All Posts

New Age of Knowledge Management

Knowledge management is experiencing a resurgence because of the growing awareness that not only can it solve current problems, but it can also help organizations prepare for the artificial intelligence (AI) solutions of the future. To find the best path forward, organizations need to understand the connection between knowledge management, knowledge engineering, knowledge architecture, and artificial intelligence.

New holy grail for KM - anticipating user needs

Fundamentally, knowledge management is about getting the right information to the right person at the right time. We hear this over and over, and it has always been the Holy Grail for knowledge management, but the ways in which it is being done are constantly changing. In the past, for example, knowledge management was about organizing information and using search engines so people could find what they needed more quickly and easily.

Now it’s about anticipating what they need and getting it to them in an automated way.

Part of this process involves filtering out the information that is extraneous—content that is not relevant to a particular customer. I was with a client whose organization is managing 800,000 pieces of content, and they need to be able to get people just the right piece of content. This is in the entertainment industry, so people’s choices are very subjective. They are developing ways of using AI to achieve personalization in a context where there are more subtleties than there are in something like grocery shopping, where purchase patterns are easier to detect.

Make things easier for users

The goal is to make it easier for people to do things--use your website, or your application, or your services--by representing an individual’s mental model in your knowledge architecture. The system needs to be able to anticipate something the users need, and it can do that only if information is correctly mapped against the user’s model. This ability is an evolution of technology, which has always been about reducing the cognitive load. Technology should make it easier for people to accomplish their task, achieve their objectives, find a solution or find an answer.

Knowledge management is a way of sharing, enabling collaboration in order to solve problems. It's really about interacting with other humans, and gaining access to enterprise information. Knowledge architecture allows us to support knowledge management, because it reflects the mental model of the users, and knowledge engineering is the way to componentize, ingest and reuse that content. Knowledge engineering involves the mechanisms for deciding how to structure that information so it can be ingested by enterprise systems. 

Knowledge architecture

One of the challenges around enterprise knowledge is that many times it's unstructured and uncurated. There's a lot of inefficiency in using that type of information. When we put it into a knowledge architecture, however, we are helping to improve the reuse and reduce the costs of managing and using it. Knowledge is usually distributed across different departments and processes. With a correctly built architecture, aggregating and retrieve knowledge can be accomplished much more easily. It doesn't have to be put in one place. What’s needed is an architecture that will allow users to retrieve that content wherever it may live. Sometimes some harmonization of the different structures is required, and a way to reconcile the different terminology from those diverse applications.

A great deal of enterprise knowledge is also embedded in systems and in enterprise processes. And the expertise of humans--tacit knowledge--is the stuff that people walk around with in their heads. Explicit knowledge is documented in some way, but tacit knowledge is not really accessible until it becomes explicit. With respect to tacit knowledge, people always know more than they can say, and they can say more than they can write down. That's just the way knowledge works. I can answer lots of questions when we have a conversation, but I know more than what I'm telling you.

Knowledge engineering

When we can componentize that knowledge we can actually break it up and drive it into various functions. It can be used for self service, or to solve day-to-day problems. We are making it more findable for humans and more digestible for the systems. So if you think about the types of applications that require question answering, such as a chatbot or intelligent virtual systems, the information you need to train that chatbot is the same information you need to train humans.

Read: Knowledge Engineering - Structuring Content for Artificial Intelligence

Training the systems

When people characterize algorithms as not needing any architecture, that's a bit of a misstatement. Even when the algorithm does not require any type of structure and can just operate on the content, we still have to give it some data. The particular pieces of data that will go into that training set need to be identified in some way. So in other words, the training data is really the knowledge and the content and the curated information that has to go into these algorithms.

The content needs to be classified in some way for it to be useable.

If a system is being trained to recognize images of a cat, it must be shown many examples of cats, and examples of animals that are not cats. What is it that defines “cat-ness”? The cats have certain features, and in machine learning parlance, features are actually metadata. You look at cats from different angles, different lighting or conditions. The system can learn to distinguish them from other animals. But at some point you have to call it something, tell the machine what it is, and the classification is “cat.” Then you tell the machine to go off and find more images of cats. That is the process.

Hypothetically, you can train a system to do something like find images that show defects in manufactured parts without any metadata being applied. But then how do you do something with that information? What parts are they? What processes are affected? What departments make them? In order to remediate the defects and use the insights from that machine learning algorithms, the information must be contextualized and the insights apply to the right processes and departments, and it is the knowledge architecture that allows this to happen.

Watch the webcast: Grow Revenue and Improve Operations with Visual Search

Where AI projects get stuck

One of the organizations we're working with is a large process manufacturing company that has hundreds of thousands of data points and hundreds of thousands of machines, equipment, and tech tools that produce data. They found they were not able to get to the next level of leveraging AI machine learning without having a knowledge architecture. The roadblock they ran into was what to do with the results. Who owns them? How do you change your procedures and feed that information back into the organization? People cannot act on the data without having a knowledge architecture that provides context.

To recap, knowledge management is really about collaboration of the culture, processes, and getting people together to leverage their expertise. Knowledge architecture is the mental model of the user, how they think about the data. What are they trying to do with the information—set up a marketing campaign, develop new products, or improve customer satisfaction? Do they think about the data by client, product, or chronologically? And finally, knowledge engineering provides the mechanisms that guide how we structure information, so that it can be ingested into a system, and ultimately inform the knowledge management process. All of these need to work together to support the rapidly growing applications of artificial intelligence, but in the meantime, they also help improve human performance.

To learn more about how we use information architecture as the foundation for digital transformation read our whitepaper: "Knowledge is Power: Context-Driven Digital Transformation

Seth Earley
Seth Earley
Seth Earley is the Founder & CEO of Earley Information Science and the author of the award winning book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable. An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions. He has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance.

Recent Posts

The Future of Bots and Digital Transformation – Is ChatGPT a Game Changer?

Digital assistants are taking a larger role in digital transformations. They can improve customer service, providing more convenient and efficient ways for customers to interact with the organization. They can also free up human customer service agents by providing quick and accurate responses to customer inquiries and automating routine tasks, which reduces call center volume. They are available 24/7 and can personalize recommendations and content by taking into consideration role, preferences, interests and behaviors. All of these contribute to improved productivity and efficiency. Right now, bots are only valuable in very narrow use cases and are unable to handle complex tasks. However, the field is rapidly changing and advances in algorithms are having a very significant impact.

[February 15] Demystifying Knowledge Graphs – Applications in Discovery, Compliance and Governance

A knowledge graph is a type of data representation that utilizes a network of interconnected nodes to represent real-world entities and the relationships between them. This makes it an ideal tool for data discovery, compliance, and governance tasks, as it allows users to easily navigate and understand complex data sets. In this webinar, we will demystify knowledge graphs and explore their various applications in data discovery, compliance, and governance. We will begin by discussing the basics of knowledge graphs and how they differ from other data representation methods. Next, we will delve into specific use cases for knowledge graphs in data discovery, such as for exploring and understanding large and complex datasets or for identifying hidden patterns and relationships in data. We will also discuss how knowledge graphs can be used in compliance and governance tasks, such as for tracking changes to data over time or for auditing data to ensure compliance with regulations. Throughout the webinar, we will provide practical examples and case studies to illustrate the benefits of using knowledge graphs in these contexts. Finally, we will cover best practices for implementing and maintaining a knowledge graph, including tips for choosing the right technology and data sources, and strategies for ensuring the accuracy and reliability of the data within the graph. Overall, this webinar will provide an executive level overview of knowledge graphs and their applications in data discovery, compliance, and governance, and will equip attendees with the tools and knowledge they need to successfully implement and utilize knowledge graphs in their own organizations. *Thanks to ChatGPT for help writing this abstract.

Innovation with Utility in Mind - What’s Ahead for 2023

It has been a year of advances in AI with new tools that create art, write essays, and have conversations that are often (but not always) startlingly eloquent.  It is that space of "not always" that keeps these tools from working out of the box to solve actual business problems without human intervention.