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Data integrity is based on four main pillars: Data integration : Regardless of its original source, on legacy systems, relational databases, or clouddata warehouses, data must be seamlessly integrated in order to gain visibility into all your data in a timely fashion.
Whatever your unique objectives may be, the Data Integrity Suite’s Data Quality module will play a critical role in your ongoing data integrity journey – ready to help you tackle new use cases with data that’s accurate, consistent, and fit for purpose where you need it most.
It’s critical that business analysts have the data they need and that IT has the appropriate metadata associated with those datasets for seamless replication into the cloud. That’s why a data catalog is critical to any organization – particularly if you run analysis and reports in clouddata platforms.
Making the experts responsible for service streamlines the data-request pipeline, delivering higher quality data into the hands of those who need it more rapidly. Some argue that datagovernance and quality practices may vary between domains. Interoperable and governed by global standards. Self-describing.
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