15. Knowledge – Intelligence Providers
Over the last 20 years knowledge has become a valued asset and, as might be expected, a number of companies have endeavoured, and are endeavouring, to capitalise on this value converting it into revenues.
Since its foundation, IEEE has been a facilitator of knowledge exchange among its members first and then opening up to a worldwide audience. Conferences (thousands of them), journals/magazines (in the hundreds) and peer-reviewed paper archive (in the millions) have been the tools offered to a worldwide market to share and access knowledge. In the last 20 years as its members, and the world audience, moved on line the paper medium has been fading away, replaced by electronic media. The sheer volume of data (and related embedded knowledge) has pushed IEEE to develop tools to facilitate access and sharing, like IEEE Xplore and Collaboratech.
In parallel, intelligence agencies, like Gartner, McKinsey and the likes, have created knowledge services to deliver intelligence crafted to the individual customer (matching its business space with the relevant knowledge space).
Artificial Intelligence is taking these two market spaces on a collision course, as knowledge and intelligence becomes one and the same. Moreover, a growing portion of knowledge search, assessment, analyses and intelligence can be fuelled by artificial intelligence.
The world based on archived knowledge and the world based on specific vertical knowledge derived from interaction with key players and self-feeding through consultant services can leverage on different sets of data. However, this difference is fading away as more data (articles and live conference interactions capture) on one side and data accrual from the increased space of consultancy is reaching a point where artificial intelligence can create the metadata, the knowledge and intelligence that is sought by the end customers.
Clearly, the competitive advantage shift from the raw ownership of data to the capability of processing these data contextualising their interpretation and their delivery to a specific customer need.
The latter implies a growing knowledge of the customer and, possibly, the evaluation of the impact of the past knowledge/intelligence transfer to keep fine tuning the delivery.
There are, obviously, many ways to achieve this. For sure one would be to apply the concept of Cognitive Digital Twin. The intelligence provider (knowledge is morphing into intelligence, so even if the knowledge part remains the crucial essential starting point, the intelligence is what is valued and perceived by the end user) can start developing a set of CDTs to deliver the service:
- The CDT embedding the knowledge owned by the provider (this includes the knowledge on where and how to retrieve additional knowledge owned by third parties);
- The CDT embedding the knowledge space of a given vertical sectors (like what knowledge is needed in the energy, or agricultural, or pharmaceutical, or automotive …. sector);
- The CDT instances, one for each customer/potential customer, that inherits from the relevant vertical sector adding the specific knowledge of that specific customer.
The interplay of these CDTs leads to knowledge gap analyses and to the creation and delivery of the needed intelligence to a specific customer.
The architecture supporting these CDTs and their interplay can differ and can become a point of competitive advantage.
As an example, a provider may enter into a partnership framework with its customer and provide the tools to create the CDT instance of that customer (even develop it in house) and then hand it over to the customer. Another provider, possibly claiming that the customer life will be easier if the CDT instance is completely managed by the provider, can just enable the customer to access the relevant CDT instance …