Improving Machine Learning: How Knowledge Graphs Bring Deeper Meaning to Data

Dec 5, 2021

Enterprise machine learning deployments are limited by two consequences of outdated data management practices widely used today:

  1. The protracted time-to-insight that stems from antiquated data replication approaches
  2. The lack of unified, contextualized data that spans the organization horizontally

Properly implementing knowledge graphs in a modern data fabric corrects these data management issues while increasing machine learning’s value. Deploying data virtualization within a knowledge graph empowered data fabric enables data scientists to bring machine learning to their data—instead of the opposite, which wastes time and resources.

Moreover, the inherent flexibility of graph models and their ability to leverage inter-connected relationships make preparing data for machine learning much easier as they provide capabilities like improved feature engineering, root cause analysis, and graph analytics. This functionality is also key to helping knowledge graphs transition to be the dominant data management construct for the next 20 years as data management and AI converge. In short, knowledge graphs will help AI as much as AI will help knowledge graphs.

Read Kendall Clark’s entire blog at Data Science Central.