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Data science is largely an enigma to the enterprise. Although there’s an array of self-service options to automate its various processes, the actual work performed by data scientists (and how it’s achieved) is still a mystery to your average business user or C-level executive.
Data modeling is the foundation of this discipline that’s responsible for the adaptive, predictive analytics that are so critical to the current data ecosystem. Before data scientists can refine cognitive computing models or build applications with them to solve specific business problems, they must rectify differences in data models to leverage different types of data for a single use case.
Since statistical Artificial Intelligence deployments like machine learning intrinsically require huge data quantities from diverse sources for optimum results, simply getting such heterogeneous data to conform to a homogenous data model has been one of the most time-honored—and time consuming—tasks in data science.
But not anymore.
Contemporary developments in data modeling are responsible for the automation of this crucial aspect of data science. By leveraging a combination of technologies revolving around cloud computing, knowledge graphs, machine learning, and Natural Language Processing (NLP), organizations can automatically map the most variegated data to …
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