In today’s world, where businesses are overwhelmed with information, the ability to intelligently sift through and utilize data distinguishes successful companies from the rest. Indeed, this is precisely where knowledge management (KM) excels. Now more than ever, cutting-edge KM strategies are harnessing the synergy of emerging technologies. At the forefront are three pivotal technologies: artificial intelligence, data analytics, and knowledge graphs. This technological synergy is undeniably transforming the landscape of knowledge management. Furthermore, these groundbreaking advancements in automating KM are more than just pushing boundaries. In fact, they are redefining how companies leverage their collective intelligence to drive innovation and assure a competitive edge.

In this article, I’ll look at the essential features of KM automation. Also, I’ll detail the explosive synergism of AI, data science, and graph tech. Moreover to fully leverage these data-intensive technological advancements, I’ll explain the importance of taking on a data-centric mindset for implementing knowledge management automation versus an application-centric approach. Lastly, I’ll provide eight examples of how data-intensive industries such as supply chain and ecommerce can leverage knowledge management automation.

1. Knowledge Management Basics and the Essential Automation Features For Business Success.

The end goal of knowledge management (KM) automation is to disseminate collective insights of the organization with precision and ease. Before getting into the details, let’s first review what knowledge management is. From there, I’ll then examine what are the essential automation features that support knowledge management.

a. What is Knowledge Management?

Here’s a definition of knowledge management.

“Knowledge management (KM) is the collection of methods relating to creating, sharing, using and managing the knowledge and information of an organization. It refers to a multidisciplinary approach to achieve organizational objectives by making the best use of knowledge.”

Wikipedia

Without a doubt, knowledge management is critical for both large enterprises and even small businesses. Indeed, knowledge is part of an organization’s DNA. This is because knowledge management is the foundation for retaining and maturing a company’s culture. As a result, its the organization’s knowledge that drives both productivity and efficiency. In addition, a company’s intellectual property provides the basis for a company’s innovation, competitive advantage, and continued success. 

At the same time, many companies are not familiar with the term knowledge management, but all companies practice it in some form or shape. For instance, management creates repeatable processes such as new employee training. That’s knowledge management. Another example of KM is where the customer service department maintains frequently asked questions (FAQs) documentation.

For a more detailed discussion, see the following knowledge management references:

• Unvarnished Facts article, Knowledge Management For Businesses: Ways Emerging Tech Empowers It.
• Cory Cannon’s article, Why Knowledge Managers Should Lead Data and Data Science within Organizations

b. Essential Features of Knowledge Management Automation.

In many organizations, knowledge management efforts often lead to outdated wiki pages that teams neither have the time to update nor the confidence to trust. But it doesn’t have to be this way. Today’s knowledge management (KM) automation empowers organizations with advanced tools and autonomous workflows. For instance, KM automation can respond to questions, assign knowledge tasks, verify responses, add new information, curate content, and share knowledge seamlessly. Below are two lists, one traditional and one advanced, of essential features that you should expect from modern knowledge management automation.

Traditional Knowledge Management Automation Features

For more discussion about knowledge management automation features, see Ayanza’s 12 Best AI-Based Knowledge Management Systems, Tettra’s How AI Knowledge Management Will Impact Your Business, and Scribe’s Smart Solutions: Navigating AI Knowledge Management.

2. The Transformative Synergy of AI, Data, and Knowledge Graph Tech Applied To Knowledge Management.

The fusion of AI, data analytics, and knowledge graph technology represents a transformative synergy in the field of knowledge management. First, AI excels at pattern recognition, predictive analytics, and natural language processing, making it highly effective at interpreting and analyzing vast amounts of data. When combined with knowledge graphs, which provide a structured and relational framework, the result is powerful KM automation that mimics human cognitive processes. To list, below are the key synergies that occur when businesses integrate AI, data, and knowledge graphs.

The Synergies Between AI, Data, and Knowledge Graphs

• How Does Knowledge Graph (KG) Tech Structure Data? Basically, knowledge graphs structure data to represent the entities of interest, and the edges represent the relations between the entities (see diagram for graphical depiction).
• 4 Ways A Knowledge Graph Can Help AI Be More Discerning, Contextual, and Trustworthy. Examples includes: 1) KG helps train AI; 2) AI create KGs; 3) KGs enrich AI’s queries and responses; 4) KGs empowers AI with digital common sense.
• What New Capabilities Does Knowledge Graph AI Offer Businesses? This includes: 1) fact verification; 2) fact ranking; 3) related entities; 4) entity linking.

For a more detailed discussion of what I call Knowledge Graph AI, see my article, Knowledge Graph Tech: Enabling A More Discerning Perspective For AI.

Knowledge Graph Tech: Enabling A More Discerning Perspective For AI

3. A Data-Centric Mindset Needed For Enterprises To Leverage KM Automation.

To fully benefit from data-intensive technologies like AI, data analytics, and graph tech, businesses need a data-centric mindset. This is especially true for knowledge management where data is the foundational building material. The problem is that most organizations today are application-centric, treating data as a by-product and locking it in silos within outdated software. Consequently, this leads to disjointed knowledge management. Without a doubt, it’s time for businesses to adopt a data-centric approach. For instance, the tenets of datacentricity include: 1) data is a most valuable corporate asset; 2) data first, not applications; 3) avoid data silos, stay agile; and 4) reduce cost and complexity to name a few.

Now, this transition to a data-centric mindset will not happen overnight, but organizations can start reaping the benefits of being data-centric immediately. These data-centric benefits include:

Data-Centric Benefits: Businesses Becoming More Innovative By Not Being Mired In Application Centricity

The Benefits of Being Data-Centric

• Superior Business Agility: Not Chained To Data Silos Or Legacy Applications.
• High Confidence In Data: There Is A Single Source Of Truth (SSOT) Vs Multiple Versions Or Copies.
• Improved Decision-Making: Having High-Quality Data that is Complete, Accurate, Timely.
• Simplified Software: Lower Costs, Increase Reuse Of Code.
• Faster Adoption of Needed Technologies: Including AI, IoT, and Other Data-Centric Information Technologies.
• Streamlined Data Security, Integration, And Analytics.

For a much more detailed discussion on what is a data-centric mindset and its benefits, see my article, Data-Centric Benefits: Businesses Becoming More Innovative By Not Being Mired In Application Centricity.

4. How Businesses Are Using AI, Data, And Knowledge Graphs To Boost Knowledge Management.

This Is What Decision Intelligence Technology Is And Know What Its Not

Forward-thinking businesses are leveraging AI, data, and knowledge graphs in concert to transform their knowledge management practices. For instance, AI technologies such as Machine Learning (ML) and Natural Language Processing (NLP) can automate the process of extracting and classifying knowledge from vast amounts of data. Similarly, data science tools can analyze complex datasets to uncover patterns and insights, aiding in knowledge discovery and decision making. Further knowledge graphs can organize data from multiple sources, capture information about entities (like people, places or events), and forge connections between them.

Below are eight examples of how Knowledge Graph AI can transform the way organizations can leverage knowledge management automation. 

Knowledge Graph AI Tech Use Cases

Superior Knowledge Management: The Best Ways That Knowledge Graph AI Can Empower Businesses

• Conversational Chatbots for Knowledge Management Access
• Streamline Data Analysis for Knowledge Management
• Automated Content Indexing
• AI-Driven Predictive Analytics Using Knowledge Graphs
• Personalized Recommendations Using Knowledge Graph AI
• AI-Augmented Collaboration and Knowledge Sharing
• Document Management Assisted by AI Agents and Knowledge Graphs
• KM Platform Answering Advanced Reasoning Questions Using Knowledge Graph AI

Also, read my article, Superior Knowledge Management: The Best Ways That Knowledge Graph AI Can Empower Businesses. This article details these eight Knowledge Graph AI use cases to include references.

More References on Knowledge Management, Knowledge Graphs, and AI.

For more information on knowledge management and how emerging tech is changing KM, see the following references:

• Knowledge Management: Tettra’s What Is Knowledge Management?; LeewayHertz’s AI IN KNOWLEDGE MANAGEMENT; Forrester’s How Generative AI Is Affecting Knowledge Management
• Data Science: SC Tech Insights’ Data Science Definition – The Truth About This Discipline And Its Massive Growth
• AI: 10 Examples Of Artificial Intelligence Technology That Will Empower Your Business.
• Knowledge Graph Tech: SC Tech Insights’ Knowledge Graph Tech: Enabling A More Discerning Perspective For AI

Knowledge Graph Tech: Enabling A More Discerning Perspective For AI

For more from SC Tech Insights, see latest topics on Data, AI, and Decision Science.