Journal of Knowledge Management Practice, Vol. 9, No. 3, September 2008

Reusable Data, Information, Knowledge And Management Techniques

O. K. Harsh, University of New England, Australia


A proposal to analyze the data, knowledge and information in three dimensions is suggested and it is concluded that they play a major role in managing knowledge. An extension of this work to analyse the role of knowledge reuse management in the technological environment has also been presented. Useful discussion on several issues such as reusable knowledge creation, reusable knowledge validation, and reusable knowledge application, reusable knowledge distribution and reuse knowledge presentation have been explored. Analysis of the knowledge component composition, reuse, tacit and explicit knowledge and metadata has also been presented. The present work has value for knowledge management and quality enhancement.


Keywords: Knowledge management, Knowledge reuse, Knowledge reuse management, Three dimensional model, Component composition, Metadata


1.         Introduction

People typically do not create data, knowledge and information with the explicit intention to reuse them, although according to Jim Highsmith (2003) When we attack reuse as a knowledge management problem we begin to ask new questions, or at least look for different avenues for finding solutions to the problem. How do we go about finding the component we need? How do we gain confidence that the component does what we want it to do, and doesn't do strange things that we don't want? How should we go about testing this component? How easily will this component integrate into our environment?”


Harsh and Sajeev (2006) extended the work of Hastings and Sajeev (2001) to three dimension to explicitly involve reusability by proposing a three dimensional model that accounts for reusability as a separate third dimension. Recently Harsh (2007a) has proposed a three dimensional knowledge management model that extends the well known Nonaka and Takeuchi model (1995) by involving independent knowledge reusability.  Earlier Harsh (2007b) proposed a model on data, information and knowledge by extending the work of Gene Bellinger (2004). This work can resolve many past issues of knowledge management.


No one has so far tried to incorporate the concept of time, component composition, component development life cycle, and metadata with data and information. In the present paper it has been decided (by extending the past work of Harsh  (2007a, 2007b), to (a) describe the data, information and knowledge in an integrated three dimensional environment with the intention to reuse in future  (b) explain the types of knowledge reuse and management concepts in the three dimension, (c) describe the effect of the involvement of time on the reuse management systems (d) explain the role of components in knowledge reuse and management (e) define the role of metadata that is data about data (f) explain the role of the three dimensional model (Figure: 2) in understanding tacit and explicit reusable knowledge.


2.         Data, Information and Knowledge


According to the Bellinger (2004) “Data is just a meaningless point in space and time, without reference to either space or time. It is like an event out of context, a letter out of context, a word out of context”. According to this author “The key concept here being ‘out of context’ and, since it is out of context, it is without a meaningful relation to anything else.”.


The act of labeling the components in a system we can call ‘differentiation’ (Csikszentmihalyi, 1994) because they are context independent, while it is appropriate to label the act of collecting components as ‘integration’. The work of Marakas (1999) suggests that Knowledge is context dependent, since ‘meanings’' are interpreted. in reference to a particular paradigm (p. 264).


The work of Csikszentmihalyi’s (1994) on data, information and knowledge was extended by the Gene Bellinger (2004), (see Figure: 1) and this work was further attempted by Harsh (2007b).  The entire knowledge of any given system always increases as time increases. In modern technological environments, increasing data and information with time can not be avoided and thus time should also be considered along with the effective reusability (Harsh, 2007b). Thus the proposal is that (in Figure: 1) reusability (data, information and knowledge) and time should be taken into consideration in the above model. Thus in the present work the inclusion of effective reusability over the time allows management of data, information and knowledge more effectively. The present model can also define relations between data, information and knowledge more closely (see Figures: 2 & 5).


Figure 1: Representation of Data, Information, Knowledge and Wisdom (Bellinger, 2004)


Figure: 2 helps us in understanding the effective knowledge reusability rate (Harsh, 2007b) with the growth of information and knowledge in a given system. Reusability without time can not be considered because data are continuously being converted into information, information into knowledge and knowledge into wisdom; as a result, knowledge component properties are affected. If we could select appropriate knowledge components at a particular moment then it not only can boost the knowledge faster but will also provide more qualitative knowledge. It is very difficult to find the exact values of data, information, knowledge and its reusabilities at a particular moment and therefore we account their effective values.


Note that wisdom which is beyond the knowledge (see Figure: 1) may be the key to success because it represents the past experiences. Thus we need experienced people who not only have substantial experience in their organizations but who at the same time can make a learning environment for others.

3.         Types Of Reusable Knowledge and Assets

The reusable assets of an organization must be coupled with the existing and available technologies, techniques and methodologies. However, the interpreter is the human mind which is based on large number of factors.  Ultimately management is done by people by adopting a sketch of series of past events or successes related to the finished projects or tasks. Pattern of success will have to be identified and should be simulated in accordance with the requirements of a present project.

Similar to the knowledge creation, validation, distribution and application processes in knowledge management, reusable knowledge also involve these steps (Harsh, 2007b). These processes may be within a project or between projects. We can identify and collect reusable knowledge and assets at each stage of the project developments. Any organisation can learn from their past experiences and history in these areas. We therefore discuss these issues below.

3.1.      Reusable Knowledge Creation

In the later phases of a project it is important that management makes sure to initiate learning from the project so that knowledge and experience are shared and leveraged in the organization in the subsequent projects. Reusable knowledge creation varies over time in the different phases. As mentioned above, the data and information are created with a view to reuse in the future (Harsh, 2007b); therefore, we have to monitor each phase of the project over the time. Motivation and inspiration should be used at each stage of the life cycle of the project to tape the reusable knowledge.

3.2.      Reusable Knowledge Validation

Knowledge creation means the capability of an organization to develop new and useful ideas and the solutions. By reconfiguring and reusing foreground and background knowledge and reusing the available assets an organization can build new knowledge assets; however, all existing knowledge can not be reused. We can adapt some of the existing knowledge by compromising some of our requirements. Reusable knowledge validation is a continuously monitoring process and t is a matter of experience.

 3.3.     Reusable Knowledge Presentation                     

Reusable knowledge presentation means the ways the reusable knowledge is displayed for the organizational members. It is up to the organization how it devises various procedures to structure its knowledge. Normally organizational knowledge is redistributed and it is not an easy to collect it for reusable assets. The organisation has to set a separate unit to monitor such knowledge. This unit adapts different types of people to work according to their styles. An organization has to adapt a standard, codification, and schemes to account such knowledge. Finally the presentation of styles of all employees has to be unified in a single pattern so that we can integrate such reusable knowledge into our practices.

 3.4.     Reusable Knowledge Distribution

It is always required to distribute knowledge before it can be reused. Knowledge distribution is affected by the interaction between existing technologies, techniques and people. Monitoring of such processes minimizes the problems for its reuse. Reusable knowledge supervision minimizes the problem of its recognition by individual member. However, the structure of the organization will also have to be taken into account before we create a strategy for its reuse.

3.5.      Reusable Knowledge Application

It is not possible to apply reuse knowledge to the problems unless it is easy to locate the right kind of knowledge in the required form. An organization should be capable to find the right type of knowledge at the right time. Knowledge reuse application means having more knowledge available for reuse. It can create more values for the organization. Criteria for evaluation of the reusable knowledge application should be set up including the priority of reusing particular type of the knowledge. The question of how the company understands about the reusable knowledge application is important.

 3.6.     Reusable Knowledge Cultures

An organization should allow its members to interact and nurture an environment for reusable knowledge sharing and integration. The members of the organization should collaborate with a view to reuse the knowledge. Reuse of knowledge is only feasible if it is encouraged at all levels of the organization. An organization should create and monitor the pattern of such activities which always encourages the reuse of its assets; however it will also depend upon the way the people interact with the technologies and techniques.

4.         Knowledge Management Component

The Knowledge Management Component Architecture comprises knowledge portals, knowledge components, and the knowledge repository. A Knowledge Portal is a beginning point web site where members of a knowledge community enter, find, and access knowledge using the various knowledge artifacts. According to Finneran (1999) “A knowledge component is a self-contained, reusable object that can be used independently or assembled with other components to satisfy knowledge management requirements”. The Knowledge Repository comprises servers in which knowledge indices and, often knowledge artifacts (documents, presentations, databases, charts, graphs, plans, audio files, and/or video files) are potentially accessed. This repository is highly affected by the types of the data and information. Reusable assets are constantly updated in such type of systems in order to meet these needs.

5.         Component-Based System Development Lifecycle

According to the present author, Knowledge components may address the issues of knowledge components management and knowledge system, and similar to a waterfall, the author suggests a knowledge waterfall model that can co-relate with the finding and selecting components.

6.         Reusable Knowledge Components And Frameworks

Similar to the software components (DENG-JYI CHEN et al., 2000), we can consider a Reusable Knowledge Component or Framework. which is similar to a software component (Harsh and Sajeev, 2006, DENG-JYI CHEN et al., 2000). Similar to software components there are Knowledge Components Generalization that may be used to create generalized components which are general to the many applications while Specialization components may be used to create specialized components which can not be used in general. Thus a Reusable Knowledge Component (RKC) or framework may be designed and implemented so that there is a balance between generalization and specialization. Here we can also say that (similar to authors (DENG-JYI CHEN et al., 2000),) a reusable knowledge component may behave like a server while a client (application program) only requires the specification of a server only.  Thus it does not need to know the details of the services provided by server. This implies that reusable components may be treated as tested one.

7.         Composition Environment And Software Knowledge Reuse

According to Boehm et al. (1984), software knowledge reuse is a useful way similar to software Reuse in software development projects for advancing productivity. Like software environment, software knowledge environment is deeply linked with four steps in reusing an artifact (Dusink et al., 1995) which  may be mentioned as  (i) find (ii) select (iii) understand; (iv) adopt. Our belief is that it is possible to optimized knowledge reuse metrics during the composition and deployability of knowledge components if we apply the concept of the reusability on data and information right from the beginning of the their creation.

8.         Knowledge Components And System Development Cycle

As mention in (Harsh and Sajeev, 2006), “Composing applications out of reusable and pre-existing software components is an important question in creating applications” . Pre reusable data and information make it flexible to match and adopt components more minute, sensible and flexible way.

9.         Component Composition Process

The model of knowledge component development environment may be refined and used to demonstrate and divide application composition process similar to software components (Lüer and van der Hoek, 2002) where the steps like search, select, adapt, compose, manage and execute may be adopted. We have additionally included manage  part in the Figure: 3 after dividing the reuse task further in the work proposed by Althoff et al. (1999).

Once we have composed the applications then we need to require it to manage so that we can get better reusable and properly linked components. Given reusable component may provide maximum reuse benefit if it could be linked and deployed appropriately. The same manage task may also be used once we identify the components because sometimes we already have the appropriate components available for our applications.



10.       Metadata

According to Garnemark (2002) “Metadata can be described to be one level above the studied object (Liebowitz, 1999), i.e. metadata is data about data” . Having additional data about data especially reusable data makes it easier to interpret, reuse and understand (Figure: 4). Figure: 4 is modified by present author from that proposed by Garnemark (2002). We have included the description of the “reuse rate” and “reuse by”. Therefore storing and reusing up-to-date additional data will not only ease the actual use of the existed data but will furnish more fertile system. We can realize that it is always easier and helpful to recall, use and reuse if you can arrange in accordance to the right or predefined cause. “For example, knowing when and by whom the data was created can make it easier to interpret the data at hand (Garnemark, 2002)” .


Figure 4: Description of Metadata - taken and extended from Garnemark (2002)

11.       Example

Bank and Insurance organizations are important examples where typically the data, information and knowledge are used for making critical decisions. Sets of reusable data and information are stored in the system and exploited whenever required. As Figure: 5 shows data, information, knowledge and meta data may be exploited in a better way by reusing and combining them appropriately.
























12.       Tacit And Explicit Knowledge

According to Figure: 2 (Harsh, 2007b), reusability plays an important role in determining the types of the convertible knowledge in the three dimensional space. Since the reusability is an independent quantity it is easier to record and express the tacit and explicit knowledge in three dimensional space.

13.       Implications

Knowledge reuse is not only an asset for an organization but it sets a pattern between technologies, techniques and people. As mentioned in Figure: 2 (Harsh, 2007b), time and reusability both are important and valuable assets for any organization. Knowledge management and reusable knowledge managements both are equally benefited by the approach shown in Figure: 2 (Harsh, 2007b). Composition and adoption of the right components at the right time is a great asset to any organization.

Reusable Knowledge management approach will have to be organised ‘right’ from the beginning. The mathematical concept of data, information and knowledge (Figure: 2), composition of components and nature of metadata (Figure: 4) can be grouped into the relevant needs of the organizations.

The present work shows that reusable knowledge may be managed and applied effectively provided we understand the concept of knowledge reusability and time in relation to the data, information and the knowledge. Reuse of existing knowledge not only saves the cost and efforts of the project but will also enhance quality due to reuse of verifiable and existing assets. Integration of existing reusable knowledge into a new project is a novel idea where the requirements of the new project may be exploited to large extent.

14.       Acknowledgment

The author is thankful to Prof A.S.M. Sajeev, Head of the School of Computer Science, Statistics and Mathematics, University of New England, Armidale, Australia for the discussion and support.

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Meet the Author:

O. K. Harsh is associated with University of New England, Armidale, Australia as a research worker and he has been working under the supervision of Professor A. S. M. Sajeev, Chair of Computer Science Department and the Head of the School of Computer Science, Mathematics and Statistics, University of New England, Armidale. He can be contacted at Department of Computer Science/IT, School of Mathematics, Computer Science and Statistics, University of New England, Armidale, NSW 2351, Australia; Tel: 0096892673249; Email: