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Selecting the strategies and tools for validating datatransformations and data conversions in your data pipelines. Introduction Datatransformations and data conversions are crucial to ensure that raw data is organized, processed, and ready for useful analysis.
Today we will share our approach to developing a data governance program to drive datatransformation and fuel a data-driven culture. Data governance is a crucial aspect of managing an organization’s data assets.
This initial phase focuses on understanding the business value-add from a business perspective, then translating this knowledge into a data mining problem definition. This may also involve the generation of a preliminary plan designed to deliver the businessobjectives. What are we trying to achieve?
This is especially beneficial when teams need to increase data product velocity with trust and dataquality, reduce communication costs, and help data solutions align with businessobjectives. In most enterprises, data is needed and produced by many business units but owned and trusted by no one.
However, you might face significant challenges when planning for a large-scale data warehouse migration. Additionally, organizations must carefully consider factors such as cost implications, security and compliance requirements, change management processes, and the potential disruption to existing business operations during the migration.
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