They say any publicity is good publicity. So with that in mind, thank you Seth G … thanks for citing my blog post.
I understand that this is a little friendly debate between Seths (perhaps Seth Maislin can chime in). And I know you are being provocative.
The challenge, of course, is one of labeling and interpretation (coming from a firm that deals quite a bit in semantics “to a man with a hammer…” this is a semantic nail.)
'Guilty as Charged'
You are interpreting the label of “knowledge portal” to mean something dated and inflexible, and shame on me for resurrecting such a 90’s technology term. Guilty as charged!
Let’s dig a little deeper behind the term itself.
My insights were a bit buried in the article so let me call them out here. Things have changed since the days when a portal was an interface that simply provided access to content that was static and of marginal quality.
Not Your Father's Portal
A knowledge portal today is a window into an ecosystem of tools, applications and data that leverages knowledge extraction, uses analytics to optimize processes and allows continual improvement in the user experience through machine learning algorithms.
Many portals now have an interface that asks, “What do you want to do?” and begin a dialog with users by presenting options, rather than providing a list of documents in response to a search.
They use search as an integration engine and leverage knowledge graphs, linking content, data, applications and functions through flexible connectors.
They provide collaboration capabilities and present dynamic, contextually relevant, and personalized content in a customized digital destination.
All this cannot happen in a vacuum, so understanding and modeling what is important to the organization and user is part of the process (in the form of domain and data models, terminology around products and processes structured as ontologies and thesauri).
Old Name, New Concept
In calling this approach a knowledge portal, I broke a fundamental rule of marketing, which requires old concepts to be labeled with new names.
Instead I used an old name to represent some new concepts. But even the term “knowledge management” itself has evolved.
To be sure, in the past it was usually almost one and the same with document management and rudimentary communities of practice, but in fact it has long included a focus on business process management and analytics.
Text analytics has also been a component of knowledge management for many years, and focuses on the meaning of the documents and the use of information for such functions as identifying fruitful areas for drug research, aiding in fraud detection, and identifying emerging technologies among many others.
Social media analysis has been a core component of knowledge management from its inception; e.g., the many software products that listen to the “voice of the customer.”
Seth vs. Seth
Much of the apparent discrepancy between Seth Grimes’ vision of knowledge management and my own is more a case of labeling and definition.
I concur wholeheartedly with his five points for the new knowledge management, and agree with Brandon Gadochi’s comment that most companies do not have the kind of platform needed to achieve the seamless integration and utility of knowledge that would be ideal.
Until recently, computers did not have the power to deal with information on the scale of big data or have the clock speed to process it with the real-time speeds needed for today’s requirements of agility.
Most companies that are innovating in such solutions are not defining themselves as “KM providers” but they function in the space of a broader definition of knowledge management.
Machine intelligence and machine learning are fundamental components of these systems. Many people are not aware that core search algorithms use many of the mechanisms that are now being hyped by a new generation of big data, analytics and machine learning solution providers in new configurations and with many refinements.
Accessing a Knowledge Ecosystem
Though there are many great new developments, content and structure around organizational knowledge are the foundation of these systems.
Some of that can be derived, inferred and implied, some requires more intentional structuring and curation.
An organizational knowledge graph (domain models, ontologies, architectural constructs) becomes the knowledge scaffold upon which everything is contextualized. The portal is the window into this knowledge ecosystem.
Perhaps I should have titled my article “the time is right for personalized, contextual, machine intelligence enabled, collaborative, adaptive, analytics and big data driven, knowledge graph and text analytics windows into fast changing, evolving, enterprise information ecosystems.”
It doesn’t roll off the tongue the same way. But that’s semantics.