Bringing the future to Knowledge Management: Data Science

Covid19 has certainly caused many unexpected changes, in the workplace as well as in personal life. For example, we find ourselves talking 'data' more frequently: we discuss the infected rate, how many patients were infected and their current state, and which data indicators provided are more or less reliable. This has led many to refer to Data Sciences as the next new thing.

For years, as KM personnel, we learned to clearly differentiate ourselves from "them", i.e. the data personnel. We emphasized the pyramid that is data-information-knowledge. Some even added a cherry at the pyramid's tip, the enticing component referred to as 'wisdom'.

Yet, data workers have never limited themselves with such definitions, and rightfully so. They did not only deal with quantified data represented sums and amounts; they included, and still do, any type of input that might contribute to their research (since they are, as the title implies, a science) as relevant.

Data Science, like Knowledge Management, is an interdisciplinary profession. It involves scientific methodologies as well as computerized tools and algorithms. These are all utilized to extract knowledge and insights from data. The data's format may vary. They can be either audio, picture, numbers, bytes, or texts. The content sources can vary as well: from device's sensors to cameras; classis databases to the content on organizational networks and the internet.

Knowledge Management is part of Data Science:

When defining data collection, info ethics, content cataloging and demarcation of the collected data are all vital.

During the processing stage, cataloging becomes extra important. Other KM tools can be incorporated as well, especially when handling content derived from texts.

The analysis stage requires examining what new knowledge can we learn, and how it relates or negates existing knowledge.

During the visualization stage, we suggest how to optimally make the products of our learning accessible, so to enable new insights.

During the decision-making stage, based on the new knowledge, we attempt to document the knowledge, the decisions, and their rationale, and attain continuity of the organizational memory.

Finally, before we begin anew, we stop; we learn lessons from the processes performed and specify courses of action for future studies.

Knowledge Management is an integral part of Data Management. As such, the latter essentially relies on the former.

A new, fascinating world awaits us. There are new opportunities to seize, and the sky seems to be the limit.