge Networks (not Strategic Business Units) – The latter applies isolated islands of information assets while the former fosters the flow of knowledge assets among partners, customers, suppliers, and other stakeholders including competitors for innovation pursuits;

4. Constructing Hybrid Human-Technology KM solutions (not Machine-based KM solutions) - Human beings are better at ‘knowledge skills’, while machines are more adept at ‘information tasks’. To fully harness knowledge for innovation, humans and machines must complement each other.

5. Fostering bottom-up knowledge processes (not top-down knowledge processes) – Creative and useful knowledge works, carried out by knowledge workers, require less top-down intervention and more bottom-up spontaneity.

6. Focusing on Customer Success (not Customer Satisfaction) - Customer satisfaction meets today's needs only, while a deliberate focus on customer success helps identify future requirements and unmet needs, which form the competitive forces for firm growth and business expansion.