ChatGPT's take first, our take below:
Knowledge management and information management are related but distinct concepts that deal with the organization, storage, retrieval, and utilization of information and knowledge within an organization. Here are the key differences between the two:
Knowledge Management (KM): Knowledge management is primarily concerned with capturing, organizing, and leveraging the intellectual assets and expertise of an organization. It focuses on people, processes, and technology to create an environment where knowledge is shared, utilized, and continuously improved to achieve the organization's goals.
Information Management (IM): Information management, on the other hand, is more concerned with the systematic handling of data and information, often including structured data like records, documents, and unstructured data. IM aims to ensure that information is stored, organized, and accessible when needed, and it emphasizes data governance and compliance.
2. Nature of Assets:
Knowledge Management (KM): KM deals with tacit and explicit knowledge. Tacit knowledge is the know-how and expertise held by individuals, which can be difficult to codify, while explicit knowledge is formalized and documented information.
Information Management (IM): IM is more focused on managing structured and unstructured data, including documents, databases, records, and other forms of information. It is typically less concerned with the personal expertise of employees.
Knowledge Management (KM): The primary goal of KM is to facilitate knowledge sharing and collaboration within an organization, leading to innovation, improved decision-making, and better problem-solving. It is about leveraging the collective intelligence of the workforce.
Information Management (IM): IM focuses on ensuring data accuracy, availability, and compliance. Its main goal is to support operational efficiency, compliance with regulations, and maintaining the integrity of data assets.
4. Processes and Tools:
Knowledge Management (KM): KM involves processes like creating communities of practice, expert directories, and collaborative platforms to facilitate knowledge sharing. Tools might include social intranets, wikis, and expertise location systems.
Information Management (IM): IM includes processes such as data classification, data retention policies, and document management. Tools typically include document management systems, data repositories, and content management systems.
Knowledge Management (KM): KM is broader in scope, as it encompasses not only information but also the expertise and insights of individuals. It often involves cultural and behavioral aspects of knowledge sharing.
Information Management (IM): IM focuses on the structured and unstructured information assets, and it tends to be more transactional and process-driven.
In practice, organizations often combine elements of both knowledge management and information management to effectively handle their information and knowledge assets. These two concepts are interrelated and can complement each other to enhance an organization's overall performance and competitiveness.
Thanks Chattie, thats a good way to break this topic down: Focus, Nature of Assets, Goals, Process & Tools and Scope. Then explaining the differences between KM and IM in each is very good work indeed. You get an A. I can't help but observe though that the two are actually converging, and that wasn't pointed out. So no A+
About the Focus of KM and IM: I remember at the launch of KM World magazine some many moons ago, no one had any idea what the focus of KM was, let alone the magazine. In fact one annoyed attendee stood up and bellowed " I came here to learn about KM and it looks like I accidentally ended up at an AI conference". We all laughed because we knew he was right. The reason he said that is because everyone kept asking "how are you going to do that?", and the answer was always "with AI", which apparently was the magic bullet. By today's standards, it barely existed back then, so not much magic, and not much bullet.
That KM focuses on "on people, processes, and technology to create an environment where knowledge is shared, utilized, and continuously improved to achieve the organization's goals" , was an insane stretch 20 years ago. Connecting all those dots was a loose cacophony of borderline concepts easily summed up as "wishful thinking". Compare that to IM's focus on "the systematic handling of data and information" , in comparison, KM was definitely the gravity belt of tech. One is measurable and achievable and the other is wishful thinking.
Fast forward to today and its a different story isn't it? The tech that generated the above essay is a very good writer and has excellent command of everything it has ever read. It mastered Strunk & White's "Elements of Style", while I daydreamed my way through that class.
About Processes and Tools: Separating knowledge from information, once a big part of "wishful thinking", has been proven to be eminently do-able. As long as the information that knowledge is being extracted from is codified, it can indeed be done. Its certainly what we believe here at Bithoop, and is in fact what we do.
Extrapolating one thing on the basis of another, deducing something from next to nothing, sparks of intuition, sudden solutions to nagging problems, understanding emotion, knowing when to express empathy, expressing a virtue, could probably be codified, but I am willing to bet it would be noticeably hollow because we know "soul", and machines don't. The bigger question is "why bother trying to codify that?"
In my opinion that's where our enthrallment with AI goes off the rails, in terms of its applicability to solve problems and being useful. A lot of AI is being deployed to artificially mimic us, and often applied to do things that we do better, as opposed to supplementing things we do poorly. Look around at how much crappy software now has AI in it. I'm waiting for someone to put ChatGPT into an electric toothbrush or a toilet for the sole reason that we can, as opposed to need or usefulness.
People also need to understand how insanely expensive it all is. No one is really talking about that, but it is a very big, very real problem. The user that wants the "Star Trek" ask any question, get all relevant information summarized, synthesized and delivered along with coffee in the morning interface, might be surprised that they can have all that (minus the coffee) for the mere price of $250,000 a month. Per user!
The challenge for those of us making intelligent tools that people can actually use is not the pie in the sky stuff, its delivering utility where the change in behavior users are being asked for is commensurate with the benefit gained. There is no way around that innovation dilemma.
That's where intelligent search comes into play. Everyone operates on the assumption that search is the ideal interface to deploy AI functionality around. Why can't I just do a search that "finds my medical stuff" ? There are two points here: One you don't have enough money to pay for that, and two, I can remember when people thought of search as a huge bother and an unacceptable change in behavior. If we really had good information management we shouldn't need to do that, was the prevailing thought. It was right up there with the Mac being nothing more than a $2,000 etch-a-sketch.
Maybe search isn't the ideal interface. Maybe its just a tool, a utility to supplement something better. Our approach is to have a user provide a context for what they need and then proactively and predictively delivering things that belong there. That's what a "hoop" is. Its a context. Search is supplemental to that instead of primary. We think doing intelligent information/knowledge management like that is affordable and practical, and we believe better.
About Scope: In my opinion, we are on the cusp of a rapid assimilation of information management by knowledge management. The current state of AI has made it abundantly clear that what we thrive on is knowledge, not an abundance of information.
Bits in managed buckets is an old idea that is rapidly evolving into being more about whats in those bits, as opposed to remembering what bucket they are in. That's not to say that buckets are obsolete, I think that's premature and in fact they are getting bigger and bigger. Whats obsolete is the focus on that kind of information management, as opposed to managing the knowledge that is in there.
I also think that what we have built is a significant step forward in that direction, making it much easier for people to get to the knowledge they need without a huge change in behavior.
October 12, 2023