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DATA, INFORMATION, KNOWLEDGE & WISDOM THEORY
Contributed bySientifix Corporation
Author: Dennis Bustamante
Our world today is focused on the concept of knowledge management. Most people who think about these things have first to define for themselves what real knowledge actually is and then, hopefully, allow itself to be tamed into being managed. The challenge of course is perhaps greater than that presented by trying to tame the weather. Therefore there are two daunting challenges that confront those who would manage knowledge. The first is to find the solution to replicating the human brain and its relationship with human sentiment. The second is how to move the reality that has been defined as knowledge so that it may be employed for business purposes from the static phase of past experience to the truly dynamic phase of future action.
Sientifix Corporation has introduced a singular and different method of capturing, collecting, relating and patterning data that produces interpretive and cumulative results of the day to day activities of a business. The end result is a knowledge management system based on the DATA, INFORMATION, KNOWLEDGE, WISDOM theory that not only preserves information, but makes the knowledge derived from it logically dictate future action for a duplicate or closely similar set of business circumstances.
REVIEW OF THE DIKUW THEORY OF KNOWLEDGE
In the SIENTIFIX™ context, knowledge is defined as: The body of enterprise related truths, or facts, that have been accumulated in the course of time with the success of the work product meeting the intended goals of the organization. This definition clarifies certain ideas. First, we can only talk of truths and facts that are related to the business while they contribute to the success of the business through the quality of its work product. Secondly, not all of the facts and the truths collected are necessarily related to the work product, but they do form a body of forces that influence the end product. Thirdly, the truths and facts are captured in a timeline to form a dynamic pool of indisputable facts that can drive the behavior of the business from one real-time point to another.
The construction of any body of knowledge takes place within the confines of best practices dictated by the DIKUW theory of how knowledge is obtained. Therefore, we should examine the theory by elucidating the meaning of the various elements of what we understand to be "knowledge" as we wish it to be defined.
THE STATE OF KNOWLEDGE INTERPRETIVE SOFTWARE (KIS)
Why do we gather so much data?
The search for the Holy Grail of business software, one that will predict the future, gains momentum every year. It is one thing to have knowledge stored away, but it is another to be able to interpret it with applied logic and turn it into wisdom. The available systems do well in gathering data and following that up with producing relationships that provide good information, but that should not be the end product of the endeavor . Success only results from having the ability to intersect well-connected data with an understanding of the relationships that exist within the collection. The more densely connected the data are, the easier it is to understand them. The denser the data, the clearer the information one is able to expect to be able to explicate from it.
Data therefore obviously enrich their density as they accumulate. They are of course generated by different stimuli from different sources. The density that is derived can then make the task of defining their meaningful relationships that much easier. Further information is then created and this creation in and of itself therefore becomes useful. It is at this stage that questions begin to emerge and the questions the information forces to develop starts to bring forth answers. The software at this point of course ,put crudely, is operating at its “one-hand-tied-behind-its-back" level. There are many applications for creating low-level information of this nature as everyone who has worked with pure data is very well aware of. More often than not, working with masses of low-level data merely creates a situation of a non-relenting series of creating a final destination in low-level “black-holes”, from which they may very well never see the light of day again. This of course causes another question to be asked, which is:
Why do some people in the same organization know more than others?
The answer to this question, one that has been intriguing leaders for millennium does not lie solely in the lack of looking to the initiative or energy of individual employees. There is a technological answer as well to be considered. The linear graph above, following the descriptions that were described earlier shows that there is more strength in software than many people and organizations are aware of or have the present captured intellectual capacity to use. There is software that presently exists that turns information into knowledge. This is software that can start to interpret what has already been organized into information. It is obviously vary low-level interpretation at this point, but still a very important one. It provides the result of identifying the patterns of the information that has been defined at a lower level. These patterns yield knowledge that is very important to the strategic operation of any enterprise. The result of the collation of the patterns it provides is the knowledge that is derived from the core of the existing system of information collection. Call it the nucleus. Knowledge that is produced for employees to know and be able to use. Every function in the company feeds the database with the seeds that may one day grow into essential advantageous knowledge. (The bottom line however is that your computer system knows, even if you do not know it yet). Therefore, we are willing to state that any enterprise that challenges itself to work with a Knowledge Interpreting Software package will soon find knowledge more evenly spread amongst its employees on all levels. It will also provide a safe leap into the future because it will protect itself against the “anecdotal” loss of knowledge that occurs when personnel turnover happens.
Why is intellectual capital lost when a knowledge worker leaves?
It is because most organizations do not have a “brain” that has captured the employeeâ€™s history, or their personal understanding of their tasks and experiences and interpreted it to a degree that allows you to make decisions that the employee would have made without missing a beat. Knowledge Interpretation Software (KIS) brings you to a point just shy of absolute Wisdom. It does not make any decisions for you, but it provides a knowledge sufficient to provide a vision of the principles that the employee was using to set strategic and tactical direction for their contributions to the company. KIS software is powerful because it collects data from across the enterprise. As soon as the data is captured, KIS categorizes it and makes it immediately available to all the operations of the company. In the case of the sudden absence of a key employee, the company is not left with a deficit in its intellectual capital. The software has collected all the activity of the employeeâ€™s function, including peripheral interaction so that the remaining employees can easily take up the slack. KIS never stops working. It is always active. It distributes its interpretations equally because its core is not just a repository, but an interpretive process.
Knowledge Interpretation Software does not provide absolute Wisdom. It does however, have the ability to create interpreted knowledge to facilitate the decision making process of all the end users in the organization.
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