Content related to Tesfaye and Majumder Speaking at EDW 2025
Semantic Search Advisory and Implementation for an Online Healthcare Information Provider
The medical field is an extremely complex space, with thousands of concepts that are referred to by vastly different terms. These terms can vary across regions, languages, areas of practice, and even from clinician to clinician. Additionally, patients often communicate … Continue reading
Content Management Strategy for a Capital Producer
A capital producer understood the complexity of navigating international regulatory environments. Operating across nations in numerous fields of specialization, the organization had to uphold diverse and disparate ordinances, many of which have changed over time. Dedicated to providing high-quality services to their customers, the organization sought a solution that would help them better navigate revisions to compliance requirements and ensure adherence to rigorous standards of excellence. Continue reading
How Sustainability Grows from KM
Sustainability is becoming increasingly important to businesses’ fiscal outcomes. Sustainability is the practice of enhancing environmentally-friendly practices in an organization, such as by reducing energy consumption or water usage. Today’s consumers value sustainability as a core principle when making purchasing … Continue reading
A Practical Guide To Knowledge Transfer Interviews
Organizations often wait too long to target and capture the lessons learned and takeaways gained from senior leaders’ experience and tenure. As a result, when senior executives leave or retire, key nuggets of institutional knowledge often leave with them. Knowledge … Continue reading
Knowledge Cast – Jeff Vargas, formerly of Paramount
Enterprise Knowledge CEO Zach Wahl speaks with Jeff Vargas, former Director of Knowledge Management at Paramount. In this conversation, Zach and Jeff discuss being KM evangelists and the trick to selling KM programs to executives. They get into “invisible KM,” … Continue reading
Optimizing Historical Knowledge Retrieval: Leveraging an LLM for Content Cleanup
Enterprise Knowledge (EK) recently worked with a Federally Funded Research and Development Center (FFRDC) that was having difficulty retrieving relevant content in a large volume of archival scientific papers. Researchers were burdened with excessive search times and the potential for knowledge loss … Continue reading
Establishing a Scalable Knowledge Management Strategy and Solution Framework for a Leading Automotive Manufacturing Company: A Case Study
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
Graph Analytics in the Semantic Layer: Architectural Framework for Knowledge Intelligence
Introduction As enterprises accelerate AI adoption, the semantic layer has become essential for unifying siloed data and delivering actionable, contextualized insights. Graph analytics plays a pivotal role within this architecture, serving as the analytical engine that reveals patterns and relationships … Continue reading
What is a Knowledge Asset?
Over the course of Enterprise Knowledge’s history, we have been in the business of connecting an organization’s information and data, ensuring it is findable and discoverable, and enriching it to be more useful to both humans and AI. Though use … Continue reading
Beyond Traditional Machine Learning: Unlocking the Power of Graph Machine Learning
Traditional machine learning (ML) workflows have proven effective in a wide variety of use cases, from image classification to fraud detection. However, traditional ML leaves relationships between data points to be inferred by the model, which can limit its ability … Continue reading