In today’s information-overloaded business landscape, the ability to intelligently harness data is what distinguishes successful companies from the rest. In particular, a superior knowledge management (KM) capability is the key to unlock and capitalize on the full potential of enterprise data. By leveraging the powerful synergy of artificial intelligence, data analytics, and knowledge graphs, emerging KM strategies can revolutionize the way companies operate.

In this article, I’m excited to share my insights on how the explosive combination of AI, data analytics, and knowledge graph technologies are transforming KM. Moreover, I’ll look at the essential features of the KM information technologies and the benefits of using a data-centric approach to exploiting KM tech. Lastly, I’ll provide eight real-world examples of how this tech can transform data-intensive industries like supply chain and ecommerce.

1. Knowledge Management Basics and the Essential Tech Capabilities Needed 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 information technology capabilities 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 crucial for businesses of all sizes. This is because it forms the foundation of a company’s culture, drives productivity and efficiency as well as fuels innovation and competitive advantage. Although many companies may not be familiar with the term, they all practice knowledge management in some way. For instance, businesses create repeatable processes such as new employee training or maintaining 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.

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.

Unquestionably, the fusion of AI, data analytics, and knowledge graph technology offers organizations a powerful synergy to transform the way they do 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

• Knowledge Graph (KG) Tech Structures and Provides Meaning to Data. Basically, knowledge graph tech structures data through “triples” consisting of two entities of interest, and an edge with a “relationship” label linking the two entities (see diagram for graphical depiction).
• Knowledge Graph Helps AI to be More Discerning, Contextual, and Trustworthy. Specifically, this 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.
• Knowledge Graph AI Provides Businesses New Capabilities. For instance, this includes: 1) fact verification; 2) superior contextual understanding; 3) fact ranking; 4) linking related entities; 5) linking Data Disparate Data Sources.

For a more detailed discussion, see my article, 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. Moreover, businesses can start taking on this data-centric mindset today by adopting some key principles as laid out in the Data-Centric Manifesto. These set of principles are endorsed by numerous tech experts and thought leaders. See below for some of its key principles 

Key Principles for a Data-Centric Organization

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

• Data is a key, permanent asset.
• Enterprises are not application-centric. Here, software applications only create, interact, and share data, rather than isolating data for their exclusive use.
• Data is self-describing and does not rely on an application for interpretation and meaning.
• Data is expressed in open, non-proprietary formats.
• Access to and security of the data is a responsibility of the enterprise data layer or the personal data vault, and not managed by applications.

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

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

Need help with an innovative supply chain solution that leverages emerging information technologies? I’m Randy McClure, and I’ve spent many years helping logistics organizations to make the most of new information technologies. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in new strategies, proof-of-concepts and operational pilot projects using emerging technologies and methodologies. If you’re ready to supercharge your supply chain or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.

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