In the last post I discussed the application of Digital Twins to people, modelling their physiological characteristics, with obvious application in Healthcare and in the previous one in this series the use of Digital Twins to model knowledge of machines, Cognitive Digital Twins -CDTs. It is time now to consider the management of the knowledge through CDTs and to apply CDTs to people, to mirror the knowledge of a person.
Knowledge management pre-dates the idea of using CDTs. Companies, notably the HR area, have records of their employees describing their knowledge/experiences as collection of information on their education, the courses they took, projects they have been involved (and roles). They also have well documented records of the processes being used in the company to execute the various activities, from procurement to sales. Most companies have records of their preferred providers (some are certifying their providers, meaning basically that they assess the skills, type of products that can be acquired, their characteristics, the capability to deliver and so on).
All of the above is about tracking knowledge. Part of this tracking is supported by tools and is formalised, other parts consist just of data that can be accessed by the company (employees) to take decision (whom to allocate to a specific project, whom to ask for procurement…). This is represented in the graphic with the claim that all this knowledge could be formalised into CDTs and these CDTs can be used to take decisions.
An important aspect of knowledge management, from a company’s standpoint, is understanding what is missing, what is becoming obsolete and what actions can be taken to fill the knowledge gap.
This is one of the motivation to adopt CDTs (more futuristics ones will be addressed in following posts). Suppose a company needs to launch a new project to deliver a new product / service on the market. Given the rapid pace of evolution it is most likely that the knowledge space required for the project cannot be filled with the knowledge owned by the company (the knowledge space of the company is represented in the graphic, so it does include, as example, the knowledge of start ups having the advanced technology that would make a difference in the production of the new product). Hence, the first step is to assess what kind of knowledge is available (among the one useful for the project). This can be assessed using AI tools and comparing it with the knowledge formalised in the CDTs.
Once the gap(s) is identified the following question is how to fill the gap(s). From a company standpoint it is a matter of economics (cost vs effectiveness) within a range of constraints. As an example a company may
- change a supplier,
- hire a consultant,
- re-train the existing workforce,
- seek for extending its labour force bringing in the right knowledge,
- acquire “artificial” knowledge, i.e. the one provided by machines,
Once CDTs are adopted in the context of the enterprise KM we can turn the above list into:
- Acquire new CDTs
- Expand existing CDTs
As it can be seen CDTs are virtualising knowledge by moving it to the cyberspace (digital transformation of knowledge) and it is irrelevant the form of the “physical” container of knowledge. This is great, but at the same time it opens up difficult issues.