As predicted, 2018 was also the year of the graph. Many new graph products were launched. Enterprises became conversant with graph products and were comfortable when deploying solutions based on knowledge graph technologies. So, what is in store for knowledge graph technologies in 2019? What key themes and trends will emerge across the industry? In this article, Sean Martin, CTO of Cambridge Semantics, offers his predictions that data and knowledge management professionals should be aware of in this New Year.
Sean predicts that the importance of data fabrics, the impact of graph databases on machine learning and the possibilities of multiple and hybrid clouds will have a significant impact in 2019. Let us examine the first two, as they are more relevant for academic publishing and KM professionals.
The majority of data generated are in both structured and unstructured formats. In all likelihood, we will witness enterprises, and data scientists unite disparate data through a unifying data fabric overlay, built on graph technologies in 2019. Data-intensive industries — especially oil and gas, financial services, life sciences, and government entities— can benefit the most from this new approach of democratization of data.
In 2019, we might see enterprises deploying graph-based data discovery tools that are capable of driving feature engineering for training useful machine learning models. New graph-based tools for data discovery, harmonization and prep are removing the data-related roadblocks to machine learning initiatives. In addition, graph-based data discovery tools can do most of the manual work that data scientists need to do more quickly. Furthermore, graphs provide the means to train machine-learning algorithms against broader accumulations of data. This will ensure that these intelligent algorithms find wider application, faster.
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