Content related to Identifying Security Risks Using Auto-Tagging and Text Analytics
Enterprise Semantic Layer for Firm-Wide Risk Management at a Financial Services Firm
One of the top global leaders in automotive manufacturing faced significant challenges in managing and accessing critical knowledge across its diverse teams. The company engaged Enterprise Knowledge (EK) to conduct a Knowledge Management (KM) Strategy and solution implementation project plan after the failure of multiple KM initiatives. The engagement’s long-term goal is to establish a shared Knowledge Management System (KMS) to streamline access to crucial information, better leverage experts’ institutional knowledge and experience, and decrease new employees’ time to proficiency. Continue reading
Steps to Improve AI Outcomes with Context Engineering
When it comes to successfully implementing AI solutions, a common refrain in most implementations is that your results are only as good as your inputs. To avoid this pitfall, we have seen both data and content teams ask “are my … Continue reading
Your AI Can Reach Everything and Understand Nothing
Connectivity is solved. Understanding and precision are not. Why retrieval systems need a semantic foundation. I met up with a colleague at a conference recently. She was newer to the world of semantics and mostly excited by what she was … Continue reading
From Risk to Readiness: Anatomy of an AI-Ready Knowledge Asset
The hidden risk in your AI investment isn’t the technology; it’s the content. The core issue is that humans and AI systems process information differently: humans infer context and build on an ever-expanding web of knowledge; AI systems require explicit … Continue reading
A Practical Guide to an Intranet Remodel: Small Taxonomy Wins for a Big Impact
The world of knowledge management is moving at a fast pace and it is challenging to keep up with trends that often demand investing in the latest tooling to solve increasingly complex semantic challenges. However, for many organizations, particularly non-profits, … Continue reading
Ontology and Knowledge Graph in the Age of AI and Agents
As organizations accelerate investments in AI, semantic data models, advanced analytics, and agentic transformation, lots of jargon gets thrown around, and this sometimes results in confusion about how data driven systems work. In the realm of semantic layers, one of … Continue reading
Why AI Projects Fail Without a Common Language: The Case for Taxonomy Standards
As organizations rush to adopt AI solutions and technologies, the necessary structures to support such solutions are often overlooked. Gartner predicts that by 2026, 63% of organizations will not have the right data management practices for AI. This gap shows … Continue reading
How to Scale a Semantic Layer with Interoperable Ontologies
A Semantic Layer is the framework for connecting data from multiple sources and formats in both a human- and machine-readable way that enables organizations to understand the meaning of their data, extract contextualized information, and discover new insights. A key … Continue reading
A Practical Guide to a Taxonomy Remodel
For anyone who has undertaken any form of home remodel or loves to watch television shows featuring them, the general phases of a home renovation are familiar: visualizing the target state of the remodeled home, carrying out structural work, demolition, … Continue reading
Taxonomies vs. Ontologies for Enabling AI-Readiness
AI solutions need to be grounded in an organization’s context. It is difficult to reliably distill context from the entirety of an organization’s knowledge assets, including facts, documents, datasets, and other structured records. Without a specific directive on what matters … Continue reading