Content related to Generative AI-Assisted Taxonomy Development for a Global Investment Bank
Enterprise AI Architecture Series: How to Build a Knowledge Intelligence Architecture (Part 1)
Since the launch of ChatGPT over two years ago, we have observed that our clients are increasingly drawn to the promise of AI. They also recognize that the large language models (LLMs), trained on public data sets, may not effectively … Continue reading
Enterprise AI Meets Access and Entitlement Challenges: A Framework for Securing Content and Data for AI
In today’s digital landscape, organizations face a critical challenge: how to leverage the power of Artificial Intelligence (AI) while ensuring their knowledge assets remain secure and accessible to the right people at the right time. As enterprise AI systems become … Continue reading
Data Management and Architecture Trends for 2025
Today, many organizational leaders are focused on AI readiness, and as the AI transformation is accelerating, so are the trends that define how businesses look for, store, secure, and leverage data and content. The future of enterprise data management and … Continue reading
A Guide to Selecting the Right Auto-Tagging Approach
Auto-tagging processes automate the manual labor of applying relevant keyword tags to data and content, enhancing accessibility and improving the organization of large datasets. Whether you’re trying to improve how quickly you find data or embarking on a content cleanup … Continue reading
Why Graph Implementations Fail (Early Signs & Successes)
Organizations continue to invest heavily in efforts to unify institutional knowledge and data from multiple sources. This typically involves copying data between systems or consolidating it into a new physical location such as data lakes, warehouses, and data marts. With … Continue reading
Enhancing Retail Performance with Semantic Layer As an Enabler for Data and Analytics Teams
Enterprise Knowledge is working with a multinational bank to enable their risk-assessing processes by using semantics and connected data. Heavily regulated financial services firms require comprehensive and complex risk management. This requires employees to thoroughly account for risk and report it in detail to regulators. Continue reading
Improve Enterprise AI with Semantic Content Management
In my article, How to Prepare Content for AI, I introduced some basic steps to help you prime your organization’s content for an effective AI implementation: two of those steps – structuring and componentizing – are often misunderstood or skipped … Continue reading
Hybrid Approaches to Green Information Management: A Case Study
Today, enterprises have more tools than ever for creating and sharing information, which leads to significant challenges in managing duplicate content. Enterprise Knowledge’s Urmi Majumder, Principal Consultant, and Nina Spoelker, Consultant, presented “Hybrid Approaches to Green Information Management: A Case … Continue reading
Semantic Layer Symposium – Norbert Gergely
Earlier this year, the Semantic Layer Symposium in Munich brought together industry leaders, data scientists, and Knowledge Management (KM) experts to explore cutting-edge developments in data integration and AI-driven insights. This groundbreaking event fostered lively discussions on the future of … Continue reading
A Semantic Layer to Enable Risk Management at a Multinational Bank
Enterprise Knowledge is working with a multinational bank to enable their risk-assessing processes by using semantics and connected data. Heavily regulated financial services firms require comprehensive and complex risk management. This requires employees to thoroughly account for risk and report it in detail to regulators. Continue reading