Actionable Knowledge
by
Joe Williamson
The potential benefits of knowledge management have led enterprises to strive to implement knowledge management initiatives for themselves in the hopes of achieving the idealized picture of an agile enterprise. Many of these initiatives have not achieved their desired results. The reason being predominantly because management is focused on the installation of an information technology solution, and they have little consideration for the social architecture of the enterprise and the ability to extract actionable knowledge from the information system.
In the agile enterprise, it is increasingly vital that we have the right knowledge anytime, anywhere, and in the form and context we desire in order to effectively do our job. Underlying this ability, we must be able to anticipate the need for a new capability and make it available to the rest of the enterprise so that it becomes an accepted offering without increasing the friction in delivery organizations. Weaving the right set of people into the fabric of the enterprise can be an art form. They have to be focused on the task at hand, working in the right work environment guided by the right kind of leadership and strategy. The only way this agility can emerge is through learning.
Agile enterprises rely on continuous improvement and innovation. Learning is emerging as a significant economic variable and has been fuelled by factors such as the speed of technological change, trends towards globalization and growing corporate competitiveness. Furthermore, organizational learning is seen as a critical complement to managerial theory, because it is through learning that complexity is managed. These factors combine to propel learning to the forefront of corporate competitiveness.
Despite our new Agora, boosting the ability for knowledge generation, codification, and transfer, knowledge generation and transfer within an enterprise remains a significant challenge. On the surface, certain factors contribute to this challenge such as economic constraints (the high cost of providing and maintaining tools and information sources on an enterprise-wide basis), lack of time, “infoglut,” heterogeneous information resources that are semi-structured and enormous, and misplaced or lost knowledge.
Knowledge is lost if it can not be found. As experienced people retire and today’s workforce becomes even more mobile, the knowledge lost to an enterprise is not some half-remembered fact or blueprint. It is the absence of mythologies, the relationships of detail to purpose, and the patterns of rationale that make plain the choices and effects of a particular set of tradeoffs. When experienced individuals retire or relevant information cannot be located, assessing knowledge artifacts such as documents or the memory of a few individuals cannot restore the knowledge. It is their memory of the documents, even temporary working notes once used, which has the greatest payoff. The knowledge that was the rationale employed to formulate the decision (options and trade-off) is lost usually within the first two weeks.
One particular methodological approach—text mining—can be used to enhance the effectiveness of an organization’s knowledge management system. Text mining technology is effective because it is an objective analysis (reliable) of existing knowledge assets (cost effective) in a rapid manner (timely). Current text mining systems are too focused however on the traditional “search and categorize” problems. In order for knowledge management systems to create value, they must point to actionable knowledge. We show how deep analytics can be used to generate actionable insight from text mining and how entity extraction can be used to illuminate the organization’s underlying social architecture.
