At the end of the day, knowledge management
consists of two main sections in the Information Technology world: the
relationship between Business Units (Capital) and Technical Units (Labor), with
knowledge management positioned right in the middle of these two entities.
Focusing specifically on the Technical leg, and more specifically on Data
Management, I have not found any model that includes Data Enablement as a
function. This also applies to Knowledge Enablement.
Data Management manages the lifecycle of data,
ensuring it’s accurate, secure, and compliant.
The misconception is that Data Governance manages these functions. Data
Governance defines policies, standards, and stewardship for managing data and
ensuring its quality, privacy, security, and compliance. In contrast, Data
Enablement makes data accessible, usable, and actionable for decision-makers
across the organization. Knowledge Enablement, on the other hand, facilitates
the capture, sharing, and application of organizational knowledge to improve
performance and innovation.
The goal of Knowledge Enablement is to enhance knowledge sharing and
enable employees to make better decisions by leveraging internal expertise,
best practices, and historical knowledge. Knowledge management is therefore not
just theoretical but also closely tied to practical application. Knowledge
Enablement acts as the translator between Business Units (Capital) and
Technical Units (Labor). This relationship is built on the grounding principle
of Trust.
As an example. “In a key strategy meeting with senior management, the team was reviewing technical results from the data science department regarding a new predictive model aimed at forecasting customer demand. As the executives looked at the complex graphs, statistical outputs, and machine learning metrics—such as feature importance scores and model accuracy—they struggled to grasp the significance and business implications of the results. The room fell silent as confusion set in. Recognizing the challenge, the head of operations, with a strong understanding of both business and data, stepped in. He accessed the company’s knowledge repository, a centralized system that housed technical documentation, case studies, and simplified explanations of common data science concepts. By referencing this knowledge, he was able to clarify the results in plain language, explaining how the model's predictions could directly improve inventory planning and reduce stockouts. He also connected the findings to previous successful applications of similar models, helping the management team see how these insights could drive actionable business decisions. With the newfound clarity, the team was able to make informed decisions and align on next steps, turning a potentially frustrating meeting into a productive one.”
This achievement was made possible by the Knowledge Enablement Business Unit. Is this not Data Enablement? While it may seem like data enablement, knowledge enablement goes beyond just providing data. It ensures that the essential context, interpretation, and understanding are delivered, helping individuals use the information effectively in decision-making. Essentially, it’s about knowledge management—capturing both memory (the data itself) and meaning (the context and insights) to guide actions and drive business outcomes.
Here a summary table with more information:
