Content related to Learning 360: Crafting a Comprehensive View of Learning Content Using a Graph
Knowledge Cast – Ashish Kanal, Founder and Independent Advisor in KM and L&D
Enterprise Knowledge CEO Zach Wahl speaks with Ashish Kanal, a founder and independent advisor currently working at the intersection of Knowledge Management and Organizational Learning. Over 24 years across life sciences, chemicals, oil and gas, IT, and more, Ashish has … Continue reading
Knowledge Cast – Melanie Adams, Global Talent Development & Training Operations Lead in Digital Manufacturing Operations at MSD
Enterprise Knowledge CEO Zach Wahl speaks with Melanie Adams, Global Talent Development & Training Operations Lead, Digital Manufacturing Operations, at MSD. With a deep background in science, leading analytical testing teams, and ensuring quality in regulated pharma environments, Melanie blends … Continue reading
What is the Difference Between a Semantic Layer and a Context Layer? When to Use a Knowledge Graph vs. a Context Graph
Before AI became part of everyday conversations, most enterprise knowledge and data projects had a somewhat straightforward goal: to create a “single source of truth.” In theory, this meant that everyone in the company could look at the same search … Continue reading
Leveraging a Semantic Layer for Research Curation and Conversational Experiences
The Challenge A global philanthropic organization focused on health programs struggled to fully leverage knowledge from semi-structured and unstructured documents. Specifically, within a health-related funding program, researchers lacked access to key qualitative data from end-user surveys and transcripts. Consequently, they … Continue reading
Building the Semantic Layer: Scaling Enterprise Intelligence at a Global Investment Firm
The Challenge A global investment firm with a $330 billion dollar portfolio and 50,000+ employees struggled with fragmented data. Investment professionals were losing critical time hunting for assets across disconnected systems. Detailed deal records were scattered as a mix of … Continue reading
GraphRAG in the Enterprise
Retrieval-Augmented Generation (RAG) is a commonly utilized pattern for grounding large language models in enterprise data. Instead of solely relying on a model’s training, RAG collects relevant information from internal sources, documents, knowledge bases, and other systems; it then uses … Continue reading
Optimizing Historical Knowledge Retrieval: Extracting Knowledge by Making Connections
The Challenge From POC to Production A Federally Funded Research and Development Center (FFRDC) faced significant challenges with low-quality or incomplete metadata for managing and cataloging scientific reports, hindering researchers’ ability to parse repositories and efficiently discover relevant content. As … Continue reading
What Are Explainable AI Knowledge Portals and Why Do You Need Them?
Knowledge Portals first came into prominence in the early 2000s. These initial portals were a collection of static links to information on everything from HR systems to company policies and communications. It was a single location to access the systems … Continue reading
Graph Database Evaluation for a Financial Services Firm
A financial services firm built a mature graph data ecosystem, but the graph database they selected originally did not scale as multiple business-critical solutions relied on graph data. As application and business teams across multiple stakeholder groups expanded usage … Continue reading
Taxonomy Alignment for SDG Tagging
In the session “Utilizing Taxonomies to Meet UN SDG Obligations“ co-presented at KMWorld 2025 on November 17th, Enterprise Knowledge’s Ben Kass and ASHA’s Mike Cannon discuss how they structured taxonomies within an ongoing auto-tagging implementation to serve content management and … Continue reading