Knowledge Management Brief

Comment: This brief was originally written in Apr 2009 and has been updated reposting on Dec 21 2013. Knowledge Management, KM, is a broad field that includes not only the storage and recovery of information but the sourcing, mining, and use of information across an operation in order to achieve a durable competitive advantage.   

Knowledge Management Brief
by 
JT Bogden, PMP

Humans have passed through many ages from the Stone Age to the Information Age. With each Age came new ways of living. The Information Age has brought dispersed populations together through internetworking technologies to be productive in new ways. Likewise, business organizations are living, breathing, and conscious entities that requires proper care and feeding in order to mature into a productive knowledge workhorses. Knowledge management is an instrument in this endeavor. The goal of any organization is to skillfully employ resources available in order to achieve durable competitive advantage. 

Knowledge Management (KM) is many things to many people. Nonetheless, KM is a concept in which an enterprise consciously and comprehensively brings to bear its resources to gather, organize, analyze, refine, and disseminate information in order to develop meaningful knowledge.  Most enterprises have some kind of knowledge management framework in place. Advances in technology and understanding often create opportunities to improve KM in practice. However, these technologies and resources are not always applied in the most effective manner.

A conscious organization has the cognizant attribute of reason or the capability to form conclusions, inferences, and judgments in a reasonable amount of time or low organizational latency. Organizational latency is the period of time between discovery and the fulfillment of a need. This requires foundational capabilities to recall experiences and information, form complex information relationships, and then act on newly formed knowledge. Operations such as data mining, referential data storage, and organizational computation capabilities are prerequisites to knowledge management.

Data mining is often used in business intelligence which is the ability to resource then organize information to identify patterns and establish complex relationships to exploit for profit. The assumption is that the information is already collected and just needs to be found then formed relationally. This is not always true and data miners may have to explore outside the organizational information stores. Data mining patterns generally fall into five categories:

1. Associative: Looking for patterns where one event is connected to another event.
2. Sequence or path analysis: Looking for patterns where one event leads to another later event.
3. Classification: Looking for new patterns.
4. Clustering: Finding and visually documenting groups of facts previously unknown.
5. Forecasting: Discovering patterns in data that can lead to reasonable predictions about the future. Often viewed as predictive analysis which generally has present and future models

Referential data storage is an organization’s memory. Critical to effective recall is how the organization structures data storage. Innumerable approaches have been developed. Files have been stored in shared directories, databases of various forms have emerged, automated document storage and retrieval systems maintain document control, and artificial intelligence systems build experience. Highly advanced storage systems use spherical reference methods as opposed to rectangular retrieval methods as well as holographic storage technologies.

Organizational computational power is the cumulative processing capability of an organization that includes both humans and machines. Elemental computational power can be in parallel, arrayed, and/or distributed. Moreover, the elements can be thought of as neural agents either human or machine. Nonetheless, the combinational sum of this power relates to an organization’s horsepower to develop knowledge. There is some debate over how to determine and measure organizational computational power. Machine computational power is often related in terms of instructions processed per unit time. Human computational power is somewhat more difficult and historical attempts have centered on intelligence and emotional quotients, IQ or EQ, as well as processing capability of the biological neural networks. Metrics for knowledge performance in the civilian world are Key Performance Indicators which relate to Objectives and Effects in Effects Based Outcome methodologies. Nonetheless, computational power is a horsepower indicator to solve complex problem sets.

By properly applying methods and processes, an organization can develop knowledge quickly and succinctly for use in its daily operations increasing effectiveness and creating competitive differentials. In doing so, the key measure of effectiveness is a decreasing organizational latency.  The major focus areas for developing meaningful knowledge management systems can be found in how organizational memory, information resourcing, and computational architectures are formed.  The goal of any organization is to skillfully employ resources available in order to achieve durable competitive advantage.