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ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing datawarehouses. We must resort to black box testing —testing the behavior of the function with a wide range of inputs.
Then the data is consumed by SaaS-based computational tools, but it still sits within our organization and sits within the controls of our cloud-based solutions.” Much of Regeneron’s data, of course, is confidential. For that reason, many of its data tools — and even its datalake — were built in-house using AWS. “We
How effectively and efficiently an organization can conduct data analytics is determined by its data strategy and data architecture , which allows an organization, its users and its applications to access different types of data regardless of where that data resides.
For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. A business glossary to explain the business terms used within a data asset.
It improves IT and business data literacy and knowledge, supporting enterprise data governance and business enablement. It helps solve the lack of visibility and control over “data at rest” in databases, datalakes and datawarehouses and “data in motion” as it is integrated with and used by key applications.
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