Data-driven use cases

Data is the core element for applications serving diverse use cases from traditional analytics and machine learning (ML)/artificial intelligence (AI) to data-intensive applications that combine both analytical and transactional workloads. All these applications generate data, and almost all enterprises today rely on multiple applications to support their business processes and fight with data accumulated in silos from different applications in their information technology (IT) landscapes. Furthermore, modern applications use a much larger number of data stores—such as objects, documents, and graphs—compared to legacy applications mainly leveraging a relational store. A modern data architecture supported by various data integration and processing technologies will need to address these challenges and hide the aforementioned underlying complexity.

Let’s have a look at a concrete example based on an Intelligent Enterprise scenario to see...