There is a dichotomy between how data scientists would like to spend their time and how actually they spend their time. This is prevalent because data science teams are spending a majority of their time on foundational tasks, such as preparing data for analysis. According to Connor Vilenio, a senior analyst with Enterprise Knowledge, this problem is fundamentally a knowledge and information management challenge and can be overcome by using a data knowledge graph.
A data knowledge graph can be used to create a model to associate a dataset with all of its crucial contexts. The unique variables it records, the subject matter it describes, past versions and derivations, as well as its most common users. This unlocks many potential avenues that data scientists can use to find relevant and related datasets quickly and effectively. Data scientists can leverage data knowledge graph to unlock a myriad of other benefits such as identifying datasets, tracking the latest versions and managing data security.
The most impactful use case of a data knowledge graph is improved dataset search. Enterprises have ineffective search capabilities because large data repositories lack useful metadata that captures what the datasets are about. Metadata allows the data knowledge graph to model the connections in the metadata. This enhances the ability to identify the numerous relationships between datasets that data scientists can capitalize on to develop their analysis.
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