Building Knowledge Management (KM) Systems in Organizations

“KM is more of a strategy supported by Information Technology (IT) that can show a quantifiable and sometimes substantial return on investment” (MacSweeney, 2002). Knowledge needs to be put at the fingertips of people within an organization i.e. readily available to everyone in an organization. This should not only be available but ease of use is very important. It needs to be the culture of the organization to use the knowledge available either by IT or collaboration. It needs to be enabled for users internally.

Buyers and customers need to be educated as with Armonk insurance company: with the use of the internet, products are described online, as well as coverage type, liability, background process for rating etc. Applying this scenario to Albion (organization for group coursework), the use of the internet for buyers, garment technologists and customers may prove to be a beneficial KM strategy in the organization (MacSweeney, 2002).

MacSweeney (2002) suggests that the focus of a knowledge strategy can either be inward or outward, either way the strategy should not be Information Technology (IT) driven but IT can support a strategy. He further purports that to have a successful KM strategy, the business community has to be involved from the inception and sponsorship from business executives in essential.

Role of IT in Supporting Knowledge Management (KM)

Case Study (Laudon and Laudon 2006)

Content Management Systems as an IT support for KM: Southern Company is an organization that generates electricity for over 4 million people in the United States of America.

The introduction of documentum system (content management system) cut down search time for essential documents and processes to 10 minutes from 2 hours and also increased the success in locating current content to 90% from 50%.

Another benefit that the IT supported knowledge management system brought to Southern Company was the helped to be more prepared for the hurricane Katrina compared to other organizations in that they were able to copy to disk critical drawings/plans and other information allowing those workers that are field based with laptops to retrieve much needed information thereby enabling them to restore energy to their users.

Southern Company’s energy restoration to their users was accomplished in 1 day and 20 hours compared to their competitors restoration time that averaged 3 days and 5 hours.

The above benefits show how efficient and effective an organization can be if KM is supported by IT. It also shows that Southern Company can gain competitive advantage by delivering faster than their competitors.

Problems Faced by Southern Company:
Document intensive i.e. piles of documents to go through before reaching the needed document(s)

• Fragmented information in legacy system i.e. a lot of legacy systems holding different forms of information
• Manual processes: so many paper based forms to fill and manual processes to follow.

Ideal situation:

• Reduced paper based documentation

• Consolidated information in one accessible system

• Automated processes

Results:

• Reduce cost: saving in the long-run on man hours regarding searching for information, designs, processes etc.
• Reduced time: to complete processes of energy restoration i.e. the knowledge required to restore energy, to find documents, to find information or content
• Competitive advantage gained through cost and time reduction
  

References:
Laudon, K., Laudon, J. (2006). Management Information Systems: Manging the Digital Firm (10^th Edition) p14
MacSweeney G., (2002). The Knowledge Management Payback. Insurance and Technology May 6, 2002

Helpless KID: Uses of Terms in Knowledge Management

This article explains the meaning and uses of Data, Information and Knowledge (KID) within knowledge management.

According to Laudon and Laudon (2006), data is a stream of raw facts representing an event occurring in an organization which is, a meaningless and useless presentation of facts unless it undergoes some form of processing e.g. numbers in a cell of a spreadsheet means nothing until it has been processed or defined

Information on the other hand are these raw facts that has been given some meaning or has been processed. Information needs to be timely, meaningful and useful e.g. following the analogy above of numbers in a cell of a spreadsheed, it becomes information when the provider names the cell ‘Friday’s Team Attendance’ for instance. As Laudon and Laudon (2006) suggests “information is data that has been shaped into a form that is meaningful and useful to human beings”

An example of data and information in an organization setting given by Laudon is raw facts collected from a supermarket checkout e.g. 001 detergent 3.50 , processed and organised to produce meaningful information e.g. Item no = 001, Item = Detergent and 3.50 = unit price

Drucker (Unknown) defines knowledge as “information that changes something or somebody -- either by becoming grounds for actions, or by making an individual (or an institution) capable of different or more effective action."

If knowledge is processed data that has been given meaning that make it useful to human beings, then, knowledge, following Laudon’s example, would be to the sales team, knowing when to reorder stock levels of product in an organization.

Supporting my position on the relationship between Data, Information and Knowledge (KID) is Bellinger et al (2004), who suggests there is a linear relationship between data, information and knowledge. Without data information cannot exist and without information cannot exist either.’

Though, I agree with Bellinger et al (2004) that said the is one item that is required throughout the relationship between KID and that is understanding i.e. understanding is needed in data collection as to what data is required, it is also needed in order to transform date into information and the same is true for information to knowledge
However, having gone through lectures/seminars on KID, I have a differing opinion from Bellinger et al (2004) on the existence of a linear relationship between data, information and knowledge (KID). I still maintain there is a relationship between them but this could also be cyclic as data can create information and information, knowledge. From the knowledge created, a new set of data can come to light that needs transforming to additional knowledge. Another view exists that before one can determine the type of data to collect, one needs some information or indeed knowledge of some sort to be efective in the task of data collection. This may further reinforce the cyclic nature or relationship of KID.

Reference:

Bellinger, G., Castro, D., Mills A., (2004). Data, Information, Knowledge, and Wisdom. Systems Thinking

Laudon, K., Laudon, J. (2006) Management Information Systems: Manging the Digital Firm (10th Edition) p14

Drucker, P. (Unknown).Knowledge. The New Realities [online, http://www.skagitwatershed.org/~donclark/knowledge/knowledge.html Accessed on 11 February 2009

Knowledge Management Models (KMM) and their Fit within Model Classification(s)

The aim of this article is to put some perspective on how a particular KMM can fit within one or more Knowledge Management (KM) model classifications. I particularly aim to fit models within Michael Earl’s nomenclature of models, with suitable examples.

Types of Knowledge Management (KM) Models

Literature research indicates that there are a number of accepted KMM including SECI model by Nonaka et al (2000), the KM Lifecycle Model by Sagsan (2006) and Epistemological and Ontological SECI (EOSECI) Model by Muina et al (2002). Under each model, knowledge can be described as either ‘tacit’ (not written) or ‘explicit’ (written).

Nonaka et al (2000) suggests that in the SECI Model, the process for knowledge creation works on the premise that knowledge can be created within an organisation by the interaction of tacit and explicit knowledge, known as Knowledge Conversion. Knowledge conversion can be achieved by following the four components within the SECI model i.e. socialization (tacit to tacit), externalization (tacit to explicit), combination (explicit to explicit) and internalization (explicit to tacit).

It is my opinion that the SECI model fits within Earls 7 schools of knowledge management classification (2001), in particular, the behavioural school classification, which encompasses; the organizational, spatial and strategic schools. I say this because the very nature of the SECI appears to be social and epistemological and on studying Earl (2001) classification of models, the behavioural attributes clearly focuses on networks, space and mindsets and aims for knowledge pooling, knowledge exchange and knowledge capabilities.

I believe the utilisation of the SECI model is best suited to, non-IT communities of practices within and outside an organization and ‘not for profit’ organizations because;
1. It does not readily provide a structure to enable profit making.
2. It is not based on the use of information technology (IT) hence cannot promote use of IT. The impact on of its ‘non-IT’ nature will be adversely felt in large organizations, as they will need some form of IT to back up and enhance their processes in promoting KM as a means to attain competitive advantage

A simple example of where the SECI model can be adopted is within a church organization where competitive advantage is not required but the aim is to share knowledge on how to win souls for Christ and tend the flock i.e. the church goers. Also, it is not imperative to the success of this type of organization that IT based knowledge management is utilize. Gebert et al (2003) further supports my view by saying “Based on their definition of knowledge, all epistemological-oriented knowledge management models share a common weakness when used in an environment that requires evaluating knowledge as a business resource”.

Whilst there has been widely acclaimed use of SECI, there has also been some criticism of the model by various authors and scholars of KM.

These critics have suggested new models including the EOSECI model, which suggests the SECI can only be complete with both the epistemological and ontological models added to each components of SECI, which Muina et al (2002) have called the ‘holistic knowledge creation’. Muina et al (2002) also suggests that SECI only addresses the epistemological creation of knowledge but when the ontological creation of knowledge is added to a model, this creates a more balanced model. This view is echoed by Gebert (2003) as mentioned above.

In my understanding, the EOSECI model gives a better balance on knowledge management, which is what Gebert (2003) calls a ‘Hybrid model’. As a software test practitioner, I compare this to software testing, where tests can either be black box or white box based. Whilst black box looks at the external functioning of the system under test (SUT) and is completely blind to the internal workings of the SUT, white box tests specifically looks and exercises the internal workings of the SUT e.g. the written code that enables the program. You cannot get a complete test and assure the quality of the SUT unless both internal and external functions have been carefully considered. Analogy was inspired by Gebert (2003)

On the other hand, another critic of Nonaka’s work, Gourlay (2004) suggests the empirical grounding for SECI is not sound in that he found flaws in each section of the model, however, he did not come up with a new KMM.

Sagsan (2006) reviewed various KMM, including,

By Award and Ghaziri (2004) which entails capturing (knowledge gathering through emails, various files etc) – organizing (organizing information gathered in a useful manner e.g. indexing) – refining (this process aims to turn explicit knowledge into tacit knowledge by the use of data mining) – transferring (the acquired knowledge is transferred to members of an organization through necessary mediums such as training).
By Alavi and Leinder (2001). Their process is known to be in the information technologies (IT) context which Sagsan (2004) believes is limited to our understanding of IT. It encompasses knowledge creation – storage/retrieval – transfer – application.

Based upon his perceived deficiencies of KMMs, Sagsan proposed the “Knowledge Management Lifecycle Model”, which seeks to introduce a hierarchical approach to the KM process. This process includes: creating, sharing, structuring, using and auditing. Sagsan argues that other models do not clearly show the hierarchical relationship that exists between components of the model thereby making knowledge management a daunting task to carry out in organizations. The new model introduces five components: 1. Knowledge creating 2. knowledge sharing, 3. knowledge structuring, 4. knowledge using and 5. knowledge auditing. Each component has sub processes to ensure the completion of one phase before the next phase can commence. After the last phase completes, then the model suggests knowledge recreation i.e. the model has a cyclic or spiral nature as with the SECI model.

I however, believe the model by Alavi and Leinder (2001), albeit IT based can be very useful within the large and IT organizations, in that their business is obviously IT related and have a need for competitive advantage.

References:

Earl, M., (2001). Knowledge Management Strategies: Towards a Taxonomy. Journal of Management Information Systems, 18(3), pp215-233.

Gracier Muina G., Martin de Castro G., Lopez Saez P., (2002). The Knowledge Creation Process: A Critical Examination of the SECI Model. Third European Conference on Organizational Knowledge

Gourlay S., (2004). The SECI Model of Knowledge Creation: Some Empirical Shortcomings

Sagsan M., (2006). A New Lifecycle Model for Processing of Knowledge Management. 2nd International Conference on Business, Management and Economics, Izmir
Gebert H., Geib M., Kolbe L., Brenner W., (2003). Knowledge-enabled customer relationship management: integrating customer relationship management and knowledge management concepts[1]. Journal of Knowledge Management, 7(5), pp107-123

Nonaka I., Toyama R., Konno N., (2000). SECI, Ba and Leadership: a Unified Model of Dynamic Knowledge Creation. Long Range Planning, 33

Strategy

This article looks at knowledge management strategy in an organization and it application thereof.

To be able to fully appreciate the term ‘knowledge management strategy, it will be beneficial to give brief definitions of strategy and knowledge.

According to Wikipedia, “Strategy is a long term plan of action designed to achieve a particular goal. Strategy is differentiated from tactics, or immediate actions, with resources at hand by its nature of being extensively premeditated, and often practically rehearsed”.

Knowledge as defined by Bellinger et al (2004) “is the appropriate collection of information, such that its intent is to be useful. Knowledge is a deterministic process”

My View on Strategy

Strategy can therefore be a definitive plan on the way forward for any given organization. Contrary to the definition, an organizations strategy can indeed be short term, taking into consideration emerging markets and evolving technologies. A strategy can start off being long term but with provisions to change directions ‘as and when’ necessary, as dictated by influences such as the environment, the economy, political changes, change in technology, etc.

Literature by Mintzberg and Waters (1985) explored two different types of Strategies, deliberate and emergent. In their opinion, for there to be a pure deliberate strategy, three conditions need to be met i.e. precise intentions need to be concrete, these intentions need to be common to all in the organization and finally, these intentions need to be realized exactly as intended. This implies that no external force such as the environment, political issues, technology etc can influence the realization of these intentions. However, to maintain competitive advantage, its knowledge management strategies need to be able to respond to external forces especially if an organization uses knowledge as an asset. This is because pure deliberate strategy rarely exists. For example, a customer based organization needs to have knowledge of their customers and changes in the environment, political issues as well as technological issues that may affect their customers in order to maintain their competitive edge. Laudon and Laudon (2006)

References:

Mintzberg H., Waters J., (1985). Of Strategies, Deliberate and Emergent. Strategic Management Journal 6, pp257-272

Bellinger G., Castro D., Mills A., (2004). Data, Information, Knowledge, and Wisdom. Systems Thinking

Wikepedia http://en.wikipedia.org/wiki/Strategy_(disambiguation)
[Accessed 11 February 20099]

Laudon, K., Laudon, J. (2006) Management Information Systems: Manging the Digital Firm (10th Edition)

Week 3

Ontology Vs Epistemology

Ontology is the attribute of things, Darwin (2007) concerned with the ‘how’ and not concerned with the ‘what’ as with epistemology. Ontology looks at how we as the observer may acquire knowledge Nel J.P, Com D. (2007)


Epistemology is the attribute of Knowledge - Darwin (2007) concerned with the study of knowledge, how to obtain it and how we can reason Nel J.P, Com D. (2007)

Determine your target first before you can gather information from your target e.g. you need to find out what and determine what the organization is i.e. your target and then you can begin to acquire information from that organization and build knowledge. Nel and Com reckon ontology precedes epistemology.

With epistemology, it requires evidence to back up it findings to show it is more that an opinion whereas with ontology, it need not be proven as it is evident, it is what already exists e.g. nature.

My View
The above has given me clarity in relation to the terms ontology and epistemology and has helped me put in perspective how this can be adopted in an organization when it comes to knowledge management.

If ontology precedes epistemology then, we do need to know ‘how’ the organization is structured, its polices and strategies in order to gather relevant information and knowledge that will help an organization’s performance for profit. For example, in the defence industry, knowledge cannot be easily shared due to the nature of the organization. A lot of the knowledge has to be kept secret and can only be shared with colleagues on the same level of security clearance. However, knowing the policy and strategy of the organization will go a long way in helping to gather information and design appropriate knowledge management facilities required by it that will in turn enhance the overall performance of the organization.

References:
Darwin, (2007). Ontological Vs Epistemological Complexity, ChilliConDarwin [On-Line] http://www.chilicondarwin.com/chilicondarwin/News/Entries/2007/6/1_Ontological_Vs._Epistemological_Complexity.html

Nel J.P. and Com D., (2007). Epistemology Vs Ontology, Mentornet [On-Line]
http://www.mentornet.co.za/files/documents/EPISTEMOLOGYVSONTOLOGY.pdf