Content related to Employee 360 Views: Common Use Cases
Knowledge Cast – Wael Taha, Vice President of Enterprise Architecture at Brown Brothers Harriman
Enterprise Knowledge’s Lulit Tesfaye, VP of Knowledge & Data Services, speaks with Wael Taha, VP of Enterprise Architecture at Brown Brothers Harriman. Over the past 12 years, Wael has dedicated his career to architecting D&A solutions and directing D&A professionals … 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
Knowledge Cast – Bridging Knowledge, Data, and AI by Zach Wahl, Joe Hilger, and Lulit Tesfaye
In this special episode of Knowledge Cast, Zach, Joe, and Lulit pass the mic to Senzing‘s Paco Nathan, who interviews them about their new book Bridging Knowledge, Data, and AI: Harnessing the Semantic Layer Framework to Drive Intelligence. Paco guides … 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
Knowledge Cast – Lasse Andresen, Founder & CEO of IndyKite
Enterprise Knowledge COO Joe Hilger speaks with Lasse Andresen, founder and CEO of IndyKite Inc., the first system of intelligence built on a live context graph. The result is agentic AI that can operate across platforms with precision and deliver … 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
Taxonomies in the Age of AI: Evolving Your Strategy
The Topic Taxonomy: An Outdated Artifact? As knowledge workers continue to navigate constantly evolving priorities in developing effective AI solutions that complement organizational priorities, semantics have maintained their value—but not without shifts that deserve our attention. Broadly speaking, one reliable … 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