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The New Generation of Knowledge Networks

An exponential opportunity to bridge the cloud-native expert knowledge gap

Demand and supply. These are two of the key principles of modern economics, and they are fully applicable to the current job market. Scarce resources such as specialized talent and knowledge are precious, and they are becoming even more important nowadays because cloud-native and other advanced technologies are being widely adopted.

McKinsey anticipated this situation a few years ago: "Demand for DevOps talent will surge over the next few years. Companies may have trouble finding staff to fill all roles, since 40 percent of survey respondents stated that a lack of internal talent and skills was the primary factor preventing DevOps from becoming mainstream". Adoption has increased, but the availability of talent and the access to specialized knowledge are still the main constraints.

Now, if we want to align these aspects with the expected evolution of cloud-native technologies (including but not limited to DevOps), we need to combine linear talent generation with exponential access to knowledge. Linear talent generation means educating more professionals to learn and adopt these technologies. On the other hand, the notion of "exponential access to knowledge" requires new paradigms to connect humans and machines, and make the most of the previously generated knowledge to facilitate further activities.

In order to support this ambitious mission, we need to build a new generation of "Knowledge Networks". For those who never heard this term before, Prof. Niek Du Preez (Stellenbosch University) defined Knowledge Networks as "a number of actors and resources, where the relationships between them bring about knowledge capturing, knowledge transfer and knowledge creation for the purpose of creating value". Technically, Knowledge Networks enhance knowledge sharing between individuals, groups, and organizations in informal and formal ways. This concept was defined more than 10 years ago, but current technical capabilities such as artificial intelligence and big data make it possible in a very powerful and scalable way.

Let's focus on existing systems such as Knowledge Management tools for enterprises, and Knowledge Sharing spaces such as technical communities and forums. Those are wonderful ways to discuss and exchange knowledge, but the generated information (e.g., new knowledge on specific cloud-native technologies,, interesting discussions, relevant answers to specific technical questions on cloud-native technologies, etc.) is stored in a way that requires more than reasonable effort to retrieve and is dependent on prior knowledge. As a result, new users are essentially cut off from accessing this content.

If we want to enable exponential access to specialized knowledge, we need systems that will allow users to extract information in a seamless manner. We need an evolving Knowledge Network that will collect millions of data points such as technical discussions, documents, and answers, in order to feed the AI-enabled engine with easy-to-adopt interfaces for new technical users to continue learning, collaborating, and exploring existing knowledge. All this combined with intuitive recommendations, generated and based on the fit between the previously mentioned data points, and the targeted knowledge.


The information to build this Knowledge Network already exists across a wide range of technical communities such as Stack Overflow and GitHub and includes more than 25 million data points on cloud-native technologies such as Kubernetes, MongoDB, and Elastic. The next step will be to continue growing the Knowledge Network and to enable cloud-native vendors and millions of developers around the world to interact and generate exponential knowledge… and this is just the beginning.


About the Author

Adrian Gonzalez Sanchez is an AI technology leader who has deep academic and professional experience. As the head of customer success at, the AI recommendation engine for support automation, he works closely with customers to help improve technical support for cloud-native technologies. He previously worked at IVADO Labs, a pioneer in AI development, and teaches a number of technology courses on AI and big data at Concordia University and École des dirigeants HEC Montréal.

Published Monday, May 10, 2021 7:35 AM by David Marshall
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