Content related to The Journey of Data: From Raw Numbers to Actionable Insights with LLMs
Women’s Health Foundation – Semantic Classification POC
A humanitarian foundation focusing on women’s health faced a complex problem: determining the highest impact decision points in contraception adoption for specific markets and demographics. Two strategic objectives drove the initiative—first, understanding the multifaceted factors (from product attributes to social influences) that guide women’s contraceptive choices, and second, identifying actionable insights from disparate data sources. The key challenge was integrating internal survey response data with internal investment documents to answer nuanced competency questions such as, “What are the most frequently cited factors when considering a contraceptive method?” and “Which factors most strongly influence adoption or rejection?” This required a system that could not only ingest and organize heterogeneous data but also enable executives to visualize and act upon insights derived from complex cross-document analyses. Continue reading
Humanitarian Foundation – SemanticRAG POC
A humanitarian foundation needed to demonstrate the ability of its Graph Retrieval Augmented Generation (GRAG) system to answer complex, cross-source questions. In particular, the task was to evaluate the impact of foundation investments on strategic goals by synthesizing information from publicly available domain data, internal investment documents, and internal investment data. The challenge laid in …. Continue reading
Unlocking Knowledge Intelligence from Unstructured Data
Introduction Organizations generate, source, and consume vast amounts of unstructured data every day, including emails, reports, research documents, technical documentation, marketing materials, learning content and customer interactions. However, this wealth of information often remains hidden and siloed, making it challenging … Continue reading
Enterprise AI Architecture Series: How to Inject Business Context into Structured Data using a Semantic Layer (Part 3)
Introduction AI has attracted significant attention in recent years, prompting me to explore enterprise AI architectures through a multi-part blog series this year. Part 1 of this series introduced the key technical components required for implementing an enterprise AI architecture. … Continue reading
Leveraging Institutional Knowledge to Improve AI Success
In an age where organizations are seeking competitive advantages from new technologies, having high-quality knowledge readily available for use by both humans and AI solutions is an imperative. Organizations are making large investments in deploying AI. However, many are … Continue reading
Understanding the Role of Knowledge Intelligence in the CRISP-DM Framework: A Guide for Data Science Projects
In today’s rapidly advancing field of data science, where new technologies and methods continuously emerge, it’s essential to have a structured approach to navigate the complexities of data mining and analysis. The CRISP-DM framework–short for Cross-Industry Standard Process for Data … Continue reading
AI & Taxonomy: the Good and the Bad
The recent popularity of new machine learning (ML) and artificial intelligence (AI) applications has disrupted a great deal of traditional data and knowledge management understanding and tooling. At EK, we have worked with a number of clients who have questions–how … Continue reading
From Enterprise GenAI to Knowledge Intelligence: How to Take LLMs from Child’s Play to the Enterprise
In today’s world, it would almost be an understatement to say that every organization wants to utilize generative AI (GenAI) in some part of their business processes. However, key decision-makers are often unclear on what these technologies can do for … Continue reading
Aligning an Enterprise-Wide Information Management (IM) Roadmap for a Global Energy Company
A global energy company sought support in detailing and aligning their information management (IM) team’s roadmaps for all four of their IM products – covering all managed applications, services, projects, and capabilities – to help them reach their target state vision of higher levels of productivity, more informed decision-making, and quality information made available to … Continue reading
Data Governance for Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) has emerged as a powerful approach for injecting organizational knowledge into enterprise AI systems. By combining the capabilities of large language models (LLMs) with access to relevant, up-to-date organizational information, RAG enables AI solutions to deliver context-aware, accurate, … Continue reading