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


Enterprise machine learning deployments are limited by two consequences of outdated data management practices widely used today. The first is the protracted time-to-insight that stems from antiquated data replication approaches. The second is the lack of unified, contextualized data that spans the organization horizontally. Excessive data replication and the resulting "second-order effects" are creating enormous efficiencies and waste for data scientists in most organizations. According to IDC, over 60 zettabytes of data were produced last year, and this is forecast to increase at a CAGR of 23 percent until 2025.