Top Semantic Layer Use Cases and Applications (with Real World Case Studies)
Today, most enterprises are managing multiple content and data systems or repositories, often with overlapping capabilities such as content authoring, document management, or data management (typically averaging three or more). This leads to fragmentation and data silos, creating significant inefficiencies. … Continue reading
What are the Different Types of Graphs? The Most Common Misconceptions and Understanding Their Applications
Over 80% of enterprise data remains unstructured, and with the rise of artificial intelligence (AI), traditional relational databases are becoming less effective at capturing the richness of organizational knowledge assets, institutional knowledge, and interconnected data. In modern enterprise data solutions, … 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
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
How to Inject Organizational Knowledge in AI: 3 Proven Strategies to Achieve Knowledge Intelligence
Generative AI (GenAI) has made Artificial Intelligence (AI) more accessible to the business, specifically by empowering organizations to leverage large language models (LLMs) for a wide range of applications. From enhancing customer support to automating content creation and operational processes, … Continue reading
The Top 3 Ways to Implement a Semantic Layer
Over the last decade, we have seen some of the most exciting innovations emerge within the enterprise knowledge and data management spaces. Those innovations with real staying power have proven to drive business outcomes and prioritize intuitive user engagement. Within … Continue reading
What is a Semantic Layer? (Components and Enterprise Applications)
Over the last decade, many organizations went through expensive migrations – either moving data into a data lake, a data warehouse, a modern data stack, or to the cloud. Yet, the business problems that many are looking to solve through … Continue reading
Five Ways to Demonstrate the Value of KM [In the Age of AI]
Knowledge management (KM) is steadily growing beyond its traditional role of providing the framework for sharing, using, and managing the knowledge and information of an organization: it now also serves as the foundation for advancements in Artificial Intelligence (AI). We … Continue reading
Top Graph Use Cases and Enterprise Applications (with Real World Examples)
Graph solutions have gained momentum due to their wide-ranging applications across multiple industries. Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Several factors are driving the … Continue reading
The Five Priorities for Every Data Strategy Today
Earlier this year, I shared the top trends we are seeing when it comes to solving enterprise data management problems. We continue to deploy and put these emerging solutions to the test (read more on that from our case studies) … Continue reading