Data scientists today face a perfect storm: an explosion of inconsistent, unstructured, multimodal data scattered across silos – and mounting pressure to turn it into accessible, AI-ready insights.

Without the right processes and tools, it’s easy for a digital analyst to spend more time pulling and organizing data than reporting their findings and delivering meaningful analyses. What can ...

Organizations face obstacles in building effective unified environments, because these environments are difficult to deploy, and hard to manage with new types of data and multiple systems. Providing ...

In an era where data is a strategic asset, organizations often falter not because they lack data—but because their architecture doesn’t scale with their needs. Leaders must design data ecosystems that ...

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“You can’t handle the truth.” We all remember Jack Nicholson’s iconic words in A Few Good Men as a seminal moment in pop culture. Yet, that statement has a lot of relevance for companies and their ...

A technical paper titled “Darwin: A DRAM-based Multi-level Processing-in-Memory Architecture for Data Analytics” was published by researchers at Korea Advanced Institute of Science & Technology (KAIST ...

If you work for a large financial firm, it’s safe to assume that your data architecture is probably what UBS Group CTO Rick Carey calls a “big red blob.” Like many technologists whose job it is to ...

If the decision is made only to, for example, send certain kinds of analytical results to the cloud, how will heavy cloud-based analytics perform? Another thing to consider is that cloud storage may ...

Artificial intelligence (AI) brings with it new requirements, paradigms and interactions that expand or challenge traditional approaches to software architecture in many areas. For software architects ...