Why Semantic Knowledge Graphs are the only way to build an Enterprise Data Fabric

Boris Shalumov
3 min readSep 27, 2022

Enterprise IT landscapes are full of data warehouses, data lakes, lake houses and whichever permutation of buzzwords will be the next one to try to conquer the hearts of enterprise architects. However, non of these technologies can currently solve the rising problems of enterprise wide data management and cope with its complexity, volumes and dynamics.

The objective of Entreprise Data Management (EDM) systems is to capture knowledge from all over the company and make it accessible to business users to derive insights.

Semantic Knowledge Graphs outperform any other existing technology in both the ability to capture complex knowledge as well as machine readability of the semantics associated with it to support human users in interpreting the huge volumes of data (s. graphic below). In other words, Knowledge Graphs can digest any data format (structured, semi-structured or unstructured) and tie it into a canonical business knoweldge model that is readable by humans as well as machines.

Comparison of pupolar Knoweldge Storage Technologies in terms of their suitability for enterprise data management.

No doubt — there are other criteria relevant to the selection process — like the use of standards, performance, ease of use, software maturity, access to talent and more. But these change over time and Semantic Knowledge Graphs already outperform other technologies in some of these areas, e.g. the use of standards (W3C) and performance (no expensive JOINs) — due to their inherent nature of dealing with complex data structures.

Despite other technologies being very well suited for narrow solutions and specific use cases, the only way to build a sustainable, robust and performant data fabric is using the technology that was built to meet exactly these requirements.

Featured: Chaos Orchestra — The Knowledge Graph Podcast

In just a few years Knowledge Graphs have exploded in usage, as has their impact in the world of Artificial Intelligence. Semantic AI has become a significant part of text analytics, search engines, chat-bots and more. And yet, few people outside of niche tech communities are fully aware of how semantic knowledge graphs can be leveraged.

In the Podcast “Chaos Orchestra” we are exploring how Knowledge Graphs can be applied over the next decade to boost many areas of Artificial Intelligence and address the most pressing challenges of our times.

Chaos Orchestra — The Knowledge Graph Podcast

The topics and inspiring guests:

1) The RobotCEO with Dan McCreary
2) Natural Language Understanding with Walid Saba
3) Knowledge-Infused ML with Manas Gaur
4) Science Knowledge Graph with Sören Auer
5) Ontology Engineering with Panos Alexopoulos
6) Abstract Wikipedia with Denny Vrandečić
7) Knowledge Graphs vs. Fake News with Daniel Schwabe
8) Graph Learning with Giuseppe Futia, PhD
9) The Future of Knowledge Graphs with Jans Aasman
10) Data Management of the Future with Sean Martin

YouTube: https://lnkd.in/dtpuU4Z
Spotify: https://lnkd.in/dH4APQB
Google Podcasts: https://lnkd.in/dh_B5be
Apple: https://lnkd.in/dzSDvBq



Boris Shalumov

I believe Knowledge Graphs are the path to collective intelligence, a common language of humans and machines and the basis for Artificial General Intelligence